Steganography is a โscienceโ, the method of hiding sent information. Unlike cryptography that deals with coding of information, the main idea of steganography is hiding the fact that the message exists. It embeds the secret message in cover media (image, audio, video, text, etc.). During the last years with the development of digital image processing, methods of digital steganography have gained a lot of popularity. The most popular steganography method is LSB (Last Significant Bit) replacement in the cover image. With extensive evolution of steganography, Steganalysis methods have a lot of importance. Steganalysis algorithms role is to detect a hidden secret message inside any media. The most notable Steganalysis algorithm is the RS method [1], which detects stegamesage by the statistical analysis applied on image pixels.
Shen Wang and others [2] created a new algorithm based on Genetic Shifting method (GSM). GSM performs manipulation and modification of the original image pixels. GSM algorithm keeps image statistic after inserting a hidden message and is hard to be detected by the RS analysis. The goal of the project is to demonstrate effectiveness and stability of GSM algorithm against RS analysis by using mathematical and statistical methods.
In this paper, a new steganography algorithm has been suggested to enforce the security of data hiding and to increase the amount of payloads. This algorithm is based on four safety layers; the first safety layer has been initiated through compression and an encryption of a confidential message using a set partition in hierarchical trees (SPIHT) and advanced encryption standard (AES) mechanisms respectively. An irregular image segmentation algorithm (IIS) on a cover-image (Ic) has been constructed successfully in
the second safety layer, and it is based on the adaptive reallocation segments' edges (ARSE) by applying an
adaptive finite-element method (AFEM) to find the numerical solution of the proposed partial differential equation (PDE). An intelligent computing technique using a hybrid adaptive neural network with a modified ant colony optimizer (ANN_MACO) has been proposed in the third safety layer to construct a
learning system. This system accepts entry using support vector machine (SVM) to generate input patterns as features of byte attributes and produces new features to modify a cover-image. The significant innovation of the proposed novel steganography algorithm is applied efficiently on the forth
safety layer which is more robust for hiding a large amount of confidential message reach to six bits per pixel (bpp) into color images. The new approach of hiding algorithm works against statistical and visual attacks with high imperceptible of hiding data into stego-images (Is). The experimental results are
discussed and compared with the previous steganography algorithms; it demonstrates that the proposed algorithm has a significant improvement on the effect of the security level of steganography by making an arduous task of retrieving embedded confidential message from color images.
Adaptive block-based pixel value differencing steganographyOsama Hosam
ย
Steganography is the science of hiding secure data in digital carriers such as images and videos. Pixel value differencing
(PVD) steganography algorithms embed data into images depending on pixel neighborhood differences. We have pro-
posed PVD scheme for embedding secure data into digital images. The image is divided into non-overlapping 33 blocks.
The blockโs median pixel is used as a reference for calculating pixel differences. The distance between the minimum and
maximum differences are fine tuned for spreading the secure data on a wide range of image regions with high-intensity
fluctuations. The embedding procedure embeds secure data into the content regions with edges and intensity transitions.
Texture images provide higher embedding size compared with regular images. The results showed that the proposed
algorithm is successfully able to avoid smooth regions in the embedding process. In addition, the proposed algorithm
shows better embedding quality compared with the state of the art PVD approaches especially with low-embedding rates.
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-LearningMLAI2
ย
Unsupervised learning aims to learn meaningful representations from unlabeled data which can captures its intrinsic structure, that can be transferred to downstream tasks. Meta-learning, whose objective is to learn to generalize across tasks such that the learned model can rapidly adapt to a novel task, shares the spirit of unsupervised learning in that the both seek to learn more effective and efficient learning procedure than learning from scratch. The fundamental difference of the two is that the most meta-learning approaches are supervised, assuming full access to the labels. However, acquiring labeled dataset for meta-training not only is costly as it requires human efforts in labeling but also limits its applications to pre-defined task distributions. In this paper, we propose a principled unsupervised meta-learning model, namely Meta-GMVAE, based on Variational Autoencoder (VAE) and set-level variational inference. Moreover, we introduce a mixture of Gaussian (GMM) prior, assuming that each modality represents each class-concept in a randomly sampled episode, which we optimize with Expectation-Maximization (EM). Then, the learned model can be used for downstream few-shot classification tasks, where we obtain task-specific parameters by performing semi-supervised EM on the latent representations of the support and query set, and predict labels of the query set by computing aggregated posteriors. We validate our model on Omniglot and Mini-ImageNet datasets by evaluating its performance on downstream few-shot classification tasks. The results show that our model obtain impressive performance gains over existing unsupervised meta-learning baselines, even outperforming supervised MAML on a certain setting.
A Secure & Optimized Data Hiding Technique Using DWT With PSNR ValueIJERA Editor
ย
Multimedia applications are becoming increasingly significant in modern world. The mushroom growth of multimedia data of these applications, particularly over the web has increased the demand for protection of copyright. Digital watermarking is much more acceptable as a solution to the problem of copyright protection and authentication of multimedia data while working in a networked environment. In this paper, a DWT based watermarking scheme is proposed. We have used Genetic Algorithm (GA) in order to make an optimum tradeoff between imperceptibility and robustness by choosing an optimum watermarking level for each coefficient of the cover image. In addition to the suitable watermarking strength, the selection of best block size is also necessary for superior perceptual shaping functions. To achieve this goal we have trained and used GA to pick the best block size to tailor the watermark in one of the coefficients of the DWT. The fitness function criterion for the genetic algorithm decision making is based on PSNR values
INTRA BLOCK AND INTER BLOCK NEIGHBORING JOINT DENSITY BASED APPROACH FOR JPEG...ijsc
ย
Steganalysis is the method used to detect the presence of any hidden message in a cover medium. A novel
approach based on feature mining on the discrete cosine transform (DCT) domain based approach,
machine learning for steganalysis of JPEG images is proposed. The neighboring joint density on both
intra-block and inter-block are extracted from the DCT coefficient array. After the feature space has been
constructed, it uses SVM like binary classifier for training and classification. The performance of the
proposed method on different Steganographic systems named F5, Pixel Value Differencing, Model Based
Steganography with and without deblocking, JPHS, Steghide etc are analyzed. Individually each feature
and combined features classification accuracy is checked and concludes which provides better
classification.
In this paper, a new steganography algorithm has been suggested to enforce the security of data hiding and to increase the amount of payloads. This algorithm is based on four safety layers; the first safety layer has been initiated through compression and an encryption of a confidential message using a set partition in hierarchical trees (SPIHT) and advanced encryption standard (AES) mechanisms respectively. An irregular image segmentation algorithm (IIS) on a cover-image (Ic) has been constructed successfully in
the second safety layer, and it is based on the adaptive reallocation segments' edges (ARSE) by applying an
adaptive finite-element method (AFEM) to find the numerical solution of the proposed partial differential equation (PDE). An intelligent computing technique using a hybrid adaptive neural network with a modified ant colony optimizer (ANN_MACO) has been proposed in the third safety layer to construct a
learning system. This system accepts entry using support vector machine (SVM) to generate input patterns as features of byte attributes and produces new features to modify a cover-image. The significant innovation of the proposed novel steganography algorithm is applied efficiently on the forth
safety layer which is more robust for hiding a large amount of confidential message reach to six bits per pixel (bpp) into color images. The new approach of hiding algorithm works against statistical and visual attacks with high imperceptible of hiding data into stego-images (Is). The experimental results are
discussed and compared with the previous steganography algorithms; it demonstrates that the proposed algorithm has a significant improvement on the effect of the security level of steganography by making an arduous task of retrieving embedded confidential message from color images.
Adaptive block-based pixel value differencing steganographyOsama Hosam
ย
Steganography is the science of hiding secure data in digital carriers such as images and videos. Pixel value differencing
(PVD) steganography algorithms embed data into images depending on pixel neighborhood differences. We have pro-
posed PVD scheme for embedding secure data into digital images. The image is divided into non-overlapping 33 blocks.
The blockโs median pixel is used as a reference for calculating pixel differences. The distance between the minimum and
maximum differences are fine tuned for spreading the secure data on a wide range of image regions with high-intensity
fluctuations. The embedding procedure embeds secure data into the content regions with edges and intensity transitions.
Texture images provide higher embedding size compared with regular images. The results showed that the proposed
algorithm is successfully able to avoid smooth regions in the embedding process. In addition, the proposed algorithm
shows better embedding quality compared with the state of the art PVD approaches especially with low-embedding rates.
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-LearningMLAI2
ย
Unsupervised learning aims to learn meaningful representations from unlabeled data which can captures its intrinsic structure, that can be transferred to downstream tasks. Meta-learning, whose objective is to learn to generalize across tasks such that the learned model can rapidly adapt to a novel task, shares the spirit of unsupervised learning in that the both seek to learn more effective and efficient learning procedure than learning from scratch. The fundamental difference of the two is that the most meta-learning approaches are supervised, assuming full access to the labels. However, acquiring labeled dataset for meta-training not only is costly as it requires human efforts in labeling but also limits its applications to pre-defined task distributions. In this paper, we propose a principled unsupervised meta-learning model, namely Meta-GMVAE, based on Variational Autoencoder (VAE) and set-level variational inference. Moreover, we introduce a mixture of Gaussian (GMM) prior, assuming that each modality represents each class-concept in a randomly sampled episode, which we optimize with Expectation-Maximization (EM). Then, the learned model can be used for downstream few-shot classification tasks, where we obtain task-specific parameters by performing semi-supervised EM on the latent representations of the support and query set, and predict labels of the query set by computing aggregated posteriors. We validate our model on Omniglot and Mini-ImageNet datasets by evaluating its performance on downstream few-shot classification tasks. The results show that our model obtain impressive performance gains over existing unsupervised meta-learning baselines, even outperforming supervised MAML on a certain setting.
A Secure & Optimized Data Hiding Technique Using DWT With PSNR ValueIJERA Editor
ย
Multimedia applications are becoming increasingly significant in modern world. The mushroom growth of multimedia data of these applications, particularly over the web has increased the demand for protection of copyright. Digital watermarking is much more acceptable as a solution to the problem of copyright protection and authentication of multimedia data while working in a networked environment. In this paper, a DWT based watermarking scheme is proposed. We have used Genetic Algorithm (GA) in order to make an optimum tradeoff between imperceptibility and robustness by choosing an optimum watermarking level for each coefficient of the cover image. In addition to the suitable watermarking strength, the selection of best block size is also necessary for superior perceptual shaping functions. To achieve this goal we have trained and used GA to pick the best block size to tailor the watermark in one of the coefficients of the DWT. The fitness function criterion for the genetic algorithm decision making is based on PSNR values
INTRA BLOCK AND INTER BLOCK NEIGHBORING JOINT DENSITY BASED APPROACH FOR JPEG...ijsc
ย
Steganalysis is the method used to detect the presence of any hidden message in a cover medium. A novel
approach based on feature mining on the discrete cosine transform (DCT) domain based approach,
machine learning for steganalysis of JPEG images is proposed. The neighboring joint density on both
intra-block and inter-block are extracted from the DCT coefficient array. After the feature space has been
constructed, it uses SVM like binary classifier for training and classification. The performance of the
proposed method on different Steganographic systems named F5, Pixel Value Differencing, Model Based
Steganography with and without deblocking, JPHS, Steghide etc are analyzed. Individually each feature
and combined features classification accuracy is checked and concludes which provides better
classification.
An optimized framework for detection and tracking of video objects in challen...ijma
ย
Segmentation and tracking are two important aspects in visual surveillance systems. Many barriers such as
cluttered background, camera movements, and occlusion make the robust detection and tracking a difficult
problem, especially in case of multiple moving objects. Object detection in the presence of camera noise
and with variable or unfavourable luminance conditions is still an active area of research. This paper
propose a framework which can effectively detect the moving objects and track them despite of occlusion
and a priori knowledge of objects in the scene. The segmentation step uses a robust threshold decision
algorithm which uses a multi-background model. The video object tracking is able to track multiple objects
along with their trajectories based on Continuous Energy Minimization. In this work, an effective
formulation of multi-target tracking as minimization of a continuous energy is combined with multibackground
registration. Apart from the recent approaches, it focus on making use of an energy that
corresponds to a more complete representation of the problem, rather than one that is amenable to global
optimization. Besides the image evidence, the energy function considers physical constraints, such as target
dynamics, mutual exclusion, and track persistence. The proposed tracking framework is able to track
multiple objects despite of occlusions under dynamic background conditions.
Comparative Performance of Image Scrambling in Transform Domain using Sinusoi...CSCJournals
ย
With the rapid development of technology, and the popularization of internet, communication is been greatly promoted. The communication is not limited only to information but also includes multimedia information like digital Images. Therefore, the security of digital images has become a very important and practical issue, and appropriate security technology is used for those digital images containing confidential or private information especially. In this paper a novel approach of Image scrambling has been proposed which includes both spatial as well as Transform domain. Experimental results prove that correlation obtained in scrambled images is much lesser then the one obtained in transformed images.
Black-box modeling of nonlinear system using evolutionary neural NARX modelIJECEIAES
ย
Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system.
SAR Image Classification by Multilayer Back Propagation Neural NetworkIJMTST Journal
ย
A novel descriptive feature extraction method of Discrete Fourier transform and neural network classifier for classification of Synthetic Aperture Radar (SAR) images is proposed. The classification process has the following stages (1) Image Segmentation using statistical Region Merging (SRM) (2) Polar transform and Feature extraction using Discrete Fourier Transform (3) Neural Network classification using back propagation. This is generally the first step in image analysis. Segmentation subdivides an image into its constituent parts or objects. The level to which this subdivision is carried depends on the problem being solved. The image segmentation in this study is performed using Statistical Region Merging proposed Richard Nock and Frank Nielsen. The key idea of the Statistical Region Merging model is to formulate image segmentation as an inference problem. Here the merging procedure is based on the theorem. Feature vectors as the input for the neural network. Polar transform is applied to segmented SAR image. The rotation problem under the Cartesian coordinates becomes the translation problem under the polar coordinates.
