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.
A Blind Steganalysis on JPEG Gray Level Image Based on Statistical Features a...IJERD Editor
This paper presents a blind steganalysis technique to effectively attack the JPEG steganographic
schemes i.e. Jsteg, F5, Outguess and DWT Based. The proposed method exploits the correlations between
block-DCTcoefficients from intra-block and inter-block relation and the statistical moments of characteristic
functions of the test image is selected as features. The features are extracted from the BDCT JPEG 2-array.
Support Vector Machine with cross-validation is implemented for the classification.The proposed scheme gives
improved outcome in attacking.
In this paper, a fruit image data set is used to compare the efficiency and accuracy of two widely used Convolutional Neural Network, namely the ResNet and the DenseNet, for the recognition of 50 different kinds of fruits. In the experiment, the structure of ResNet-34 and DenseNet_BC-121 (with bottleneck layer) are used. The mathematic principle, experiment detail and the experiment result will be explained through comparison.
Selective encryption presents a great solution to optimize time efficiency during encryption
process. In this paper a novel selective encryption scheme based on DCT transform with AES
algorithm is presented. In the DCT method, the basic idea is to decompose the image into 8×8
blocks and these blocks are transformed from the spatial domain to the frequency domain by the
DCT. Then, the DCT coefficients correlated to the lower frequencies of the image block are
encrypted. The proposed cryptosystem is evaluated using various security and statistical
analysis; results show that the proposed algorithm is strong against attacks and suitable for
practical application.
A Blind Steganalysis on JPEG Gray Level Image Based on Statistical Features a...IJERD Editor
This paper presents a blind steganalysis technique to effectively attack the JPEG steganographic
schemes i.e. Jsteg, F5, Outguess and DWT Based. The proposed method exploits the correlations between
block-DCTcoefficients from intra-block and inter-block relation and the statistical moments of characteristic
functions of the test image is selected as features. The features are extracted from the BDCT JPEG 2-array.
Support Vector Machine with cross-validation is implemented for the classification.The proposed scheme gives
improved outcome in attacking.
In this paper, a fruit image data set is used to compare the efficiency and accuracy of two widely used Convolutional Neural Network, namely the ResNet and the DenseNet, for the recognition of 50 different kinds of fruits. In the experiment, the structure of ResNet-34 and DenseNet_BC-121 (with bottleneck layer) are used. The mathematic principle, experiment detail and the experiment result will be explained through comparison.
Selective encryption presents a great solution to optimize time efficiency during encryption
process. In this paper a novel selective encryption scheme based on DCT transform with AES
algorithm is presented. In the DCT method, the basic idea is to decompose the image into 8×8
blocks and these blocks are transformed from the spatial domain to the frequency domain by the
DCT. Then, the DCT coefficients correlated to the lower frequencies of the image block are
encrypted. The proposed cryptosystem is evaluated using various security and statistical
analysis; results show that the proposed algorithm is strong against attacks and suitable for
practical application.
A secure image steganography based on JND model IJECEIAES
Minimizing distortion produced by embedding process is very important to improve the security of hidden message and maintain the high visual quality of stego images. To achieve these objectives, an effective strategy is to perform pixel selection which is well-known as a channel selection rule. In this approach, a pixel associated with the smallest image degradation is chosen to carry secret bits. From these facts, in this paper, a new secure channel selection rule for digital images in spatial domain is designed and proposed. In this new approach, the modified matrix embedding method is utilized as data hiding method because it introduces more than one embedding change to be performed. This enables us to select a suitable pixel to embed message bits with less degradation yielded in a stego-image. In pixel selection of the proposed method, a just noticeable difference value and gradient value of a considering pixel are employed together. The experimental results (which were conducted on 10,000 uncompressed images) indicate that stego images of the proposed approach achieve a higher perceptual quality and security than those of the stego-images created by the previous approaches.
Rough Set based Natural Image Segmentation under Game Theory Frameworkijsrd.com
The Since past few decades, image segmentation has been successfully applied to number of applications. When different image segmentation techniques are applied to an image, they produce different results especially if images are obtained under different conditions and have different attributes. Each technique works on a specific concept, such that it is important to decide as to which image segmentation technique should for a given application domain. On combining the strengths of individual segmentation techniques, the resulting integrated method yields better results thus enhancing the synergy of the individual methods alone. This work improves the segmentation technique of combining results of different methods using the concept of game theory. This is achieved through Nash equilibrium along with various similarity distance measures. Using game theory the problem is divided into modules which are considered as players. The number of modules depends on number of techniques to be integrated. The modules work in parallel and interactive manner. The effectiveness of the technique will be demonstrated by simulation results on different sets of test images.