A new partial image encryption method for document images using variance base...IJECEIAES
ย
The proposed method partially and completely encrypts the gray scale Document images. The complete image encryption is also performed to compare the performance with the existing encryption methods. The partial encryption is carried out by segmenting the image using the Quad-tree decomposition method based on the variance of the image block. The image blocks with uniform pixel levels are considered insignificant blocks and others the significant blocks. The pixels in the significant blocks are permuted by using 1D Skew tent chaotic map. The partially encrypted image blocks are further permuted using 2D Henon map to increase the security level and fed as input to complete encryption. The complete encryption is carried out by diffusing the partially encrypted image. Two levels of diffusion are performed. The first level simply modifies the pixels in the partially encrypted image with the Bernoulliโs chaotic map. The second level establishes the interdependency between rows and columns of the first level diffused image. The experiment is conducted for both partial and complete image encryption on the Document images. The proposed scheme yields better results for both partial and complete encryption on Speed, statistical and dynamical attacks. The results ensure better security when compared to existing encryption schemes.
Extended of TEA: A 256 bits block cipher algorithm for image encryption IJECEIAES
ย
This paper introduces an effective image encryption approach that merges a chaotic map and polynomial with a block cipher. According to this scheme, there are three levels of encryption. In the first level, pixel positions of the image are scuffled into blocks randomly based on a chaotic map. In the second level, the polynomials are constructed by taking N unused pixels from the permuted blocks as polynomial coefficients. Finally, the third level a proposed secret-key block cipher called extended of tiny encryption algorithm (ETEA) is used. The proposed ETEA algorithm increased the block size from 64-bit to 256-bit by using F-function in type three Feistel network design. The key schedule generation is very straightforward through admixture the entire major subjects in the identical manner for every round. The proposed ETEA algorithm is word-oriented, where wholly internal operations are executed on words of 32 bits. So, it is possible to efficiently implement the proposed algorithm on smart cards. The results of the experimental demonstration that the proposed encryption algorithm for all methods are efficient and have high security features through statistical analysis using histograms, correlation, entropy, randomness tests, and the avalanche effect.
A REVIEW ON BLIND STILL IMAGE STEGANALYSIS TECHNIQUES USING FEATURES EXTRACTI...IJCSEIT Journal
ย
Steganography is the technique for hiding secret information in other data such as still, multimedia
images, text, audio. Whereas Steganalysis is the reverse technique in which detection of the secret
information is done in the stego image. Steganalysis can be classified on the basis of the techniques used
classified statistical techniques, pattern classification techniques and visual detection techniques .All the
existing techniques can be broadly classified on the basis of the information required for the designing of
the steganalysis. They are targeted and blind steganalysis In targeted technique, we first look at
steganalysis techniques is designed for a particular steganographic embedding algorithm in mind whereas
in blind steganalysis is general class of steganalysis techniques which can be implemented with any
steganographic embedding algorithm, even an unknown algorithm. In this paper, an extensive review
report is presented chronologically on the Blind Image Steganalysis for the still stego images using the
classification techniques.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A B. TECH PROJECT REPORT on โSTEGANOGRAPHYโ Submitted in partial ... STEGANOGRAPHY Dept. of Information Technology 4.1.2 Image Steganography Hiding t
Imaging and Image sensors is a field that is continuously evolving. There are new products
coming into the market every day. Some of these have very severe Size, Weight and Power
constraints whereas other devices have to handle very high computational loads. Some require
both these conditions to be met simultaneously. Current imaging architectures and digital image
processing solutions will not be able to meet these ever increasing demands. There is a need to
develop novel imaging architectures and image processing solutions to address these
requirements. In this work we propose analog signal processing as a solution to this problem.
The analog processor is not suggested as a replacement to a digital processor but it will be used
as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing the highly
computational Normalized Cross Correlation algorithm is implemented. We propose two novel
modifications to the algorithm and a new imaging architecture which, significantly reduces the
computation time.
Analog signal processing approach for coarse and fine depth estimationsipij
ย
Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the
market every day. Some of these have very severe Size, Weight and Power constraints whereas other
devices have to handle very high computational loads. Some require both these conditions to be met
simultaneously. Current imaging architectures and digital image processing solutions will not be able to
meet these ever increasing demands. There is a need to develop novel imaging architectures and image
processing solutions to address these requirements. In this work we propose analog signal processing as a
solution to this problem. The analog processor is not suggested as a replacement to a digital processor but
it will be used as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing two stereo
correspondence algorithms are implemented. We propose novel modifications to the algorithms and new
imaging architectures which, significantly reduces the computation time
Analog signal processing approach for coarse and fine depth estimationsipij
ย
Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the
market every day. Some of these have very severe Size, Weight and Power constraints whereas other
devices have to handle very high computational loads. Some require both these conditions to be met
simultaneously. Current imaging architectures and digital image processing solutions will not be able to
meet these ever increasing demands. There is a need to develop novel imaging architectures and image
processing solutions to address these requirements. In this work we propose analog signal processing as a
solution to this problem. The analog processor is not suggested as a replacement to a digital processor but
it will be used as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing two stereo
correspondence algorithms are implemented. We propose novel modifications to the algorithms and new
imaging architectures which, significantly reduces the computation time
An image steganography using improved hyper-chaotic Henon map and fractal Tro...IJECEIAES
ย
Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. First, the cover image is converted into a wavelet environment using the integer wavelet transform (IWT), which protects the cover images from false mistakes. The grey wolf optimizer (GWO) is used to choose the pixelโs image that would be utilized to insert the hidden image in the cover image. GWO effectively selects pixels by calculating entropy, pixel intensity, and fitness function using the cover images. Moreover, the secret image was encrypted by utilizing a proposed hyper-chaotic improved Henon map and fractal Tromino. The suggested method increases computational security and efficiency with increased embedding capacity. Following the embedding algorithm of the secret image and the alteration of the cover image, the least significant bit (LSB) is utilized to locate the tempered region and to provide self-recovery characteristics in the digital image. According to the findings, the proposed technique provides a more secure transmission network with lower complexity in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM), entropy and mean square error (MSE). As compared to the current approaches, the proposed method performed better in terms of PSNR 70.58% Db and SSIM 0.999 respectively.
An optimized framework for detection and tracking of video objects in challen...ijma
ย
Segmentation and tracking are two important aspects in visual surveillance systems. Many barriers such as
cluttered background, camera movements, and occlusion make the robust detection and tracking a difficult
problem, especially in case of multiple moving objects. Object detection in the presence of camera noise
and with variable or unfavourable luminance conditions is still an active area of research. This paper
propose a framework which can effectively detect the moving objects and track them despite of occlusion
and a priori knowledge of objects in the scene. The segmentation step uses a robust threshold decision
algorithm which uses a multi-background model. The video object tracking is able to track multiple objects
along with their trajectories based on Continuous Energy Minimization. In this work, an effective
formulation of multi-target tracking as minimization of a continuous energy is combined with multibackground
registration. Apart from the recent approaches, it focus on making use of an energy that
corresponds to a more complete representation of the problem, rather than one that is amenable to global
optimization. Besides the image evidence, the energy function considers physical constraints, such as target
dynamics, mutual exclusion, and track persistence. The proposed tracking framework is able to track
multiple objects despite of occlusions under dynamic background conditions.
Comparative Performance of Image Scrambling in Transform Domain using Sinusoi...CSCJournals
ย
With the rapid development of technology, and the popularization of internet, communication is been greatly promoted. The communication is not limited only to information but also includes multimedia information like digital Images. Therefore, the security of digital images has become a very important and practical issue, and appropriate security technology is used for those digital images containing confidential or private information especially. In this paper a novel approach of Image scrambling has been proposed which includes both spatial as well as Transform domain. Experimental results prove that correlation obtained in scrambled images is much lesser then the one obtained in transformed images.
Black-box modeling of nonlinear system using evolutionary neural NARX modelIJECEIAES
ย
Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system.
SAR Image Classification by Multilayer Back Propagation Neural NetworkIJMTST Journal
ย
A novel descriptive feature extraction method of Discrete Fourier transform and neural network classifier for classification of Synthetic Aperture Radar (SAR) images is proposed. The classification process has the following stages (1) Image Segmentation using statistical Region Merging (SRM) (2) Polar transform and Feature extraction using Discrete Fourier Transform (3) Neural Network classification using back propagation. This is generally the first step in image analysis. Segmentation subdivides an image into its constituent parts or objects. The level to which this subdivision is carried depends on the problem being solved. The image segmentation in this study is performed using Statistical Region Merging proposed Richard Nock and Frank Nielsen. The key idea of the Statistical Region Merging model is to formulate image segmentation as an inference problem. Here the merging procedure is based on the theorem. Feature vectors as the input for the neural network. Polar transform is applied to segmented SAR image. The rotation problem under the Cartesian coordinates becomes the translation problem under the polar coordinates.
A new partial image encryption method for document images using variance base...IJECEIAES
ย
The proposed method partially and completely encrypts the gray scale Document images. The complete image encryption is also performed to compare the performance with the existing encryption methods. The partial encryption is carried out by segmenting the image using the Quad-tree decomposition method based on the variance of the image block. The image blocks with uniform pixel levels are considered insignificant blocks and others the significant blocks. The pixels in the significant blocks are permuted by using 1D Skew tent chaotic map. The partially encrypted image blocks are further permuted using 2D Henon map to increase the security level and fed as input to complete encryption. The complete encryption is carried out by diffusing the partially encrypted image. Two levels of diffusion are performed. The first level simply modifies the pixels in the partially encrypted image with the Bernoulliโs chaotic map. The second level establishes the interdependency between rows and columns of the first level diffused image. The experiment is conducted for both partial and complete image encryption on the Document images. The proposed scheme yields better results for both partial and complete encryption on Speed, statistical and dynamical attacks. The results ensure better security when compared to existing encryption schemes.
Extended of TEA: A 256 bits block cipher algorithm for image encryption IJECEIAES
ย
This paper introduces an effective image encryption approach that merges a chaotic map and polynomial with a block cipher. According to this scheme, there are three levels of encryption. In the first level, pixel positions of the image are scuffled into blocks randomly based on a chaotic map. In the second level, the polynomials are constructed by taking N unused pixels from the permuted blocks as polynomial coefficients. Finally, the third level a proposed secret-key block cipher called extended of tiny encryption algorithm (ETEA) is used. The proposed ETEA algorithm increased the block size from 64-bit to 256-bit by using F-function in type three Feistel network design. The key schedule generation is very straightforward through admixture the entire major subjects in the identical manner for every round. The proposed ETEA algorithm is word-oriented, where wholly internal operations are executed on words of 32 bits. So, it is possible to efficiently implement the proposed algorithm on smart cards. The results of the experimental demonstration that the proposed encryption algorithm for all methods are efficient and have high security features through statistical analysis using histograms, correlation, entropy, randomness tests, and the avalanche effect.
Extended of TEA: A 256 bits block cipher algorithm for image encryption
ย
Similar to Steganographic Application of improved Genetic Shifting algorithm against RS analysis - BScEE final assignment by Vadim Purinson, adviser Vladislav Kaplan MScEE
A REVIEW ON BLIND STILL IMAGE STEGANALYSIS TECHNIQUES USING FEATURES EXTRACTI...IJCSEIT Journal
ย
Steganography is the technique for hiding secret information in other data such as still, multimedia
images, text, audio. Whereas Steganalysis is the reverse technique in which detection of the secret
information is done in the stego image. Steganalysis can be classified on the basis of the techniques used
classified statistical techniques, pattern classification techniques and visual detection techniques .All the
existing techniques can be broadly classified on the basis of the information required for the designing of
the steganalysis. They are targeted and blind steganalysis In targeted technique, we first look at
steganalysis techniques is designed for a particular steganographic embedding algorithm in mind whereas
in blind steganalysis is general class of steganalysis techniques which can be implemented with any
steganographic embedding algorithm, even an unknown algorithm. In this paper, an extensive review
report is presented chronologically on the Blind Image Steganalysis for the still stego images using the
classification techniques.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A B. TECH PROJECT REPORT on โSTEGANOGRAPHYโ Submitted in partial ... STEGANOGRAPHY Dept. of Information Technology 4.1.2 Image Steganography Hiding t
Imaging and Image sensors is a field that is continuously evolving. There are new products
coming into the market every day. Some of these have very severe Size, Weight and Power
constraints whereas other devices have to handle very high computational loads. Some require
both these conditions to be met simultaneously. Current imaging architectures and digital image
processing solutions will not be able to meet these ever increasing demands. There is a need to
develop novel imaging architectures and image processing solutions to address these
requirements. In this work we propose analog signal processing as a solution to this problem.
The analog processor is not suggested as a replacement to a digital processor but it will be used
as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing the highly
computational Normalized Cross Correlation algorithm is implemented. We propose two novel
modifications to the algorithm and a new imaging architecture which, significantly reduces the
computation time.
Analog signal processing approach for coarse and fine depth estimationsipij
ย
Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the
market every day. Some of these have very severe Size, Weight and Power constraints whereas other
devices have to handle very high computational loads. Some require both these conditions to be met
simultaneously. Current imaging architectures and digital image processing solutions will not be able to
meet these ever increasing demands. There is a need to develop novel imaging architectures and image
processing solutions to address these requirements. In this work we propose analog signal processing as a
solution to this problem. The analog processor is not suggested as a replacement to a digital processor but
it will be used as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing two stereo
correspondence algorithms are implemented. We propose novel modifications to the algorithms and new
imaging architectures which, significantly reduces the computation time
Analog signal processing approach for coarse and fine depth estimationsipij
ย
Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the
market every day. Some of these have very severe Size, Weight and Power constraints whereas other
devices have to handle very high computational loads. Some require both these conditions to be met
simultaneously. Current imaging architectures and digital image processing solutions will not be able to
meet these ever increasing demands. There is a need to develop novel imaging architectures and image
processing solutions to address these requirements. In this work we propose analog signal processing as a
solution to this problem. The analog processor is not suggested as a replacement to a digital processor but
it will be used as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing two stereo
correspondence algorithms are implemented. We propose novel modifications to the algorithms and new
imaging architectures which, significantly reduces the computation time
An image steganography using improved hyper-chaotic Henon map and fractal Tro...IJECEIAES
ย
Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. First, the cover image is converted into a wavelet environment using the integer wavelet transform (IWT), which protects the cover images from false mistakes. The grey wolf optimizer (GWO) is used to choose the pixelโs image that would be utilized to insert the hidden image in the cover image. GWO effectively selects pixels by calculating entropy, pixel intensity, and fitness function using the cover images. Moreover, the secret image was encrypted by utilizing a proposed hyper-chaotic improved Henon map and fractal Tromino. The suggested method increases computational security and efficiency with increased embedding capacity. Following the embedding algorithm of the secret image and the alteration of the cover image, the least significant bit (LSB) is utilized to locate the tempered region and to provide self-recovery characteristics in the digital image. According to the findings, the proposed technique provides a more secure transmission network with lower complexity in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM), entropy and mean square error (MSE). As compared to the current approaches, the proposed method performed better in terms of PSNR 70.58% Db and SSIM 0.999 respectively.
Tissue segmentation methods using 2D histogram matching in a sequence of mr b...Vladimir Kanchev
ย
This presentation aims to present segmentation results of the suggested segmentation method of tissues in MR brain images. For that purpose we give benchmark results and additional details of implementation of our method.
A simple report on implementation of an Optical Character Recognition (ORC) as a Handwritten Digit Recognition Machine. It is basically tested on a single neural network using 3 methods: K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest Classifier (RFC) Algorithm.
IMPROVED STEGANOGRAPHIC SECURITY BY APPLYING AN IRREGULAR IMAGE SEGMENTATION ...IJNSA Journal
ย
In this paper, a new steganography algorithm has been suggested to enforce the security of data hiding and to increase the amount of payloads. This algorithm is based on four safety layers; the first safety layer has been initiated through compression and an encryption of a confidential message using a set partition in hierarchical trees (SPIHT) and advanced encryption standard (AES) mechanisms respectively. An irregular image segmentation algorithm (IIS) on a cover-image (Ic) has been constructed successfully in the second safety layer, and it is based on the adaptive reallocation segments' edges (ARSE) by applying an adaptive finite-element method (AFEM) to find the numerical solution of the proposed partial differential equation (PDE). An intelligent computing technique using a hybrid adaptive neural network with a modified ant colony optimizer (ANN_MACO) has been proposed in the third safety layer to construct a learning system. This system accepts entry using support vector machine (SVM) to generate input patterns as features of byte attributes and produces new features to modify a cover-image.
The significant innovation of the proposed novel steganography algorithm is applied efficiently on the forth safety layer which is more robust for hiding a large amount of confidential message reach to six bits per pixel (bpp) into color images. The new approach of hiding algorithm works against statistical and visual attacks with high imperceptible of hiding data into stego-images (Is). The experimental results are discussed and compared with the previous steganography algorithms; it demonstrates that the proposed algorithm has a significant improvement on the effect of the security level of steganography by making an arduous task of retrieving embedded confidential message from color images.
FPGA Based Pattern Generation and Synchonization for High Speed Structured Li...TELKOMNIKA JOURNAL
ย
Recently, structured light 3D imaging devices have gained a keen attention due to their potential
applications to robotics, industrial manufacturing and medical imaging. Most of these applications require
high 3D precision yet high speed in image capturing for hard and/or soft real time environments. This
paper presents a method of high speed image capturing for structured light 3D imaging sensors with FPGA
based structured light pattern generation and projector-camera synchronization. Suggested setup reduces
the time for pattern projection and camera triggering to 16msec from 100msec that should be required by
conventional methods.
Similar to Steganographic Application of improved Genetic Shifting algorithm against RS analysis - BScEE final assignment by Vadim Purinson, adviser Vladislav Kaplan MScEE (20)
Project presentation - Steganographic Application of improved Genetic Shifti...Vladislav Kaplan
ย
Steganography is a โscienceโ, the method of hiding sent information. Unlike cryptography that deals with coding of information, the main idea of steganography is hiding the fact that the message exists. It embeds the secret message in cover media (image, audio, video, text, etc.). During the last years with the development of digital image processing, methods of digital steganography have gained a lot of popularity. The most popular steganography method is LSB (Last Significant Bit) replacement in the cover image. With extensive evolution of steganography, Steganalysis methods have a lot of importance. Steganalysis algorithms role is to detect a hidden secret message inside any media. The most notable Steganalysis algorithm is the RS method [1], which detects stegamesage by the statistical analysis applied on image pixels.
Shen Wang and others [2] created a new algorithm based on Genetic Shifting method (GSM). GSM performs manipulation and modification of the original image pixels. GSM algorithm keeps image statistic after inserting a hidden message and is hard to be detected by the RS analysis. The goal of the project is to demonstrate effectiveness and stability of GSM algorithm against RS analysis by using mathematical and statistical methods.
LVTS - Image Resolution Monitor for Litho-MetrologyVladislav Kaplan
ย
Significant challenges for various Critical Dimension (CD) measurement matching procedures are reaching a comparable complexity as result of negative effects of roughness on the features. Due to the constant trend of integrated circuit in features reduction, impact of roughness start to be more destructive for various sets of measurement algorithms. Commonly used attempts to increase magnification for pattern recognition in measurement mode could in turn detect higher deviation from predefined patterns and thus initiate shift in placement of measurement gate. The purpose of this paper is to discuss how to reduce measurement gate (MG) placement variation impact and filter acquired data using edge correlation approach. The essence of listed above approach is to create set of width correlation function represents particular feature under test and compare it to โgoldenโ one as a mean of detection of uncorrelated scans, which in turn should be excluded from overall computation of matching results. We describe general approach for algorithm stepping and various techniques for judgment of measurement comparison validity. Presented approach also has particular interest in determination of specified tool performance for predefined pattern recognition feature as well as for pattern recognition algorithm robustness study - direct interest for manufacturer. Precise matching estimation as part of Round Robin (RR) routines creating possibility to work with restricted amount of data and perform quick reliable qualification procedures. This paper concentrated on practical approach and used both simulation and actual data measurements data before and after proposed optimization taken by various generation tools by Hitachi (S-8840, S-9300, S-9380) in production environment
Problem statement:
Possibility to command set of CDSEMs tools in remote mode and instantly adress any failures and issues during POR run of the products ( patter recognition failures, measurement failures, lot release, beam adjustment, etc..) are highly valuable in HVM environment. CDSEM manufactures marketing such features for failrly long period of time, but significant issues is related to the cost and flexibility (layout dependence, tool type restriction, etc..)
Solution:
Development of the low cost remote operation center which virtually independent of listed above shortages and has basic operational capabililties of the manufacturers systems with wide range of flexibility regarding layout and type tool mdependance.
Such system succesfully developed in F18 and implemented for Litho Metrology operation..
Benefits results & summary:
1. HC reduction.
2. Cost - up to 20 times less than OEM.
3. Flexibility - could work with any tool type where remote operation could be beneficial.
4. No impact on local network.
Problem statement:
Challenges in achievement of critical layers CD stability in advanced flash processes are reaching significant complexity as result of tight process window imposed on expected focus/dose variation in lithography tool. Common way to perform feed forward prediction for focus/dose variation is creation of FE (Focus /Exposure) Map. In current methodology CD measurements is the only source of FE map construction. Taking in account that for critical layers significant amount of most sensitive features are created using OPC techniques which in turn impact shape/roughness of targeted CD with minimal variation of the focus/dose. Thus CD measurements could be highly unreliable for FE map predictor with commonly used quadratic approximation. Manual CD measurements data filtering is necessary condition in this case. Furthermore for some features quadratic approximation approach itself raising additional concerns about focus shifts for different dose levels. Also no well defined techniques exist for online focus performance tracing and focus trend detection partly due to the mentioned above approximation approach.
Solution:
The purpose of this paper is to discuss how to perform reliable feed forward FE prediction taking in account challenges in advanced flash processes. We introduce here additional variable for FE determination โ pattern recognition score and thus elimination manual data preprocessing. Also modification in commonly used approximation techniques introduced with sole purpose to differentiate positive and negative focus trends as part of superposition of classical FE map with score FE maps.
Description of general approach for algorithm stepping and various techniques for judgment of measurement validity presented in paper as well.
Benefits results & summary:
Elimination of manual data preprocessing and construction of reliable FE map predictor which
could in turn be used for online FE drift estimation as part of routine DCCD/FCCD check as well
as drastically reduction of FE measurement
Problem statement:
Challenges in achievement of critical layers CD stability in advanced flash processes are reaching significant complexity as result of tight process window imposed on expected focus/dose variation in lithography tool. Common way to perform feed forward prediction for focus/dose variation is creation of FE (Focus /Exposure) Map. In current methodology CD measurements is the only source of FE map construction. Taking in account that for critical layers significant amount of most sensitive features are created using OPC techniques which in turn impact shape/roughness of targeted CD with minimal variation of the focus/dose. Thus CD measurements could be highly unreliable for FE map predictor with commonly used quadratic approximation. Manual CD measurements data filtering is necessary condition in this case. Furthermore for some features quadratic approximation approach itself raising additional concerns about focus shifts for different dose levels. Also no well defined techniques exist for online focus performance tracing and focus trend detection partly due to the mentioned above approximation approach.
Solution:
The purpose of this paper is to discuss how to perform reliable feed forward FE prediction taking in account challenges in advanced flash processes. We introduce here additional variable for FE determination โ pattern recognition score and thus elimination manual data preprocessing. Also modification in commonly used approximation techniques introduced with sole purpose to differentiate positive and negative focus trends as part of superposition of classical FE map with score FE maps.
Description of general approach for algorithm stepping and various techniques for judgment of measurement validity presented in paper as well.
Benefits results & summary:
Elimination of manual data preprocessing and construction of reliable FE map predictor which
could in turn be used for online FE drift estimation as part of routine DCCD/FCCD check as well
as drastically reduction of FE measurement
LVTS Advanced matching matching concept for CDSEMVladislav Kaplan
ย
Motivation:
Significant challenges for various CD measurement matching procedures are reaching a comparable complexity as result of negative effects of roughness on the features. Due to the constant trend of integrated circuit in features reduction, impact of roughness start to be more destructive for various sets of measurement algorithms. Commonly used attempts to increase magnification for pattern recognition in addressing mode could in turn detect higher deviation from predefined patterns and thus initiate shift in placement of measurement gate.
Description of the approach:
The purpose of this paper is to discuss how to reduce measurement gate placement variation
impact and filter acquired data using edge correlation approach โ creation of width correlation
function represents particular feature under test and itโs comparison to โgoldenโ one as a mean of
detection of uncorrelated scans, which in turn should be excluded from overall computation of
matching results.
We describe general approach for algorithm stepping and various techniques for judgment of measurement comparison validity. Presented approach also has particular interest in determination of specified tool performance for predefined pattern recognition feature as well as for pattern recognition algorithm robustness study - direct interest for manufacturer.
Evaluation of results:
Precise matching estimation as part of Round Robin routines creating possibility to work with
restricted amount of data and perform quick reliable qualification procedures.
This paper concentrated on practical approach and used both simulation data and actual
measurement data before and after proposed optimization taken by various generation tools by
Hitachi (S-8840, S-9300, S-9380) in production environment.
In order to check performance of Fuzzy APC vs. WA APC simulation of the system performed (Labview).
Dose values were taken as input variables, also Focus values are present, but not used in simulation.
Membership function were created as well as for Dose and Focus variables.
Rules includes Dose and Focus impact, but feedback loop updates just Dose performance (close simulation for FAB Litho tool activity).
Actual simulation not included any translation of Dose values to CD values for given Focus, it assumes that any inconsistencies are added as WN or trend in the final measurement.
WA APC simulated as 5 tag window with 0.35/0.25/0.2/0.14/0.06 weights accordingly which is effectively matched NSO exponential weights average approach.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
ย
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
ย
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Cosmetic shop management system project report.pdfKamal Acharya
ย
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Forklift Classes Overview by Intella PartsIntella Parts
ย
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
ย
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the studentโs details, driverโs details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Courier management system project report.pdfKamal Acharya
ย
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Nuclear Power Economics and Structuring 2024Massimo Talia
ย
Title: Nuclear Power Economics and Structuring - 2024 Edition
Produced by: World Nuclear Association Published: April 2024
Report No. 2024/001
ยฉ 2024 World Nuclear Association.
Registered in England and Wales, company number 01215741
This report reflects the views
of industry experts but does not
necessarily represent those
of World Nuclear Associationโs
individual member organizations.
Final project report on grocery store management system..pdfKamal Acharya
ย
In todayโs fast-changing business environment, itโs extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
6. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
Abstract
Steganography is a โscienceโ, the method of hiding sent information. Unlike cryptography that
deals with coding of information, the main idea of steganography is hiding the fact that the message
exists. It embeds the secret message in cover media (image, audio, video, text, etc.). During the
last years with the development of digital image processing, methods of digital steganography
have gained a lot of popularity. The most popular steganography method is LSB (Last Significant
Bit) replacement in the cover image. With extensive evolution of steganography, Steganalysis
methods have a lot of importance. Steganalysis algorithms role is to detect a hidden secret message
inside any media. The most notable Steganalysis algorithm is the RS method [1], which detects
stegamesage by the statistical analysis applied on image pixels.