Data Hiding Method With High Embedding Capacity CharacterCSCJournals
Recently, the data hiding method based on the high embedding capacity by using improved EMD method was proposed by Kuo et al.[6]. They claimed that their scheme can not only hide a great deal of secret data but also keep high safety and good image quality. However, in their scheme, the sender and the receiver must share the synchronous random secret seed before they transmit the stego-image each other. Otherwise, they can not recover the correct secret information from the stego-image. In this paper we propose an improved scheme based on EMD and LSB matching method to overcome the above problem, in other words, the sender does not share the synchronous random secret seed the receiver before the stego-image is transmitted. Observing the experimental results, they show that our proposed scheme acquires high embedding capacity and acceptable stego-image quality.
Protecting Data by Improving Quality of Stego Image based on Enhanced Reduced...IJECEIAES
In this era of internet development, security of information sharing is the main problem faced by human being. Data hiding technique is one of the solutions. However, hiding credential information within a multimedia file such as image reduces its visual quality. Therefore, unauthorized users may suspect the existance of secret data within that image. In the past years, various data hiding algorithms have been developed by researchers to overcome the problem of high distortion of image after data embedding process. Achieving a high quality stego image, however, is still a challenging problem. In this paper, we proposed a new data hiding algorithm based on different expansion. It aims to enhance the quality of stego image for a given payload size. The new algorithm is evaluated on various medical images. Thereafter, the experimental results show that the visual quality is improved; and increasing the embedding capacity leads to more noises. Therefore, a better choice of base point and a reduced difference expansion affect the quality of stego image.
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.
Fuzzy Logic based Edge Detection Method for Image Processing IJECEIAES
Edge detection is the first step in image recognition systems in a digital image processing. An effective way to resolve many information from an image such depth, curves and its surface is by analyzing its edges, because that can elucidate these characteristic when color, texture, shade or light changes slightly. Thiscan lead to misconception image or vision as it based on faulty method. This work presentsa new fuzzy logic method with an implemention. The objective of this method is to improve the edge detection task. The results are comparable to similar techniques in particular for medical images because it does not take the uncertain part into its account.
A new block cipher for image encryption based on multi chaotic systemsTELKOMNIKA JOURNAL
In this paper, a new algorithm for image encryption is proposed based on three chaotic systems which are Chen system,logistic map and two-dimensional (2D) Arnold cat map. First, a permutation scheme is applied to the image, and then shuffled image is partitioned into blocks of pixels. For each block, Chen system is employed for confusion and then logistic map is employed for generating subsititution-box (S-box) to substitute image blocks. The S-box is dynamic, where it is shuffled for each image block using permutation operation. Then, 2D Arnold cat map is used for providing diffusion, after that XORing the result using Chen system to obtain the encrypted image.The high security of proposed algorithm is experimented using histograms, unified average changing intensity (UACI), number of pixels change rate (NPCR), entropy, correlation and keyspace analyses.
Improved block based segmentation for jpeg compressed document imageseSAT Journals
Abstract
Image Compression is to minimize the size in bytes of a graphics file without degrading the quality of the image to an unacceptable
level. The compound image compression normally based on three classification methods that is object based, layer based and block
based. This paper presents a block-based segmentation. for visually lossless compression of scanned documents that contain not only
photographic images but also text and graphic images. In low bit rate applications they suffer with undesirable compression artifacts,
especially for document images. Existing methods can reduce these artifacts by using post processing methods without changing the
encoding process. Some of these post processing methods requires classification of the encoded blocks into different categories.
Keywords- AC energy, Discrete Cosine Transform (DCT), JPEG, K-means clustering, Threshold value
Soft computing is likely to play aprogressively important role in many applications including image enhancement. The paradigm for soft computing is the human mind. The soft computing critique has been particularly strong with fuzzy logic. The fuzzy logic is facts representationas a
rule for management of uncertainty. Inthis paperthe Multi-Dimensional optimized problem is addressed by discussing the optimal thresholding usingfuzzyentropyfor Image enhancement. This technique is compared with bi-level and multi-level thresholding and obtained optimal
thresholding values for different levels of speckle noisy and low contrasted images. The fuzzy entropy method has produced better results compared to bi-level and multi-level thresholding techniques.
An image steganography using improved hyper-chaotic Henon map and fractal Tro...IJECEIAES
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 pixel’s 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.
A NOVEL IMAGE STEGANOGRAPHY APPROACH USING MULTI-LAYERS DCT FEATURES BASED ON...ijma
Steganography is the science of hidden data in the cover image without any updating of the cover image.
The recent research of the steganography is significantly used to hide large amount of information within
an image and/or audio files. This paper proposed a new novel approach for hiding the data of secret image
using Discrete Cosine Transform (DCT) features based on linear Support Vector Machine (SVM)
classifier. The DCT features are used to decrease the image redundant information. Moreover, DCT is
used to embed the secrete message based on the least significant bits of the RGB. Each bit in the cover
image is changed only to the extent that is not seen by the eyes of human. The SVM used as a classifier to
speed up the hiding process via the DCT features. The proposed method is implemented and the results
show significant improvements. In addition, the performance analysis is calculated based on the
parameters MSE, PSNR, NC, processing time, capacity, and robustness.
A Secure & Optimized Data Hiding Technique Using DWT With PSNR ValueIJERA Editor
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
SELECTIVE IMAGE ENCRYPTION USING DCT WITH AES CIPHERcscpconf
Selective encryption presents a great solution to optimize time efficiency during encryption
process. In this paper a novel selective encryption scheme based on DCT transform with AES
algorithm is presented. In the DCT method, the basic idea is to decompose the image into 8×8
blocks and these blocks are transformed from the spatial domain to the frequency domain by the
DCT. Then, the DCT coefficients correlated to the lower frequencies of the image block are
encrypted. The proposed cryptosystem is evaluated using various security and statistical
analysis; results show that the proposed algorithm is strong against attacks and suitable for
practical application.
MAGNETIC RESONANCE BRAIN IMAGE SEGMENTATIONVLSICS Design
Segmentation of tissues and structures from medical images is the first step in many image analysis applications developed for medical diagnosis. With the growing research on medical image segmentation, it is essential to categorize the research outcomes and provide researchers with an overview of the existing segmentation techniques in medical images. In this paper, different image segmentation methods applied on magnetic resonance brain images are reviewed. The selection of methods includes sources from image processing journals, conferences, books, dissertations and thesis. The conceptual details of the methods are explained and mathematical details are avoided for simplicity. Both broad and detailed categorizations of reviewed segmentation techniques are provided. The state of art research is provided with emphasis on developed techniques and image properties used by them. The methods defined are not always mutually independent. Hence, their inter relationships are also stated. Finally, conclusions are drawn summarizing commonly used techniques and their complexities in application.
A secure image steganography based on JND model IJECEIAES
Minimizing distortion produced by embedding process is very important to improve the security of hidden message and maintain the high visual quality of stego images. To achieve these objectives, an effective strategy is to perform pixel selection which is well-known as a channel selection rule. In this approach, a pixel associated with the smallest image degradation is chosen to carry secret bits. From these facts, in this paper, a new secure channel selection rule for digital images in spatial domain is designed and proposed. In this new approach, the modified matrix embedding method is utilized as data hiding method because it introduces more than one embedding change to be performed. This enables us to select a suitable pixel to embed message bits with less degradation yielded in a stego-image. In pixel selection of the proposed method, a just noticeable difference value and gradient value of a considering pixel are employed together. The experimental results (which were conducted on 10,000 uncompressed images) indicate that stego images of the proposed approach achieve a higher perceptual quality and security than those of the stego-images created by the previous approaches.
Rough Set based Natural Image Segmentation under Game Theory Frameworkijsrd.com
The Since past few decades, image segmentation has been successfully applied to number of applications. When different image segmentation techniques are applied to an image, they produce different results especially if images are obtained under different conditions and have different attributes. Each technique works on a specific concept, such that it is important to decide as to which image segmentation technique should for a given application domain. On combining the strengths of individual segmentation techniques, the resulting integrated method yields better results thus enhancing the synergy of the individual methods alone. This work improves the segmentation technique of combining results of different methods using the concept of game theory. This is achieved through Nash equilibrium along with various similarity distance measures. Using game theory the problem is divided into modules which are considered as players. The number of modules depends on number of techniques to be integrated. The modules work in parallel and interactive manner. The effectiveness of the technique will be demonstrated by simulation results on different sets of test images.