Shen Wang and others [2] created a new algorithm based on Genetic Shifting method (GSM).
GSM performs manipulation and modification of the original image pixels. GSM algorithm keeps
image statistic after inserting a hidden message and is hard to be detected by the RS analysis. The
goal of the project is to demonstrate effectiveness and stability of GSM algorithm against RS
analysis by using mathematical and statistical methods.
7. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
Table of Contents
1. Introductionโฆโฆโฆโฆโฆโฆโฆโฆ..โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.โฆ.โฆโฆโฆโฆโฆ........1
1.1 Backgroundโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.โฆโฆ.โฆโฆ.โฆ.......1
1.2 Applications and usage..โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...โฆ.โฆโฆโฆ.โฆ.......1
1.3 Digital steganography advantageโฆโฆโฆโฆโฆโฆโฆ..โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.โฆโฆ...โฆโฆ.2
1.4 Engineering problemโฆ..โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..โฆโฆโฆโฆโฆโฆโฆ.โฆ.โฆโฆ.โฆโฆโฆ.......2
1.5 Project Objectivesโฆโฆโฆโฆโฆโฆโฆโฆโฆ..โฆโฆโฆโฆโฆโฆโฆโฆ....โฆโฆโฆโฆโฆโฆโฆโฆโฆ........3
2. Literature surveyโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..โฆ..4
2.1 Steganography techniques limitationsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...4
2.2 Terminology and Definitionsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.....4
2.2.1 Steganographyโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..4
2.2.2 Secret messageโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.โฆโฆโฆโฆ4
2.2.3 Cover mediaโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.โฆโฆโฆโฆโฆ....โฆ....4
2.2.4 Key ๐โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...โฆ.โฆ4
2.2.5 Stegoimageโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..5
2.2.6 Steganographic algorithmโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..โฆโฆโฆ..5
2.2.7 Steganographic system or Stegasystemโฆโฆโฆโฆโฆโฆโฆโฆ...โฆโฆโฆโฆโฆโฆ..โฆ.โฆโฆโฆ.5
2.2.8 Steganalysisโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..โฆโฆโฆโฆโฆโฆ...โฆ6
2.2.9 Steganalyst โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..6
2.2.10 Attack on steganography systemโฆโฆโฆโฆโฆโฆโฆ...โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..6
2.3 Stegattacks methods and classesโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...โฆ.7
2.3.1 Classes of the stegattacksโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..7
2.3.2 The results of stegattackโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.โฆโฆโฆโฆโฆโฆ..7
2.3.3 Three main methods are used to perform stegattackโฆโฆโฆโฆโฆ...โฆโฆโฆโฆโฆ...โฆโฆโฆ.7
2.4 Human eye propertiesโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ7
2.4.1 Human eye brightness sensitivityโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.8
2.4.2 Human eye frequency sensitivityโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...โฆโฆโฆโฆโฆโฆโฆโฆโฆ..9
2.4.3 Masking effectโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ10
8. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
2.5 Classification of Steganography Categoriesโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...โฆโฆโฆโฆโฆโฆโฆโฆ..11
2.6 Classification of Steganography Methodsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..โฆ..12
2.6.1 Substitution methods substitute redundant parts of a cover with a secret message (spatial
domain)โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ....12
2.6.2 Transform domain techniques embed secret information in a transform space of the signal
(frequency domain)โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..โฆโฆโฆโฆโฆโฆ13
2.6.3 Spread spectrum techniques adopt ideas from spread spectrum communicationโฆโฆโฆโฆ13
2.6.4 Statistical methods encode information by changing several statistical properties of a cover
and use hypothesis testing in the extraction processโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.13
2.6.5 Distortion techniques store information by signal distortion and measure the deviation from
the original cover in the decoding stepโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..14
2.6.6 Cover generation methods encode information in the way a cover for secret communication
is createdโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...14
2.7 Classification of Steganalysis Categoriesโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.14
2.8 Classification of Steganalysis Methods and Techniquesโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.15
2.8.1 Visual Attacksโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ15
2.8.2 Histogram Analysis Attackโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ16
2.8.3 Statistical Analysis Attackโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..17
2.8.4 Stego Only Attackโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..17
2.8.5 Known Cover Attackโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..17
2.8.6 Known Message Attackโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..18
2.8.7 Blind Steganalysisโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..18
2.8.8 Semi-blindโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..18
2.9 RS Steganalysis algorithmโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..โฆ..18
2.10 Genetic Shifting algorithm (GSM)โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.21
2.10.1 Steps of GSMโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.22
3. Description and system requirementsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...24
3.1 Program interfaceโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.24
3.2 Front panel viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...โฆโฆโฆโฆโฆ25
9. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
3.3 Steps for basic encoding and decoding procedure of imagesโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ26
3.4 LSB steganography stepsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.30
3.5 Digital image definitionโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ....31
3.6 Message embedding mathematical definitionโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..31
4. Steps of experiment, discussion and definitionโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.33
4.1 Steps definitionโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.โฆโฆโฆโฆโฆโฆโฆโฆโฆ33
4.2 Build LabVIEW based steganography systemโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.33
4.3 Perform basic message coding and recovery. Compare visual image degradationโฆโฆโฆโฆ..35
4.3.1 Perform LSB-1 codingโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...35
4.3.2 Comparing toolโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...37
4.3.3 Perform LSB-2 codingโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...39
4.3.4 Perform LSB-3 codingโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...41
4.3.5 Perform LSB-4 codingโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...43
4.3.6 Intermediates conclusionsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..44
4.4 Compare visual degradation through common tools (Histogram, STD)โฆโฆโฆโฆโฆโฆโฆโฆ.44
4.4.1 Histogramโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...44
4.4.2 Intermediates conclusionsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..49
4.5 RS analysis (Fridrich algorithm) routine implementationโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ49
4.5.1 Confirm validity of RS analysis on gray imagesโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.49
4.5.2 Intermediates conclusionsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..56
4.5.3 RS analysis for LSB โ 2, 3, 4 levelsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ57
4.5.4 Intermediate conclusionโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.63
4.6 Implement secure generic steganography method for RS baseline shifting for LSB-1โฆโฆ.โฆ63
4.6.1 Perform basic message encoding and recovery with shifting algorithmโฆโฆโฆโฆโฆโฆโฆ.63
4.7 Perform RS analysis comparison for different message length with GSM for RS shifting and
without, use different โsnakeโ division array image representationโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ64
4.7.1 13 division snaked array analysisโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...64
4.7.2 29 division snaked array analysisโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...67
4.7.3 51 division snake array analysisโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.69
10. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
4.7.4 Intermediate conclusionโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.70
5. Summary, compare results and conclusionsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.71
6. Problems and solutionsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.73
Attachmentsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...โฆโฆโฆโฆโฆโฆ.A-1
A. Introduction to LabVIEWโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.โฆ.A-1
A.1L LabVIEW pre phraseโฆโฆโฆโฆโฆโฆ..................................................................................A-1
A.2 Dataflow Programmingโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.A-1
A.3Graphical Programmingโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.A-2
A.4 The LabVIEW Environmentโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.A-2
A.5Front Panelโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.A-3
A.6Block Diagramโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...A-4
A.7Controls Paletteโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..A-5
A.8Function Paletteโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.A-7
A.9Tools paletteโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..A-8
A.10 Wiringโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..A-8
A.11 SubVisโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.A-8
B. Main program proceduresโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.B-1
B.1Open image sequenceโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.B-1
B.2Message to image embeddingโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.B-1
C. Comparing tool proceduresโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆC-1
D. Additional literature surveyโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆD-1
11. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
List of Figures
Figure 2.2.1 Simplified model of Stegasystemโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ....6
Figure2.4.1 Human eye sensitivity to contrast and threshold of indistinguishability โ ๐ผโฆโฆโฆโฆ.8
Figure 2.4.2 Experimental data by Aubert (1865), Koenig and Brodhun (1889) and Blanchard
(1918). It indicates that the Weber-Fechner law - according to which the smallest perceptible
change in intensity โ ๐ผ vs. intensity level I is constantโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..9
Figure 2.4.3 Sensitivity of eye for the colorsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.10
Figure 2.4.4 Herman Gridโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..11
Figure 2.7.1 The hierarchy of the classification of Steganalysis techniquesโฆโฆโฆโฆโฆโฆโฆโฆ..15
Figure 2.8.1 Grayscale image visual attack exampleโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.16
Figure 2.8.2 Grayscale image filter visual attack exampleโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ16
Figure 2.8.3 Grayscale image Cover image versus Stegoimage histogramโฆโฆโฆโฆโฆโฆโฆโฆโฆ17
Figure 2.9.1 RS-diagram of an image taken by a digital camera. The X-axis is the percentage of
pixels with flipped LSBs, the Y-axis is the relative number of regular and singular groups with
mask Mโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...21
Figure 2.10.1 Basic diagram of proposed GSM methodโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ23
Figure 3.1.1 โSteganographyโ directory viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...24
Figure 3.2.1 Detailed program front panel viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ....25
Figure 3.3.1 โEncoding / Decodingโ dashboard viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ26
Figure 3.3.2 โInput filesโ directory viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...27
Figure 3.3.3โCover Imagesโ directory viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.โฆ.27
Figure 3.3.4 Resulting โStegoimageโ directory viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ28
Figure 3.3.5 message recovery processโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.29
Figure 3.3.6 Resulting recovered message viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ29
Figure 3.4.1 LSB stepsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...30
Figure 4.2.1 Program block diagram viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.34
Figure 4.2.2 Detailed Program block diagram viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...34
Figure 4.3.1 LSB-1 Cover and Stego visual comparison, Input Data5.txt have been embedded...36
Figure 4.3.2 1LSB โGreyโ pattern, Input Data5.txt have been embeddedโฆโฆโฆโฆโฆโฆโฆ....โฆ37
12. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
Figure 4.3.3 Compare tool front panel viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.......38
Figure 4.3.4 Compare tool calculation panel viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.38
Figure 4.3.6 LSB-3 Cover and Stego visual comparison, Input Data5.txt have been embeddedโฆ41
Figure 4.3.7 LSB-3 โGreyโ pattern visual comparison, Input Data5.txt have been embeddedโฆ.42
Figure 4.4.1 Histogram and STD representation in LabVIEWโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..45
Figure 4.4.2 Cover image versus Histogramโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..45
Figure 4.4.3 LSB-1 and LSB-2 level versus Cover image Histogramโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ46
Figure 4.4.4 LSB-3 and LSB-4 level versus Cover image Histogramโฆโฆโฆโฆโฆโฆโฆโฆ.โฆโฆ...46
Figure 4.5.1 LabVIEW RS analysis implementationโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.49
Figure 4.5.1 Plot legend (applicable for all next RS stegoanalisys plots)โฆโฆโฆโฆโฆโฆโฆโฆโฆ..50
Figure 4.5.2 Example 1. RS analysis results on Stegoimageโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..51
Figure 4.5.3 Example 2. RS analysis results on Stegoimageโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..52
Figure 4.5.4 Images used in next RS analysisโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ53
Figure 4.5.5 Plot Image 1 ๐ ๐ โ ๐ ๐ versus bit replacedโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.55
Figure 4.5.6 Plot Image 2 ๐ ๐ โ ๐ ๐ versus bit replacedโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.55
Figure 4.5.7 Plot Image 3 ๐ ๐ โ ๐ ๐ versus bit replacedโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.56
Figure 4.5.8 Plot LSB-1 Average Image3 ๐ ๐ โ ๐ ๐ versus bit replaced. (Red line represents
normalized linear trend line dependence.)โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..57
Figure 4.5.9 Image2 LSB-2, 3, 4 RS analysis graph representationโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..58
Figure 4.5.10 Image 2 LSB-2 RS analysis plotโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..60
Figure 4.5.11 Image 2 LSB-3 RS analysis plotโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.60
Figure 4.5.12 Image 2 LSB-4 RS analysis plotโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..61
Figure 4.5.13 Average LSB-2 RS analysis plot. (Red line represents normalized linear trend line
dependence.)โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..61
Figure 4.5.14 Average LSB-3 RS analysis plot. (Red line represents normalized linear trend line
dependence.)โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..62
Figure 4.5.15 Average LSB-4 RS analysis plot. (Red line represents normalized linear trend line
dependence.)โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..62
Figure 4.6.1 Example image 8 ร 8 matrix representationโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ63
13. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
Figure 4.6.2 Example image โsnakeโ representationโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.....63
Figure 4.6.3 โsnakeโ dividingโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ....64
Figure 4.7.1 Average shifted with 13 division LSB-1 RS analysis plot. (Red line represents
normalized linear trend line dependence.)โฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..66
Figure 4.7.2 Average shifted with 29 division 1LSB RS analysis plotโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...68
Figure 4.7.3 Average shifted with 51 division 1LSB RS analysis plotโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...70
Figure A.1 Block diagram of Dataflow ProgrammingโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆA-2
Figure A.2 Getting started windowโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.A-3
Figure A.3 Example of Front panel viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆA-4
Figure A.4 Example of Block diagram viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...A-5
Figure A.5 Controls palette viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.A-6
Figure A.6 Function palette viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆA-7
Figure A.7 Tools palette viewโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.A-8
FigureB.1 Image opening by using Standard opening procedure in LabVIEWโฆโฆโฆโฆโฆโฆ..B-1
Figure B.2 Message to binary chain conversionโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..B-2
Figure B.3 Message to image merges LabVIEW implementationโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ..B-3
Figure B.4 Genetic shifting algorithm LabVIEW implementationโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.B-3
Figure C.1 comparing tool LabVIEW implementationโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ...C-1
Figure C.2 comparing tool image binarisationโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆC-1
14. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
List of Tables
Table 3.6.1 capacity as function of LSB levelโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ32
Table 4.3.1 Used messages sizesโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ35
Table 4.3.2 Cover image pixel matrixโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ39
Table 4.3.3 Stegoimage pixel matrixโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.39
Table 4.3.4 Difference image pixel matrixโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.39
Table 4.3.5 LSB-2 Cover and Stego visual comparisonโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.โฆโฆโฆโฆโฆ40
Table 4.3.8 LSB-4 Cover and Stego visual comparisonโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.43
Table 4.4.1 Histogram degradation trough of message enlargement for LSB-4 levelโฆโฆโฆโฆโฆ47
Table 4.4.2 STD graph degradation trough of LSB level increaseโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.48
Table 4.5.1 message number versus message lengthโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.50
Table 4.5.1 Example 1 ๐ ๐, ๐ ๐, ๐ โ๐, ๐โ๐ pairs versus message volumeโฆโฆโฆโฆโฆโฆโฆโฆโฆ..51
Table 4.5.2 Example 1 ๐ ๐, ๐ ๐, ๐ โ๐, ๐โ๐ pairs versus message volumeโฆโฆโฆโฆโฆโฆโฆโฆโฆ...53
Table 4.5.3 ๐ ๐/๐ ๐ differences versus message volumeโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ54
Table 4.5.4 Average ๐ ๐/๐ ๐ differences versus message volumeโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.56
Table 4.5.5 Image 2 RS analysis resultsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.59
Table 4.7.1 Shifted with 13 division LSB-1 RS analysis resultsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.65
Table 4.7.2 Shifted with 29 division 1LSB RS analysis resultsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.67
Table 4.7.3 Shifted with 51 division 1LSB RS analysis resultsโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆโฆ.69
15. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
1
1. Introduction
1.1 Background
In modern word information is has great value. With global computer networks appearance volume
of transmitted and received information has been increased, a lot of data transferred via global
webs. And as results of easy accessibility to different information, sometimes to high sensitive
information, there is a need to protect data security and threat unauthorized access to information.