Data Hiding Method With High Embedding Capacity CharacterCSCJournals
Recently, the data hiding method based on the high embedding capacity by using improved EMD method was proposed by Kuo et al.[6]. They claimed that their scheme can not only hide a great deal of secret data but also keep high safety and good image quality. However, in their scheme, the sender and the receiver must share the synchronous random secret seed before they transmit the stego-image each other. Otherwise, they can not recover the correct secret information from the stego-image. In this paper we propose an improved scheme based on EMD and LSB matching method to overcome the above problem, in other words, the sender does not share the synchronous random secret seed the receiver before the stego-image is transmitted. Observing the experimental results, they show that our proposed scheme acquires high embedding capacity and acceptable stego-image quality.
Protecting Data by Improving Quality of Stego Image based on Enhanced Reduced...IJECEIAES
In this era of internet development, security of information sharing is the main problem faced by human being. Data hiding technique is one of the solutions. However, hiding credential information within a multimedia file such as image reduces its visual quality. Therefore, unauthorized users may suspect the existance of secret data within that image. In the past years, various data hiding algorithms have been developed by researchers to overcome the problem of high distortion of image after data embedding process. Achieving a high quality stego image, however, is still a challenging problem. In this paper, we proposed a new data hiding algorithm based on different expansion. It aims to enhance the quality of stego image for a given payload size. The new algorithm is evaluated on various medical images. Thereafter, the experimental results show that the visual quality is improved; and increasing the embedding capacity leads to more noises. Therefore, a better choice of base point and a reduced difference expansion affect the quality of stego image.
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.
Fuzzy Logic based Edge Detection Method for Image Processing IJECEIAES
Edge detection is the first step in image recognition systems in a digital image processing. An effective way to resolve many information from an image such depth, curves and its surface is by analyzing its edges, because that can elucidate these characteristic when color, texture, shade or light changes slightly. Thiscan lead to misconception image or vision as it based on faulty method. This work presentsa new fuzzy logic method with an implemention. The objective of this method is to improve the edge detection task. The results are comparable to similar techniques in particular for medical images because it does not take the uncertain part into its account.
A new block cipher for image encryption based on multi chaotic systemsTELKOMNIKA JOURNAL
In this paper, a new algorithm for image encryption is proposed based on three chaotic systems which are Chen system,logistic map and two-dimensional (2D) Arnold cat map. First, a permutation scheme is applied to the image, and then shuffled image is partitioned into blocks of pixels. For each block, Chen system is employed for confusion and then logistic map is employed for generating subsititution-box (S-box) to substitute image blocks. The S-box is dynamic, where it is shuffled for each image block using permutation operation. Then, 2D Arnold cat map is used for providing diffusion, after that XORing the result using Chen system to obtain the encrypted image.The high security of proposed algorithm is experimented using histograms, unified average changing intensity (UACI), number of pixels change rate (NPCR), entropy, correlation and keyspace analyses.
Improved block based segmentation for jpeg compressed document imageseSAT Journals
Abstract
Image Compression is to minimize the size in bytes of a graphics file without degrading the quality of the image to an unacceptable
level. The compound image compression normally based on three classification methods that is object based, layer based and block
based. This paper presents a block-based segmentation. for visually lossless compression of scanned documents that contain not only
photographic images but also text and graphic images. In low bit rate applications they suffer with undesirable compression artifacts,
especially for document images. Existing methods can reduce these artifacts by using post processing methods without changing the
encoding process. Some of these post processing methods requires classification of the encoded blocks into different categories.
Keywords- AC energy, Discrete Cosine Transform (DCT), JPEG, K-means clustering, Threshold value
Soft computing is likely to play aprogressively important role in many applications including image enhancement. The paradigm for soft computing is the human mind. The soft computing critique has been particularly strong with fuzzy logic. The fuzzy logic is facts representationas a
rule for management of uncertainty. Inthis paperthe Multi-Dimensional optimized problem is addressed by discussing the optimal thresholding usingfuzzyentropyfor Image enhancement. This technique is compared with bi-level and multi-level thresholding and obtained optimal
thresholding values for different levels of speckle noisy and low contrasted images. The fuzzy entropy method has produced better results compared to bi-level and multi-level thresholding techniques.
An image steganography using improved hyper-chaotic Henon map and fractal Tro...IJECEIAES
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 pixel’s 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.
A NOVEL IMAGE STEGANOGRAPHY APPROACH USING MULTI-LAYERS DCT FEATURES BASED ON...ijma
Steganography is the science of hidden data in the cover image without any updating of the cover image.
The recent research of the steganography is significantly used to hide large amount of information within
an image and/or audio files. This paper proposed a new novel approach for hiding the data of secret image
using Discrete Cosine Transform (DCT) features based on linear Support Vector Machine (SVM)
classifier. The DCT features are used to decrease the image redundant information. Moreover, DCT is
used to embed the secrete message based on the least significant bits of the RGB. Each bit in the cover
image is changed only to the extent that is not seen by the eyes of human. The SVM used as a classifier to
speed up the hiding process via the DCT features. The proposed method is implemented and the results
show significant improvements. In addition, the performance analysis is calculated based on the
parameters MSE, PSNR, NC, processing time, capacity, and robustness.
A Secure & Optimized Data Hiding Technique Using DWT With PSNR ValueIJERA Editor
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
SELECTIVE IMAGE ENCRYPTION USING DCT WITH AES CIPHERcscpconf
Selective encryption presents a great solution to optimize time efficiency during encryption
process. In this paper a novel selective encryption scheme based on DCT transform with AES
algorithm is presented. In the DCT method, the basic idea is to decompose the image into 8×8
blocks and these blocks are transformed from the spatial domain to the frequency domain by the
DCT. Then, the DCT coefficients correlated to the lower frequencies of the image block are
encrypted. The proposed cryptosystem is evaluated using various security and statistical
analysis; results show that the proposed algorithm is strong against attacks and suitable for
practical application.
MAGNETIC RESONANCE BRAIN IMAGE SEGMENTATIONVLSICS Design
Segmentation of tissues and structures from medical images is the first step in many image analysis applications developed for medical diagnosis. With the growing research on medical image segmentation, it is essential to categorize the research outcomes and provide researchers with an overview of the existing segmentation techniques in medical images. In this paper, different image segmentation methods applied on magnetic resonance brain images are reviewed. The selection of methods includes sources from image processing journals, conferences, books, dissertations and thesis. The conceptual details of the methods are explained and mathematical details are avoided for simplicity. Both broad and detailed categorizations of reviewed segmentation techniques are provided. The state of art research is provided with emphasis on developed techniques and image properties used by them. The methods defined are not always mutually independent. Hence, their inter relationships are also stated. Finally, conclusions are drawn summarizing commonly used techniques and their complexities in application.
We present a new image compression method to improve visual perception of the decompressed images and achieve higher image compression ratio. This method balances between the compression rate and image quality by compressing the essential parts of the image-edges. The key subject/edge is of more significance than background/non-edge image. Taking into consideration the value of image components and the effect of smoothness in image compression, this method classifies the image components as edge or non-edge. Low-quality lossy compression is applied to non-edge components whereas high-quality lossy compression is applied to edge components. Outcomes show that our suggested method is efficient in terms of compression ratio, bits per-pixel and peak signal to noise ratio.
Stegnography of high embedding efficiency by using an extended matrix encodin...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Stegnography of high embedding efficiency by using an extended matrix encodin...eSAT Journals
Abstract F5 Steganography is way totally different from most of LSB replacement or matching steganographic schemes, as a result of matrix encryption is used to extend embedding potency while reducing the amount of necessary changes. By victimisation this theme, the hidden message inserted into carrier media observably is transferred via a safer imperceptible channel. The embedding domain is that the quantitative DCT coefficients of JPEG image, which makes the theme, be proof against visual attack and statistical attack from the steganalyst. Based on this effective theme, An extended matrix encoding algorithm is planned to improve the performance further in this paper. The embedding potency and embedding rate get accrued to large extent by changing the hash function in matrix encryption and changing the coding mode. Eventually, the experimental results demonstrate the extended algorithm is more advanced and efficient to the classic F5 Steganography.