On other hand, with advancements in digital communication technology and the growth of
computer power and storage, the difficulties in ensuring individualsโ privacy become increasingly
challenging. Data, intellectual property and privacy protection โ this is scabrous problem with that
we face on a daily basis.
Various methods have been investigated and developed to perform data protection and personal
privacy. Encryption is probably the most obvious one, and then comes steganography. Encryption
lends itself to noise and is generally observed while steganography is not observable.
Unfortunately it is sometimes not enough to keep the contents of a message secret, it may also be
necessary to keep the existence of the message secret.
Steganography is the art and science of invisible communication. This is accomplished through
hiding information in other information, thus hiding the existence of the communicated
information. The word steganography is derived from the Greek words โstegosโ meaning โcoverโ
and โgrafiaโ meaning โwritingโ defining it as โcovered writingโ.
1.2 Applications and usage:
In general, steganography approaches hide a message in a cover e.g. text, image, audio file, etc.,
in such a way that is assumed to look innocent and there for would not raise suspicion [3].
Except to transfer secret information or embed secret messages into media, one of important and
perspective application of steganography is to protect intellectual property and copyright on digital
media, images, books to avoid unauthorized copying and theft. The special, mark (DIGITAL
WATER MARK) is embedded in to protected object, this mark is invisible by eye but can be
detected by the software features.
16. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
2
In recent years digital image-based steganography has established itself as an important discipline
in signal processing.
1.3 Digital steganography advantage:
The advantage of steganography algorithm is because of its simple security mechanism. Because
the steganographic message is integrated invisibly and covered inside other harmless sources, it is
very difficult to detect the message without knowing the existence and the appropriate encoding
scheme.
The main advantages of digital images steganography is:
๏ท There are a variety of methods used in which information can be hidden in the images.
๏ท Relatively large volume of digital images representation, that allows the embedding of
large amount of information.
๏ท Known size of the cover media, that absence of restrictions, requirements imposed by real-
time.
๏ท Presence of relatively large textural regions in most digital images that have noise structure
and well suited for information integration.
๏ท Weak sensitivity of the human eye to minor changes the color of the image, brightness,
contrast and the noise presence.
๏ท Image steganography has come quite far with the development of fast, powerful graphical
computers.
1.4 Engineering problem:
In this work three main problems are appeared:
๏ท Build working steganography model, based on LabVIEW software.
๏ท Understand and perform RS analysis attack based on Fridrich works.
๏ท Improve existing Shen Wang and al [2] Genetic Shifting Method (GSM).
๏ท Validate effectiveness of GSM against Fridrich RS analysis [1].
17. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
3
1.5 Project Objectives:
The main purpose of this work is to study LSB based Steganographic and Steganalysis methods.
Implement and study RS Fridrich algorithm [1]. In second part of work introduce modified
โGenetic shifting Algorithmโ proposed by Shen Wang and al [2], method of embedding secret
message in to digital image, without causing visual degradation of cover/stego image and to avoid
stegamesage presence detection by RS Analysis algorithm.
Opposite to Shen Wang steganography method, which performs final stegoimage bits
manipulation, this paper is deals with original โcoverโ image. Changes are made in cover image
with target to โworsenโ bits statistics. And as a result of this permutations, secret message
embedding provides โpositiveโ statistics changes that affect RS analysis determine message
existence.
The current project objectives are:
1. Perform comparison visual and statistical analysis for different message length.
2. Check what message length can be embedded into cover image without visual or statistical
image degradation.
3. Check dependence of the image degradation from embedded message length.
4. New Stego optimized Genetic Shifting Algorithm definition.
5. Confirm effectiveness of new method in interaction with Fridrich RS algorithm, for Grey
scale images.
18. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
4
2. Literature survey
2.1 Steganography techniques limitations [3], [4], [5].
Research in hiding data inside image using steganography techniques has been done by many
researches. Some methods have some limitations, such as:
1. Stegoimage capacity - length of embedded message. Ability to hide messages inside image
without visual or statistical image degradation.
2. Computation limitation โ algorithms or methods which requires high computer resources and
many computer (program) time for data processing.
3. Recovery problems โ โtricksโ steganography methods which have problem with recovery secret
message without errors and lost data.
4. Low security methods โ algorithms which can be simply or detected by different
Steganalysis procedures: visual analysis, statistical analysis, histograms, etc.
2.2 Terminology and Definitions[3], [4]:
2.2.1 Steganography.
Is a โscienceโ, the method of hiding of sending information. Unlike cryptography that deals with
coding of information, the main idea of steganography is hiding the fact that the message exists.
2.2.2 Secret message.
A message m, which will be embedded in to cover media.
2.2.3 Cover media.
Image, audio file, test or other kind of containers b, which can be used for secret data embedding.
2.2.4 Key ๐.
The method that define algorithm of specific Stegasystem.
19. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
5
2.2.5 Stegoimage.
This is image with secret message embedded inside. Can defined like container of form ๐ ๐,๐ for
key using systems or ๐ ๐ for no key using systems.
2.2.6 Steganographic algorithm.
This is two ways transformation is applied on the media container. Forward steganographic
transformation meet equation 2.2.1 and inverse steganographic transform according to equation
number 2.2.2
(2.2.1) ๐น: ๐ ๐ฅ ๐ต ๐ฅ ๐พ โ ๐ต
(2.2.2) ๐นโ1
: ๐ต ๐ฅ ๐พ โ ๐
Need remember condition number 2.2.3 for key used systems.
(2.2.3) ๐น(๐, ๐, ๐) = ๐ ๐,๐ ; ๐๐๐ ๐นโ1
(๐ ๐,๐, ๐) = ๐
Or condition 2.2.4 for no key used systems.
(2.2.4) ๐น(๐, ๐) = ๐ ๐ ; ๐๐๐ ๐นโ1(๐ ๐, ๐) = ๐
2.2.7 Steganographic system or Stegasystem.
This is set of tools and methods are used to generate a secret channel of information transmission.
The following assumptions should be considered in the stegosystem:
1. The steganalyst has a complete knowledge of the steganographic systems and the details of
their implementation. The only information that remains unknown is the presence and content
hidden message.
20. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
6
2. If the steganalyst somehow can detect the fact of hidden message existence, it should not allow
him to remove this message from the media. And in ideal case, not allow him to detect the
message volume (length).
Basic Steganographic โkeyโ used system is presented in Figure 2.1.1
Figure 2.2.1 Simplified model of Stegasystem.
2.2.8 Steganalysis.
Steganalysis algorithms role is to detect a hidden secret message inside any media.
2.2.9 Steganalyst.
The person, whose role is to work with cover media to detect the fact of secret image presence.
Recovery or destroy the secret message.
2.2.10 Attack on steganography system
This is applying Steganalysis on cover media to detect secret message existence. Unlike
Cryptography, a disclosure (crack) of steganography system, this is determine whether the hidden
information in the container, and the opportunity to prove this approval to the third party with a
high degree of certainty.
21. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
7
2.3 Stegattacks methods and classes [5], [6]:
2.3.1 Classes of the stegattacks:
๏ท Attack with the knowledge of the modified media only.
๏ท Attack with knowledge of unmodified container.
2.3.2 The results of stegattack:
๏ท Detect secret message presence.
๏ท Recover secret message from stegoimage.
๏ท Destroy the message in case no possibility to recover message.
2.3.3 Three main methods are used to perform stegattack:
๏ท Visual analysis โ detect visual image degradation by โnakedโ eye.
๏ท Statistical Histogram and STD analysis.
๏ท Detection methods are based on data hiding analyzing the characteristics of the probability
distribution of the container.
2.4 Human eye properties [3].
The properties of the human eye used in the steganography and for stega- algorithms development.
Visual Attacks are widely regarded as the simplest form of Steganalysis. A visual attack largely
involves examining the subject file with the naked eye to identify any obvious inconsistencies. In
visual analyzing stage, steganalyst must to decide is an image whether interest for future analysis
or not, in another words decide presence stega-message in cover image. Of course, the first rule
of steganography is that any modifications made to a file should not result in quality degradation,
so a good method implementation will create stegoimage that do not look any more suspicious
than the cover image. However, when we remove the parts of the image that were not altered as a
result of embedding a message, and instead concentrate on the likely areas of embedding in
isolation, it is usually possible to observe signs of manipulation. It can therefore be argued that the
key aspect of a successful visual attack is to correctly determine which features of the image can
22. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
8
be ignored (redundant data), and which features should be considered (test data) in order to test
the hypothesis that a suspect image contains steganography.
Can be selected three most important characteristics that influence to the background noise in the
images: selectivity to brightness fluctuations, frequency sensitivity and masking effect.
2.4.1 Human eye brightness sensitivity.
Human eye brightness sensitivity can be measured through next experiment (scheme of experiment
is displayed in Figure 2.4.1):
The person has to focus on the test monotone picture, after the eye is adapted to the illuminance ๐ผ
of the picture, start gradually change the brightness around the central spot. Changing of
illuminance โ ๐ผ continue as long as it will not be detected.
Figure2.4.1 Human eye sensitivity to contrast and threshold of indistinguishability โ ๐ฐ
Figure 2.4.2 shows the dependence of the minimum contrast sensitivity in brightness ๐ผ
โ๐ผโ changes.
As can be seen from the Figure 2.4.2, for mid-range brightness variations the contrast value is
approximately constant. Whereas for small and large brightness threshold indistinguishable
increases. It was found that โ ๐ผ โ 0.01 โ 0.03 ๐ผ for medium brightness values.
23. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
9
Figure 2.4.2 Experimental data by Aubert (1865), Koenig and Brodhun (1889) and
Blanchard (1918). It indicates that the Weber-Fechner law - according to which the
smallest perceptible change in intensity โ ๐ฐ vs. intensity level I is constant.
But according to new modern research in this branch detected that for smallest brightness values
the threshold indistinguishable decreases, that is human eye is more sensitive for noise in this
range.
2.4.2 Human eye frequency sensitivity.
Human eye frequency sensitivity determined by the fact that people are much more susceptible to
low frequency (LF) than to the high frequency (HF) noise.
The experiment to detect frequency sensitivity is very same to previous one, but in this case
changes are applying on spatial frequency of the picture as long as it will not be detected by eye.
Human eye to color sensitivity dependents is presented in Figure 2.4.3.
24. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
10
Figure 2.4.3 Sensitivity of eye for the colors.
2.4.3 Masking effect.
The Human eye construction is divide incoming visual signal into independent components, every
component have different spatial and frequency properties. These components transmitted by
different photoreceptors to the retina. In case, few components have same (or very close) spatial
and frequency characteristics they affect same photoreceptors in the eye. As result of this case the
masking effect is presence.
The perfect example of disorientation of Human eye this is Herman Grid presented in Figure 2.4.4.
The intensity at a point in the visual system is not simply the result of a single receptor, but the
result of a group of receptors which respond to the presentation of stimuli in what is called a
receptive field.
25. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
11
Figure 2.4.4 Herman Grid
The most of high quality stegaalgorithms are use Human eye properties are listed above. Usage of
these properties helps to avoid stegoimage visual detection and as result of this the stegoimage
canโt be attacked by digital Steganalysis.