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.
Enhancement of genetic image watermarking robust against cropping attackijfcstjournal
The enhancement of image watermarking algorithm robust against particular attack by using genetic
algorithm is presented here. There is a trade-off between imperceptibility and robustness in image
watermarking. To preserve both of these characteristics in digital image watermarking in a logical value,
the genetic algorithm is used. Some factors were introduced for providing robustness of image
watermarking against cropping attack such as the Centre of Interest Proximity Factor (CIPF), the
Complexity Factor (CF) and the Priority Coefficient (PC).
A new image steganography algorithm basedIJNSA Journal
In recent years, the rapid growth of information technology and digital communication has become very
important to secure information transmission between the sender and receiver. Therefore, steganography
introduces strongly to hide information and to communicate a secret data in an appropriate multimedia
carrier, e.g., image, audio and video files. In this paper, a new algorithm for image steganography has
been proposed to hide a large amount of secret data presented by secret color image. This algorithm is
based on different size image segmentations (DSIS) and modified least significant bits (MLSB), where the
DSIS algorithm has been applied to embed a secret image randomly instead of sequentially; this approach
has been applied before embedding process. The number of bit to be replaced at each byte is non uniform,
it bases on byte characteristics by constructing an effective hypothesis. The simulation results justify that
the proposed approach is employed efficiently and satisfied high imperceptible with high payload capacity
reached to four bits per byte.
A NEW IMAGE STEGANOGRAPHY ALGORITHM BASED ON MLSB METHOD WITH RANDOM PIXELS S...IJNSA Journal
In recent years, the rapid growth of information technology and digital communication has become very important to secure information transmission between the sender and receiver. Therefore, steganography introduces strongly to hide information and to communicate a secret data in an appropriate multimedia carrier, e.g., image, audio and video files. In this paper, a new algorithm for image steganography has been proposed to hide a large amount of secret data presented by secret color image. This algorithm is based on different size image segmentations (DSIS) and modified least significant bits (MLSB), where the DSIS algorithm has been applied to embed a secret image randomly instead of sequentially; this approach has been applied before embedding process. The number of bit to be replaced at each byte is non uniform, it bases on byte characteristics by constructing an effective hypothesis. The simulation results justify that the proposed approach is employed efficiently and satisfied high imperceptible with high payload capacity reached to four bits per byte.
Similar to INTRA BLOCK AND INTER BLOCK NEIGHBORING JOINT DENSITY BASED APPROACH FOR JPEG STEGANALYSIS (20)
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
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/
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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INTRA BLOCK AND INTER BLOCK NEIGHBORING JOINT DENSITY BASED APPROACH FOR JPEG STEGANALYSIS
1. International Journal on Soft Computing (IJSC) Vol.3, No.2, May 2012
DOI: 10.5121/ijsc.2012.3206 63
INTRA BLOCK AND INTER BLOCK NEIGHBORING
JOINT DENSITY BASED APPROACH FOR JPEG
STEGANALYSIS
Arun R1
, Nithin Ravi S2
and Thiruppathi K3
1,2,3
TIFAC CORE in Cyber Security, Amrita Vishwa Vidhyapeetham, Coimbatore
1
csarunr@gmail.com , 2
nithinravi535@gmail.com, 3
ktirupathi1988@gmail.com
ABSTRACT
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.
KEYWORDS
Steganography, Steganalysis, DCT, PVD, MB1, MB2,F5, JPHS, Steghide.
1. INTRODUCTION
Steganography is the art of passing information through apparently innocent files in a manner that
the very existence of the message is unknown. Steganalysis is the art of discovering hidden data
in cover objects. As in cryptanalysis, we assume that the steganographic method is publicly
known with the exception of a secret key. The method is secure if the stego-images do not contain
any detectable artifacts due to message embedding. In other words, the set of stego-images should
have the same statistical properties as the set of cover-images [1]. If there exists an algorithm that
can guess whether or not a given image contains a secret message with a success rate better than
random guessing, the steganographic system is considered broken.
Steganalysis is the art and science to detect whether a given medium has hidden message in it.