2.5 Classification of Steganography Categories [6].
Steganography is classified into 3 categories:
๏ท Pure steganography where there is no stego- key. It is based on the assumption that no other
party is aware of the communication;
๏ท Secret key steganography where the stego key is exchanged prior to communication. This
is most susceptible to interception;
๏ท Public key steganography where a public key and a private key is used for secure
communication;
26. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
12
2.6 Classification of Steganography Methods [6].
๏ท Substitution methods substitute redundant parts of a cover with a secret message (spatial
domain);
๏ท Transform domain techniques embed secret information in a transform space of the signal
(frequency domain);
๏ท Spread spectrum techniques adopt ideas from spread spectrum communication;
๏ท Statistical methods encode information by changing several statistical properties of a cover
and use hypothesis testing in the extraction process;
๏ท Distortion techniques store information by signal distortion and measure the deviation from
the original cover in the decoding step;
๏ท Cover generation methods encode information in the way a cover for secret communication
is created;
2.6.1 Substitution methods substitute redundant parts of a cover with a secret message (spatial
domain).
These techniques use the pixel gray levels and their color values directly for encoding the message
bits. These techniques are some of the simplest schemes in terms of embedding and extraction
complexity. The major drawback of these methods is amount of additive noise that creeps in the
image which directly affects the Peak Signal to Noise Ratio and the statistical properties of the
image.
One of the common and popular data hiding methods is based on manipulating the Least
Significant Bit (LSB) planes, by direct replacing the LSBโs of the pixel value of the cover image
with the secret message bits. This is the simplest of the digital steganography methods and good
example for explain the main idea behind the bit manipulating theory. The imbedding process
consists of the sequential substitution of each LSB of image pixel for the bit message.
27. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
13
2.6.2 Transform domain techniques embed secret information in a transform space of the signal
(frequency domain):
These techniques try to encode message bits in the transform domain coefficients of the image.
Data embedding performed in the transform domain is widely used for robust watermarking.
Similar techniques can also realize large capacity embedding for steganography. Candidate
transforms include discrete cosine Transform (DCT), discrete wavelet transform (DWT), and
discrete Fourier transform (DFT). By being embedded in the transform domain, the hidden data
resides in more robust areas, spread across the entire image, and provides better resistance against
signal processing.
2.6.3 Spread spectrum techniques adopt ideas from spread spectrum communication:
Spread-spectrum communication describes the process of spreading the bandwidth of a
narrowband signal across a wide band of frequencies. This can be accomplished by modulating
the narrowband waveform with a wideband waveform, such as white noise. After spreading, the
energy of the narrowband signal in any one frequency band is low and therefore difficult to detect.
In these techniques typically uses a binary signal, within very low power white Gaussian noise.
The resulting signal, perceived as noise, is then combined with the cover image to produce the
stegoimage.
2.6.4 Statistical methods encode information by changing several statistical properties of a cover
and use hypothesis testing in the extraction process:
Statistical methods for hiding information based on altering some statistical properties of the
image. They are based on verification of statistical hypotheses. The idea of this method is to change
statistical pattern of the image in manner, whereby received side only can to distinguish modified
image from not modified.
28. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
14
2.6.5 Distortion techniques store information by signal distortion and measure the deviation from
the original cover in the decoding step:
Distortion techniques require the knowledge of the original cover in the decoding process.
Embedding scheme is based on consistent cover image modification by using pseudorandom bits
permutations. The sender first choses ๐ฟ(๐) different cover-pixels he wants to use for information
transfer. Such a selection can again be done using pseudorandom number generators or
pseudorandom permutations. To encode a 0 in one pixel, the sender leaves the pixel unchanged:
to encode a 1, he adds a random value โ ๐ to the pixelโs color. Although this approach is similar
to a substitution system, there is one significant difference: the LSB of the selected color values
do not necessarily equal secret message bits. In particular, no cover modifications are needed when
coding 0. Furthermore, โ ๐ can be chosen in a way that better preserves the coverโs statistical
properties.
2.6.6 Cover generation methods encode information in the way a cover for secret communication
is created:
In contrast to all embedding methods presented above, where secret information is added to a
specific cover by applying an embedding algorithm, some steganographic applications generate a
digital object only for the purpose of being a cover for secret communication.
2.7 Classification of Steganalysis Categories [6].
Normally, Steganalysis can be dividing into two main categories:
๏ท Visual Attacks
๏ท Statistical Attacks
The next Figure 2.7.1 provides visual scheme of Steganalysis hierarchy. Every analysis starts with
visual inspection, only then the steganalyst decides to continue with complicated analysis or not.
29. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
15
Figure 2.7.1 The hierarchy of the classification of Steganalysis techniques.
2.8 Classification of Steganalysis Methods and Techniques [4], [6].
2.8.1 Visual Attacks.
Steganalysis by visual attack was used early in Steganalysis research. The idea of visual attacks is
to remove any parts of the image that cover the message in order for the human eye to distinguish
where there is any hidden message or still image content. An example for sequential embedding
can be to extract the LSB plane of the image and check for any possible suspicious structure in the
image. The LSB plane of a natural grayscale image can be seen in Figure 2.8.1, where it is clear
that there are not any suspicious structures, while viewing the LSB plane of a Stego made with
30. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
16
sequential embedding we can see some sort of structure on the left-most part which can lead to
further investigation in the image.
Natural image Stegoimage
Figure 2.8.1 Grayscale image visual attack example.
Another more technical way to make a visual attack is to apply specific filters on the image and compare it
with a known natural image filtered with same filter, like displayed in Figure 2.8.2.
Natural image filtered Stegoimage filtered
Figure 2.8.2 Grayscale image filter visual attack example.
2.8.2 Histogram Analysis Attack.
Histograms analysis attack works on JPEG sequential and pseudo-random embedding type stegosystems.
It can effectively estimate the length of the message embedded and it is based on the loss of histogram
symmetry after embedding. Figure 2.8.3 is displays comparison of natural and stegoimage histograms.
31. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
17
Natural image Natural image histogram Stegoimage histogram
Figure 2.8.3 Grayscale image Cover image versus Stegoimage histogram.
2.8.3 Statistical Analysis Attack.
Changes will be apparent in the statistical property of cover image if the secret message bits are
inserted in image. In most of the original digital images exists a high matching between the pixels
that are placed next to each other [1], in case any bit manipulation is performed this causes a
matching between pixels is worsens. More deliberately, it can be achieved by coding a program
that examines the stegoimage structure and measures its statistical properties: first order statistics,
histograms or second order statistics, correlation between pixels, distance and direction.
2.8.4 Stego Only Attack.
In a Stego-only attack the steganalyst does not have any other information available apart from the
Stego medium investigated. Realistically, the only way a steganalyst would be able to attack it is
by trying every possible known attacks on current steganographic algorithms.
2.8.5 Known Cover Attack.
In a known cover attack apart from the stego medium, the original cover medium is also available.
In this scenario, the steganalyst can find differences in the two mediums and hence attempt to find
what kind of steganographic algorithm was used.
32. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
18
2.8.6 Known Message Attack.
A known message attack can be used when the hidden message is revealed. The steganalyst by
knowing the hidden message can attempt to analyze the Stegoimage for future attacks. Even by
knowing the message, this may be very difficult and may even be considered equivalent to the
Stego-only attack.
2.8.7 Blind Steganalysis.
Technique is designed to work on all types of embedding techniques and image formats. In a few
words, a blind Steganalysis algorithm โlearnsโ the difference in the statistical properties of pure
and Stego images and distinguish between them. The โlearningโ process is done by training the
machine on a large image database. Blind techniques are usually less accurate than targeted ones,
but a lot more expandable.
2.8.8 Semi-blind.
Technique Steganalysis works on a specific range of different Stego-systems. The range of the
Stego-systems can depend on the domain they embed on, i.e. spatial or transform.
2.9 RS Steganalysis algorithm [1].
Among the methods, the RS Steganalysis algorithm proposed by Fridrich [1], is considered as the
most reliable and accurate method to detect LSB replacing and other bit manipulation
steganography. Fridrich et al. propose a statistical method that uses high order statistics.
This algorithm is worked with regular and singular groups to measure relationship of pixels.
LSB replacement violates the proportion between regular and singular groups and the existence of
the steganography is detected, the secret message length can be estimated by the amount of regular
and singular groups.
In current work RS method is used like reference to prove the viability of proposed improved
Genetic Shifting Algorithm.
33. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
19
According to Fridrich method the image is partitioned into not overlapping groups of a fixed shape.
The LSB embedding increase the noisiness in the image, and thus expects that the value of
discrimination function ๐ to increase after LSB embedding. The LSB embedding process
described using flipping functions ๐น1 ๐๐๐ ๐นโ1.
Positive flipping ๐น1 โ transformation relationship between 2๐ ๐๐๐ (2๐ + 1) (0-1, 2-3โฆ 254-255).
Negative flipping ๐นโ1 โ transformation relationship between (2๐ โ 1)๐๐๐ 2๐ (-1-0, 1-2โฆ 255-
256).
None flipping ๐น0.
The relationship between two flipping according to equation 2.9.1
(2.9.1) ๐นโ1 = ๐น1(๐ฅ + 1) โ 1
Define ๐น0 according to equation 2.9.2
(2.9.2) ๐น1(๐ฅ) = ๐ฅ
Now we are can define flipping group โ applying flipping function on pixels of image block,
according to 2.9.3.
(2.9.3) ๐น(๐บ) = (๐น ๐(1)(๐ฅ1), ๐น ๐(2)(๐ฅ2), โฆ ๐น ๐(๐)(๐ฅ ๐)
Regular and Singular groups subject to the next rules: equations 2.9.4 and 2.9.5
(2.9.4) ๐(๐น(๐บ)) > ๐(๐บ)
(2.9.5) ๐(๐น(๐บ)) < ๐(๐บ)
The discrimination function ๐and the flipping operation ๐น define three types of pixel groups. By
using concept of shifted LSB flipping or negative mask applying. Each group is classified as
34. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
20
โregularโ ,โsingularโ or โunchangedโ depending on whether the pixel noise within the group is
increased or decreased after flipping the LSBโs of fixed set pixels within each group (the pattern
of pixels to flip is called the โmaskโ M). The classification is repeated for a dual type of flipping.
Some theoretical analysis and some experimentation show that the proportion of regular and
singular groups form curves quadratic in the amount of message embedded by the LSB method.
Under a similar assumption to above, this time about the proportions of regular and singular groups
with respect to the standard and dual flipping, sufficient information can be gained to estimate the
proportion of an image in which data is hidden. Statistically tested that applying flipping on typical
image total number of โRegularโ groups will be larger than the total number of โSingularโ groups.
For positive flipping, denote the number of Regular groups for mask ๐ as ๐ ๐ (in percents of all
groups). Similarly, ๐ ๐ will denote the number of Singular groups. In the same way ๐ โ๐ and ๐โ๐
are defined as the number of Regular and Singular blocks after the negative flipping.
In case embedding โzeroโ message in typical cover image ๐ ๐ is approximately equal to ๐ โ๐, and
the same should be true for ๐ ๐ and ๐โ๐.
According to Fridrich statistically analysis permutations in LSB plane forces the difference
between ๐ ๐ and ๐ ๐ to zero as the length m of the embedded message increases. Another words,
after flipping some quantity of LSB we obtain result ๐ ๐ โ ๐ ๐. But this applies opposite effect on
๐ โ๐ and ๐โ๐ components- their difference increases with the length m of imbedded message.
The principle of Fridrich steganalytic method, which called RS Steganalysis, is to estimate the four
curves of the RS diagram and calculate their intersection using extrapolation. Fridrich collected
experimental evidence that the ๐ โ๐ and ๐โ๐curves are well modeled with straight lines, while
second-degree polynomials can approximate the โinnerโ curves ๐ โ๐ and ๐โ๐ reasonably well.
Statistical data accumulated by Fridrich is presented in Figure 2.9.1.
35. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
21
Figure 2.9.1 RS-diagram of an image taken by a digital camera. The X-axis is the
percentage of pixels with flipped LSBs, the Y-axis is the relative number of regular and
singular groups with mask M.
2.10 Genetic Shifting algorithm (GSM) [2].
Shen Wang and al [2], propose new โGeneticโ based algorithm in which the existence of the secret
message is hard to be detected by the RS analysis [1]. And better visual quality of stegoimage can
be achieved by this steganography method. The main idea of Genetic algorithm to search for a best
adjustment matrix. Genetic algorithm is a general optimization algorithm. After secret message is
embedded and stegoimage is received the type (regular or singular) of the block can be changed
by a proper adjustment. Pixel adjustment of stegoimage is performed to make ๐ ๐ โ ๐ ๐, ๐ โ๐ โ
๐โ๐ and keep image statistic characteristics. Hence, the RS analysis cannot detect the existence
of the stegomessage. This is method was used as the base for current work. But main disadvantage
of proposed method is performing manipulation on the stego and not on the original image. In this
case adjustment matrix (secret key) should be transmitted with every image.
36. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
22
2.10.1 Steps of GSM.
๏ท Perform operations same to regular LSB method: convert cover image to binary numbers
chain.
๏ท In next step perform โCorrelation Factorโ calculation of original image, by equation 2.10.1.
(2.10.1)
๐ถ = โ
(๐ + 1) โ ๐
๐ โ 1
๐
๐
๏ท Where ๐ โ this is number of pixels, and (๐ + 1) and ๐ are indicate current and next pixel
values.
๏ท After the first Correlation Factor is calculated apply non-positive flipping ๐นโ and no-negative
flipping ๐น+ on first pixel of original image binary chain.
๏ท Perform Correlation Factor calculation for these new values, according to formula (6.1)
๏ท Move to the next pixel and perform step 4 again.
๏ท Continue to calculate Correlation Factors till last pixel of the original image.
๏ท Need to choose biggest value of Correlation Factor from all Correlation Factors are calculates
in previous steps and adjust original image according to this value.
For example, original cover image consist of three pixels, calculate Correlation Factors by using
equation 2.10.1. The result is four Correlation factors equation 2.10.2, where ๐ถ0 is Correlation
Factor for original binary chine and ๐ถ1 ๐ถ2 ๐ถ3 values for other pixels.