Also steganalysis can serve as an effective way to judge the security performance of
steganographic techniques. Steganalysis can be mainly classified into two-Blind Steganalysis and
Targeted Steganalysis [2]. Targeted Steganalysis are designed for a particular steganographic
algorithm. Blind Steganalysis are schemes which are independent of any specific embedding
technique are used to alleviate the deficiency of targeted analyzers by removing their dependency
on the behavior of individual embedding techniques. To remove their dependency a set of
distinguishing statistics that are sensitive to a wide variety of embedding operations are
determined and collected. These statistics are taken from both cover and stego images and are
2. International Journal on Soft Computing (IJSC) Vol.3, No.2, May 2012
64
used to train a classifier, which is latter used to distinguish between cover and stego images.
Hence, the dependency on a specific embedder is removed using these statistics.
Universal steganalysis is composed of feature extraction and feature classification. In feature
extraction, a set of distinguishing statistics are obtained from a data set of images. In feature
classification the obtained distinguishing statistics from both stego and cover images are used to
train a classifier and finally the trained classifier is used to classify an input image as either being
a stego image which carrying a hidden data or a clear image. The above statistics are obtained by
observing general image features that exhibit strong variation under embedding.
In [3] Fridrich proposed a universal steganalysis scheme specially designed for JPEG
steganography. A set of 23 distinguishing features from the block discrete cosine
transform(BDCT) domain and spatial domain is proposed. In [4] Shie et al. presented a new
universal steganalysis secheme in which all the 324 features are calculated directly from the
quantized DCT coefficients. The Markov process is applied to modeling the difference of JPEG
2D arrays along horizontal, vertical and diagonal directions so as to utilize the second order
statistics for steganalysis. In [4] Fu et al. presented another universal JPEG steganalysis scheme
totally based on quantized DCT coefficients which extracted 200 features. The Markov empirical
transition matrices are used to exploit the magnitude correlations between BDCT coefficients in
both intra and inter block. By extending the feature set in [3] and applying calibration to the
Markov features a new JPEG steganalysis scheme is developed by Penvy et al. [5] with 274
features. In [6] Qingzhong et al. proposed a new approach based on feature mining on the discrete
cosine transform (DCT) and machine learning for steganalysis of JPEG images. The neighboring
joint density features on both intra and inter block are extracted from the DCT coefficient array
and the absolute coefficient array.
In this paper we propose a new steganalysis scheme to attack some latest developed JPEG
steganographic schemes. In this features from neighboring joint probabilities of the DCT
coefficients on intra and inter block are extracted. The paper is organized as follows. In the next
section the statistical models and information hiding is explained. In the section 3 the neighboring
joint density features on both intra and inter block features are explained. In section 4 proposed
method is explained followed by the experimental results of the proposed steganalysis method in
section 5. And paper is concluded in section 6.
2. STATISTICAL MODELS AND INFORMATION HIDING
A model Probability density function (PDF) can characterize the statistical behavior of a signal.
For multimedia signals, the Generalized Gaussian distribution (GGD) is often used. GGD can be
applied to model the distribution of Discrete Cosine Transform (DCT) coefficients, the wavelet
transform coefficients, pixels difference, etc. Thus, it might be used in video and geometry
compression, watermarking, etc. GGD is also known in economy as Generalized Error
Distribution (GED). Probability density function of the continuous random variable of GGD takes
the form [5]
p(x; α, β) =
where ᴦ(z) =
ᴦ(z) is the Gamma function, scale parameter α models the width of the PDF peak and shape
parameter β models the shape of the distribution. Their exists the dependency between the
3. International Journal on Soft Computing (IJSC) Vol.3, No.2, May 2012
65
compressed DCT coefficients and their neighbors. The information hiding will modify the
neighboring joint density of the DCT coefficients. Let the left or upper adjacent DCT coefficient
be denoted by random vector X1 and the right or lower adjacent DCT coefficients be denoted by
random vector X2; let X = (X1,X2). When hidden data are embedded in the compressed DCT
domain in JPEG images by using any steganographic algorithms the DCT neighboring joint
probability density coefficients is affected and these changes will be helpful for steganalysis.
The change in joint density due to message embedding is shown by the following example.