(2.10.2) ๐ถ0 ๐ถ1 ๐ถ2 ๐ถ3
Choose the biggest value from the result and apply on the original image. After that LSB
manipulation can be performed. See Figure 2.10.1 for diagram of Shen Wang [2] GSM method.
38. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
24
3. Description and system requirements.
3.1 Program interface.
Main Directory โ Steganography, Figure 3.1.1;
Cover Images โ list of images to run though;
Input files โ text messages with different length for simulation;
Output files โ recovered messages;
Stegoimage โ images with embedded message;
New_Encoding+Decoding_RGB.viโ code/decode/extract message and perform RS/GSM with 1D
array representation;
Figure 3.1.1 โSteganographyโ directory view.
39. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
25
3.2 Front panel view.
Figure 3.2.1 displayed program front panel view.
Figure 3.2.1 Detailed program front panel view.
1. Source cover image;
2. Result stegoimage;
3. Cover Histogram and STD statistic window;
4. Stegoimage Histogram and STD statistic window;
5. RS analysis results on Stegoimage;
6. LSB level to be used (up to LSB-4);
7. Start shifting (GSM);
8. Standard deviation evaluation;
9. Snaked array length;
10. Open output result text message;
11. Decode message from stegoimage;
12. Encode message into cover message;
13. Stop button;
14. Start / stop menu;
40. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
26
3.3 Steps for basic encoding and decoding procedure of images.
1. Load โEncoding / Decodingโ dashboard, presented in Figure 3.3.1
Figure 3.3.1 โEncoding / Decodingโ dashboard view
2. Choose number of LSB bits included in embedding message into image, by using โBits
Embeddedโ up/down button.
3. Choose Shifted array length by using โDivision of shifted arrayโ toggle.
4. Pushing โEncodeโ button will start Encoding process. The encoding process - this is
embedding messages from โInput filesโ Figure 3.3.2, directory into images preloaded to
โCover Imagesโ directory, Figure 3.3.3.
41. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
27
Figure 3.3.2 โInput filesโ directory view
Figure 3.3.3โCover Imagesโ directory view
Input text files have different size to learn statistical and visual degradation of images after
message embedding. Encoding process generate seven โStegoโ images, for every encoding image
- product of different message size embedded, in the โStego imagesโ directory, see Figure 3.3.4.
First text file have zero value and required to design RS analysis graphical and statistical
representation
42. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
28
Figure 3.3.4 Resulting โStegoimageโ directory view
Every stegoimage presented in โStegoimagesโ directory have different message size imbedded
inside. Image ending with Data5 have maximum message length and have ending Data have zero
message imbedded respectively.
5. To start the recovery of message from โStegoโ image need to push โDecodeโ button. This
action is open window for choosing specific image for text recovery. This process is
presented in Figure 3.3.5.
43. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
29
Figure 3.3.5 message recovery process.
User sign the required image and press โOKโ button after thereafter.
6. Pressing โOpen outputโ button will open recovered text message, Figure 3.3.6.
Figure 3.3.6 Resulting recovered message view.
The output message size depends of original image size, LSB number and input message size.
44. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
30
3.4 LSB steganography steps.
1. Convert the secret data (message that will be imbedded in to cover image) to binary form.
2. Read cover image and convert decimal form of the cover image to binary form.
3. Replace of Least Significant Bit of image with bits from a message by using LSB encoder.
4. Repeat previous operation many times as needed to imbed the all message in to the image.
5. After manipulating with LSB is done and all message inserted in to the cover image convert
the new binary matrix back to decimal form and to a pixels.
6. The new image which is obtained after this process is named โStego- imageโ.
Figure 3.4.1 LSB steps.
45. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
31
3.5 Digital image definition.
A digital image is binary representation of a two dimensional image and contains a fixed number
of rows and columns of pixels.
For Grey scale images the digital image have pixels representation, every pixel consist of one byte
and bytes have 8 bit term.
An image file is merely a binary file containing a binary representation of the color or light
intensity of each picture element (pixel) comprising in image. Images typically use either 8-bit or
24-bit color. When using 8-bit color, there is a definition of up to 256 colors forming of palette for
this image - each pixel is represented by one 8-bit byte.
The size of an image file, then, is directly related to the number of pixels and the granularity of the
color definition. A typical 640 ร 480 pix image using a palette of 256 colors would require a file
about 307 KB in size (640 ร 480 bytes), whereas a 1024 ร 768 pix high-resolution 24-bit color
image would result in a 2.36 MB file (1024 ร 768 ร 3 bytes).
To avoid long time calculation and provide better statistical data, in this project uses small size
grey scale images compressed by JPEG format. All images are 225 ร 225 size, uses 8-bit color
scheme.
The grey scale image has 3 dimensions. Color depth, also known as bit depth, is either the number
of bits used to indicate the color of a single pixel. For example image is 200 pixels horizontal by
200 pixels vertical. Now we need to know the bit depth. The bit depth of image is 8. File size
calculation is presented by equation number 3.5.1.
3.6 Message embedding mathematical definition.
8 bit Grayscale equivalents to 1 byte per pixel.
For example, for the image size of 7 Kbyte maximum message size can be embedded, by using 1
LSB is 7168 bit. Equation number 3.5.2 displays calculation of bit image capacity.
(3.5.1) ๐ญ๐๐๐ ๐๐๐๐ =
๐๐๐ ร 200 ร 8
๐ ร 1024
=
320000
8192
= 39 ๐พ๐
46. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
32
(3.5.2) 7 ๐พ๐ ร 1024 = 7168 ๐๐ฆ๐ก๐๐
7168 ๐๐ฆ๐ก๐๐ ร 8 ๐๐๐ก = 57344 ๐๐๐ก
We are replacer one bit in every byte. In case we are try to embed message larger than maximum
image capacity the message will be cut, and part of information will be lost.
Table number 3.6.1 demonstrate maximum possible embedded message capacity as function of
LSB level to be used in same image size.
LSB level Maximum message size
1 7 Kb
2 14 Kb
3 21 Kb
4 28 Kb
Table 3.6.1 capacity as function of LSB level.
47. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
33
4. Steps of experiment, discussion and definition.
4.1 Steps definition.
All experiments and research to be performed in LabVIEW environment.
1. Build LabVIEW based steganography system.
2. Perform basic message coding (Cover Image) up to LSB-4 for gray images.
3. Perform basic message recovery (Stegoimages) up to LSB-4 for gray images.
4. Compare visual image degradation.
5. Compare visual degradation through common tools (Histogram, STD).
6. Perform study of coded message saturation (message of different length) vs. recovery and
image degradation per different LSB coding at gray images.
7. Build RS analysis (Fridrich algorithm) routine.
8. Confirm validity of RS analysis on gray images.
9. Implement secure genetic steganography method for RS baseline shifting for LSB-1. (GSM
for RS shifting).
10. Perform basic message recovery with GSM for RS shifting for LSB-1.
11. Perform RS analysis comparison for different message length with GSM for RS shifting
and without, use different โsnakeโ division array image representation.
12. Conclusion.
4.2 Build LabVIEW based steganography system.
Next Figures 4.2.1 and 4.2.2 are displays most important parts of steganography system block
diagram.
49. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
35
๏ท Two loops chosen for taking images and messages permutation.
๏ท First step โ open image.
๏ท Same time we could perform math analysis and RS analysis of the image without message.
๏ท In case if we need to mask RS dependency we could press.
๏ท Next we start to open shortest message, convert it from ASCII to Int U8 in binary code.
๏ท Next step we are interleaving image with message โ for 1LSB image opens to 1D array,
for 2LSB to 2D array and so on. Every image byte consequently getting be changed by
value of message bit (1 bit in byte for 1 LSB 2 bits in byte for 2LSB and so on) โ by this
getting the stegoimage.
๏ท After receiving of stegoimage we run math analysis and RS analysis of the image with
message.
๏ท RS and Math analysis will be displayed.
๏ท In text (inner) loop we are taking same image, but longer message.
๏ท All the process will repeat itself.
๏ท After we use all the messages, we going to the next image in directory and all the process
come back until we will not use all the images and messages.
4.3 Perform basic message coding and recovery. Compare visual image degradation.
Visual Attacks are widely regarded as the simplest form of Steganalysis. A visual attack largely
involves examining the subject file with the naked eye to identify any obvious inconsistencies.
4.3.1 Perform LSB-1 coding.
Prepare six messages of different length to be embed in to images, according to table 4.3.1:
Input Data.txt 0 bytes
Input Data0.txt 699 bytes
Input Data1.txt 2.17 KB (2,225 bytes)
Input Data2.txt 3.95 KB (4,055 bytes)
Input Data3.txt 4.12 KB (4,222 bytes)
Input Data4.txt 4.78 KB (4,896 bytes)
Input Data5.txt 36.0 KB (36,920 bytes)
Table 4.3.1 Used messages sizes.
50. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
36
Also use six different Grey scale images. All images are 225 ร 225 ๐๐๐ฅ๐๐๐ size, uses 8-bit color
scheme. Images have different patterns with different grey scale distributions. One of the images
this is lines pattern of few shadows of grey. This is image provides better visual comparing
capabilities.
Compare Cover (original) image with Stegoimage. Figure 4.3.1 displays visual comparison.
Cover image Stegoimage
Figure 4.3.1 LSB-1 Cover and Stego visual comparison, Input Data5.txt have been
embedded.
No any visual degradation seen, even if zoom in to both images.
In next step try to recover message from Stegoimage. The recovered text file size is 6.21 KB (6,361
bytes), 1177 words text, equivalent to 2.5 pages in WORD format. We can see that by using 1LSB
level only possibly to embed enough amount of information in relatively small image.
Perform similar comparison of special โGrey scaleโ pattern, Figure 4.3.2:
51. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
37
Cover image Stegoimage
Figure 4.3.2 1LSB โGreyโ pattern, Input Data5.txt have been embedded.
The result is same โ no any visual image degradation.
4.3.2 Comparing tool.
Prepare comparing tool, LabVIEW based also, to validate message embedding in to image. The
function of the comparing tool is calculating the difference between bit matrices of Cover image
and bit matrices of Stego Image to determinate percentage of pixels permutations.
The comparing procedure is simple:
1. Open comparing LabVIEW based tool.
Figure 4.3.3 is displayed Comparing tool front panel.
52. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
38
Figure 4.3.3 Compare tool front panel view.
2. Load original โCoverโ and final โStegoโ images, according to Figure 4.3.4:
Figure 4.3.4 Compare tool calculation panel view.
53. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
39
The number in matrix is shows bits difference between two images. Zero, denotes no differences.
For example, take small part of the compare matrix of โGray scaleโ image, presented in Tables
4.3.2 and 4.3.3.
67 70 55 88 96 80 121 98
91 73 56 73 81 42 80 80
108 104 58 108 80 80 59 79
78 100 64 128 105 131 73 78
61 78 93 119 146 126 109 103
72 79 89 120 129 97 113 132
81 81 49 101 79 73 92 109
100 86 48 76 85 76 109 78
Table 4.3.2 Cover image pixel matrix.
66 71 55 88 96 80 121 98
90 72 56 72 81 42 80 81
109 104 59 109 81 80 58 79
79 100 65 129 104 131 72 78
60 79 93 118 146 127 108 102
73 79 88 121 129 96 112 132
80 80 48 100 78 72 92 109
100 86 48 76 84 76 109 78
Table 4.3.3 Stegoimage pixel matrix.
From Table 4.3.4 can see, that not every pixel has been changed.
0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 1
1 0 1 1 1 0 0 0
1 0 1 1 0 0 0 0
0 1 0 0 0 1 0 0
1 0 0 1 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
Table 4.3.4 Difference image pixel matrix.
4.3.3 Perform LSB-2 coding.
Use scheme of experiment is analog to LSB-1 coding. Table 4.3.5 provide us by Cover and
Stego visual comparison results.
54. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
40
Original images
Input Data0.txt is
embedded.
2% pixels have
been modified in
2LSB plane.
Another words 2%
bits manipulation.
Input Data5.txt is
embedded.
100% pixels have
been modified in
1LSB plane.
Another words
12.5% bits
manipulation.
Table 4.3.5 LSB-2 Cover and Stego visual comparison.
โMonkeyโ image do not have any visual degradation, result of image structure.
โGrey patternโ image distinct degradations is appears. By experimental way is decided the minimal
value of 1.5% pixels message, which can be embedded into โGrey patternโ without any visual
effect on pattern. This is because of color and structure the left upper corner of the image.
55. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
41
In case the largest message was embedded into โGrey patternโ changes in high number of pixels
create the effect of โclearโ image without visual degradation.
Recover message from Stego image. The recovered text file size is 12.4 KB (12,737 bytes), 2228
words text, equivalent to 5 pages in WORD format.
4.3.4 Perform LSB-3 coding.
Use scheme of experiment is analog to LSB-1 coding. Tables 4.3.6 and 4.3.7 provides us by
Cover and Stego visual comparison results.
Cover image Stegoimage
Figure 4.3.6 LSB-3 Cover and Stego visual comparison, Input Data5.txt have been
embedded.
56. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
42
Cover image Stegoimage
Figure 4.3.7 LSB-3 โGreyโ pattern visual comparison, Input Data5.txt have been
embedded.
โMonkeyโ image do not have any visual degradation, result of image structure.
โGrey patternโ image distinct degradations is appears.
Recover message from Stego image. The recovered text file size is 18.6 KB (19,106 bytes), 3317
words text, equivalent to 7.5 pages in WORD format.
57. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
43
4.3.5 Perform LSB-4 coding
Use scheme of experiment is analog to LSB-1 coding. Table 4.3.8 provide us by Cover and
Stego visual comparison results.
Original images
Input Data4.txt is
embedded.
8% pixels have
been modified in
4LSB plane.
Another words
4.3% bits
manipulation.
Input Data5.txt is
embedded.
100% pixels have
been modified in
4LSB plane.
Another words
50% bits
manipulation.
Table 4.3.8 LSB-4 Cover and Stego visual comparison.
Both of the images have distinct degradation after LSB-4 manipulation.
58. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
44
Recover message from Stego image. The recovered text file size is 24.8 KB (25,477 bytes), 4468
words text, equivalent to 10 pages in WORD format โ this is a maximum text file size can be
imbedded into 225 ร 225 ๐๐๐ฅ๐๐๐ image by using 4LSB plane.
4.3.6 Intermediates conclusions.
Image color and structure have important value in steganography process. One tone images are
unsuitable for steganography, due to high sensitivity to bits manipulation โ high statistical
dependence between closed pixels. Opposite, images with more small details, wide spectrum of
shadows and with structural margins are ideal candidates for steganography, even high LSB levels
and large messages use.
4.4 Compare visual degradation through common tools (Histogram, STD).
4.4.1 Histogram.
โImage histogram, is a type of histogram that acts as a graphical representation of the tonal
distribution in digital image. It plots the number of pixels for each tonal value. By looking at the
histogram for a specific image a viewer will be abble to judge the entire tonal distribution. The
horisontal axis of the graph represents the tonal variations, while the verticalaxis represents the
number of pixels in that particular tone. The left side of the horisontal axis represents the black
areas, the middle represents medium grey and the right hand side represents pure white areas.
Thus, the istogramm for a very dark image will have the majority of its data points on the left side
and sentre og graph. Conversely, the histogram for a very bright image with few dark areas will
have most of its ata points on the right side and centre of the graph.
So, based on above, it is possible to analising stego image by studing his histogram. LabVIEW
Histogram and STD representation demonstrated in Figure 4.4.1.
Compare Histogram of the cover image with Histogram of the same stegoimage by with different
depth of the LSB impact. In current iteration have been used maximum possible message size for
each LSB replacement level.
59. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
45
Figure 4.4.1 Histogram and STD representation in LabVIEW.
So, based on above, it is possible to analyzing stegoimage by studding his histogram, Figure 4.4.2.
Cover image Cover image Histogram
Figure 4.4.2 Cover image versus Histogram.
In next step perform Histogram comparison of the cover image with Histogram of the same
stegoimage with different depth of the LSB impact (up to LSB-4), Figures 4.4.3 and 4.4.4. In
current iteration have been used maximum possible message size for each LSB replacement level.
Blue line is displays Cover image Histogram and red line represents manipulated image
distribution.
60. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
46
1LSB level 2LSB level
Figure 4.4.3 LSB-1 and LSB-2 level versus Cover image Histogram.
3LSB level 4LSB level
Figure 4.4.4 LSB-3 and LSB-4 level versus Cover image Histogram.
Next Table 4.4.1 can us to see โhowโ stegoimage Histogram is depredate in dependence of input
message volume.
61. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
47
No message inside 4LSB level 699 bytes message
4LSB level 2,225 bytes message 4LSB level 4,055 bytes message
4LSB level 4,222 bytes message 4LSB level 6,201 bytes message
4LSB level 25,477 bytes message
Table 4.4.1 Histogram degradation trough of message enlargement for LSB-4 level.
STD degradation of Stegoimage in dependence of LSB level is presented in Table 4.4.2.
63. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
49
4.4.2 Intermediates conclusions.
In most of the original digital images exists a high matching between the pixels that are placed
next to each other [1], in case any bit manipulation is performed this causes a matching between
pixels is worsens. This is reason for high histogram sensitive for any bits replacements.
But in same time, we can see, that 1LSB level do not dramaticaly impact image histigram, and in
case no clean image histogram presents to cmpare, this is immposible to determinate stegomesage
is exists. In turn, have low sensitivity to LSB permutations and up to 3LSB canโt to provide exact
information according to message presents.
4.5 RS analysis (Fridrich algorithm) routine implementation.
4.5.1 Confirm validity of RS analysis on gray images.
๏ท Algorithm require transform image to 1D array in snake pattern (snake array).
๏ท Apply positive ๐น1 and negative ๐นโ1 flipping on resulting array.
๏ท Evaluate amount of ๐ ๐ and ๐ โ๐ (regular groups) for positive and negative flipping.
๏ท Evaluate amount of ๐ ๐ and ๐โ๐ (singular groups) for positive and negative flipping.
๏ท ๐ 0 ๐๐๐ ๐0 represents Unchanged groups and not used for analysis.
LabVIEW RS analysisand representation demonstrated in Figure 4.5.1, and results plot legend is
presented in Figure 4.5.1.
Figure 4.5.1 LabVIEW RS analysis implementation.
64. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
50
X axis: Message sequence
number. Every message have
different length ( Table number
4.5.1 )
Y axis: Relative number of
regular and singular groups with
masks M and -M
Figure 4.5.1 Plot legend (applicable for all next RS stegoanalisys plots).
The next Table 4.5.1 provides information for Figure 4.5.1 understanding.
Message Embedded Message length(bytes)
0 0
1 699
2 2,225
3 4,055
4 4,222
5 4,896
6 25,477
Table 4.5.1 message number versus message length.
65. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
51
Perform RS analysis according to Fridrich algorithm on our images, use LSB-1 level. RS analyzing
result plot and data are displayed in Figures 4.5.2, 4.5.3 and Tables 4.5.1, 4.5.2.
Just to remember, according to RS algorithm we are expect that ๐ ๐ ๐๐๐ ๐ ๐ pair will strive to
equality and ๐ โ๐ ๐๐๐ ๐โ๐ pair will strive to opposite ways.
Figure 4.5.2 Example 1. RS analysis results on Stegoimage.
Message Message length(bytes) % of Rm % of Sm % of R-m % of S-m
0 0 54.9 45.1 49.8 50.2
1 699 53.8 46.2 50.7 49.3
2 2,225 52.1 47.9 52.1 47.9
3 4,055 51.5 48.5 52.6 47.4
4 4,222 51.4 48.6 52.7 47.3
5 4,896 50.8 49.2 53.2 46.8
6 25,477 49.8 50.2 53.8 46.2
Table 4.5.1 Example 1 ๐น ๐, ๐บ ๐, ๐นโ๐, ๐บโ๐ pairs versus message volume.
70. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
56
Figure 4.5.7 Plot Image 3 ๐น ๐ โ ๐บ ๐ versus bit replaced.
Table 4.5.4 is demonstrate imbedded message volume by ๐ ๐/๐ ๐ differences dependence.
Message Average ๐ ๐ โ ๐ ๐ Average message length in %
0 6.65 0
1 5.8 1.93
2 3.55 6.16
3 2.55 11.22
4 2.4 11.69
5 0.4 17.16
6 -0.3 21.49
Table 4.5.4 Average ๐น ๐/๐บ ๐ differences versus message volume.
4.5.2 Intermediates conclusions.
After statistical data from 30 different images processing, we are can to see, that for Stegoimages
with maximum length message embedded, images in which all pixels have been modified,
๐ ๐ ๐๐๐ ๐ ๐ groups percentage presence very close one to another. The result is very matches to
Fridrich theory. Plot in Figure 4.5.8 confirms our results.
71. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
57
Figure 4.5.8 Plot LSB-1 Average Image3 ๐น ๐ โ ๐บ ๐ versus bit replaced. (Red line represents
normalized linear trend line dependence.).
This is statistical analysis gives us tool to determinate stegamesage presence in the image and
approximate length of presence message. Use presence graphical dependence and know given
image volume we can with high probability determine embedded message existence and
approximate message length. Another words๐ ๐ โ ๐ ๐ differences less 7% indicates LSB
manipulations with high probability.
4.5.3 RS analysis for LSB โ 2, 3, 4 levels.
Use Image 2 for example (reference Figure 4.5.4). Figure 4.5.9 and Table 4.5.5 show results of
Image 2 RS analysis for difference LSB levels.
75. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
61
Figure 4.5.12 Image 2 LSB-4 RS analysis plot.
Next three plots presented in Figures 4.5.13, 4.5.14 and 4.5.15 are average results of LSB-2, LSB-
3 and LSB-4 data analysis.
Figure 4.5.13 Average LSB-2 RS analysis plot. (Red line represents normalized linear trend
line dependence.).
76. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
62
Figure 4.5.14 Average LSB-3 RS analysis plot. (Red line represents normalized linear trend
line dependence.).
Figure 4.5.15 Average LSB-4 RS analysis plot. (Red line represents normalized linear trend
line dependence.).
77. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
63
4.5.4 Intermediate conclusion.
With LSB level recessing and secret imbedded message volume increasing, RS analysis provide
us by more accurate data.
4.6 Implement secure generic steganography method for RS baseline shifting for LSB-1.
4.6.1 Perform basic message encoding and recovery with shifting algorithm.
According to proposed Genetic shifting algorithm all manipulations are performs with Cover
image before secret message embedding is done.
๏ท Represent the cover image in matrix form. Use, for example matrix 8 ร 8, shows in Figure
4.6.1.
198 185 203 195 172 176 177 183
185 197 183 184 177 180 191 194
191 182 185 178 178 184 182 175
178 180 188 184 183 182 188 196
187 182 188 195 185 190 192 187
169 187 191 178 194 185 182 187
183 195 180 176 194 182 194 180
189 194 187 195 187 200 183 189
Figure 4.6.1 Example image ๐ ร ๐ matrix representation.
๏ท Transmit matrix form to โsnakeโ: Run through the line, from left to right, in end of the line
move one step down, come back to left end of the next line and continue the process till down
right matrix corner was reached. The result of this process is displayed in Figure 4.6.2
185 203 195 172 176 177 183 185 โฆ โฆ โฆ 194 187 195 187 200 183
Figure 4.6.2 Example image โsnakeโ representation.
๏ท Next step is presented in Figure 4.6.3. Divide โsnakeโ into non overlapping blocks according
to user needs.
78. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
64
Figure 4.6.3 โsnakeโ dividing.
๏ท Apply RS algorithm on each block and choose worst case mask, another word chose minimal
๐ ๐ โ ๐ ๐ value.
๏ท Move to next block and repeat previous steps.
๏ท Continue the sequence till end of chain.
๏ท Average all masks received from each block โ the results ๐ ๐ โ ๐ ๐ value this final mask that
will be applied on the cover image.
๏ท After adjustment mask is applied, perform standard message emending procedure.
It expected, that applied mask must be reduce the image statistic and message imbedding after that,
must be improve image statistic back and against RS Fridrich analysis.
4.7 Perform RS analysis comparison for different message length with GSM for RS shifting and
without, use different โsnakeโ division array image representation.
4.7.1 13 division snaked array analysis.
Examine images presented on Figure 4.5.4 with 13 division Snaked array and summarize the
received data in Table 4.7.1.
80. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
66
Plot in Figure 4.7.1 provide us by final graphical presentation of average LSB-1 13 division RS
analysis for three test images.
Figure 4.7.1 Average shifted with 13 division LSB-1 RS analysis plot. (Red line represents
normalized linear trend line dependence.).
82. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
68
Plot in Figure 4.7.2 provide us by final graphical presentation of average LSB-1 29 division RS
analysis for three test images.
Figure 4.7.2 Average shifted with 29 division 1LSB RS analysis plot.
84. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
70
Plot in Figure 4.7.3 provide us by final graphical presentation of average LSB-1 51 division RS
analysis for three test images.
Figure 4.7.3 Average shifted with 51 division 1LSB RS analysis plot.
4.7.4 Intermediate conclusion.
Implemented improved Shifting Algorithm provides high capability to upset the RS analysis
statistical data and ability against RS attacks. Distinctly displayed strong move up of ๐ ๐ ๐๐๐ ๐ ๐
pair differences statistics, cause of increasing shifting โshakeโ division.
85. โซืืืืงืืจืื ืืงืโฌ โซืืฉืืโฌ โซืืื ืืกืชโฌ โซืืืืืงืโฌ
71
5. Summary, compare results and conclusions
This project is meet all objectives was targeting in start of the work:
๏ท Working LabVIEW based model for secret message to image encoding/decoding is
implemented.
๏ท Study images visual degradation by โnakedโ eye and Compare visual degradation through
common tools (Histogram, STD).
๏ท Study Fridrich RS analysis algorithm is performed.
๏ท Checked and proved RS algorithm validity for secret message presence detection into Grey
scale images.
๏ท Determined strong dependence of image visual degradation on message length (volume)
and depth of LSB levels manipulations.
๏ท Definition and improvement of existing Shen Wang and al [2] Genetic Shifting algorithm.
๏ท Checked and proved ability of proposed Shifting algorithm to against RS attack.
Image color and structure have important value in steganography process. Monotonic images are
unsuitable for steganography, due to high sensitivity to bits manipulation โ high statistical
dependence between closed pixels. Opposite, images with more small details, wide spectrum of
shadows and with structural margins are ideal candidates for steganography, even high LSB levels
and large messages use.
In most of the original digital images exists a high matching between the pixels that are placed
next to each other [2], in case any bit manipulation is performed this causes a matching between
pixels is worsens. This is reason for high histogram sensitivity for any bits replacements.
But in same time, we can see, that LSB-1 level do not dramatically impact image histogram, and
in case no clean image histogram presents to compare, this is impossible to determinate
stegamesage is exists. By using received statistical data we can with high probability determine
embedded message existence and approximate message length. Another words, ๐ ๐ โ ๐ ๐
differences (under normal conditions) less 7% indicates LSB manipulations with high probability.
Recess of LSB levels manipulations improve RS analysis stability to determine embedded
messages, but in this case visual attack is prefer and easy.