Figure 1 shows the cover image, F5 embedded image and the steghide embedded image. Figure 2
shows the compressed DCT neighboring joint density probability, the neighboring joint density
distribution of a F5 steganogram carrying some hidden data and the neighboring joint density
distribution of a steghide steganogram carrying some hidden data. From figure 2 it is clear that
the neighboring joint density is approximately symmetric about the origin. Figure 3 shows the
difference of neighboring joint density of F5 steganogram and steghide steganogram with cover
image. So by embedding message the neighboring joint density get modified.
Figure 1. Cover Image, F5 Steganogram and Steghide Steganogram in (a), (b) and (c).
Figure 2. Compressed DCT neighboring joint density probability, the neighboring joint density
distribution of a F5 steganogram and the neighboring joint density distribution of a steghide
steganogram in (d), (e) and (f).
4. International Journal on Soft Computing (IJSC) Vol.3, No.2, May 2012
66
Figure 3. Difference of neighboring joint density of F5 steganogram and steghide steganogram
with cover image in (g) and (h).
3. NEIGHBORING JOINT DENSITY FEATURES
The dependency between compressed DCT coefficients and their neighbours is explained in [5].
The information hiding will modify the neighboring joint density. When messages are embedded
in the compressed DCT domain in JPEG images by any of the steganographic algorithms the
DCT neighboring joint density probability density is affected which will gives a way for
steganalysis. The modification of joint densities as a result of data embedding is shown in [6].
3.1. Feature Extraction
The neighboring joint features are extracted on intra-block and inter-block from the DCT
coefficient array respectively.
From the DCT coefficient array the neighboring joint density of intra block and inter block
features are extracted as shown below. Let F denote the compressed DCT coefficient array of a
JPEG image, consisting of M × N blocks (i = 1, 2.. M; j = 1, 2.. N). Each block has a size of 8
× 8. The intra-block neighboring joint density matrix on horizontal direction and the matrix
on vertical direction are constructed as
where stands for the compressed DCT coefficient located at the row and the column
in the block ; δ = 1 if its arguments are satisfied, otherwise, δ = 0; x and y are integers. For
computational efficiency, we define as the neighboring joint density features on intra-block,
calculated as follows:
5. International Journal on Soft Computing (IJSC) Vol.3, No.2, May 2012
67
Here the values of x and y are in the range of [−6, +6], so has 169 features. Similarly the inter-
block neighboring joint density matrix on horizontal direction and the matrix on vertical
direction are constructed as follows:
We define as the neighboring joint density features on inter-block, calculated as follows:
Similarly, the values of x and y are in the range of [−6, +6] and has 169 features
Hence we extract 169 features from both neighboring joint density of intra and inter block. So
totally 338 features are extracted from neighboring joint density DCT array.
4. FEATURE BASED JPEG STEGANALYSIS USING NEIGHBORING JOINT
DENSITY BASED FEATURES
By combining the features obtained from the neighboring joint densities, a new feature based
JPEG steganalysis scheme is proposed. From the neighboring joint density of intra block 169
features and from neighboring joint density of inter block another 169 features are extracted and
totally 338 distinguishable statistics are extracted for better steganalysis.
After the features are extracted from both stego and clear images it will be given to SVM like
binary classifier for training. After the training is completed the features from test images are
given for classification.
5. EXPERIMENTAL RESULTS
Five hundred and eighty five natural images were collected and these color images span a range
of indoor and outdoor scenes and typically are 256 x 256 pixels in size. Another five hundred and
eighty five stego images were generated by embedding messages of various sizes into the cover
images. The payload corresponding to 100%, 75%, 50%, 25%, 20% and 10% of total cover
capacity. The total cover capacity is defined to be the maximum size of a message that can be
embedded by the embedding algorithm. Messages were embedded using F5, Model Based
6. International Journal on Soft Computing (IJSC) Vol.3, No.2, May 2012
68
Steganography (MB1 and MB2), Pixel Value Differencing (PVD), JPHS and Steghide
algorithms.
Individually each feature set is used for steganalysis and the combined one is also used.
Neighboring joint density of intra block, Neighboring joint density of inter block and Combined
Neighboring joint density of intra and inter block are extracted and used for steganalysis of all the
above mentioned stego algorithms. Features will be extracted from each images yielding to a 169,
169 and 338 feature vector respectively. These features are used to train the linear SVM classifier
separately. The performance of the classifier was tested using 250 test images which contain 25
cover and 25 stego images for F5, Model Based Steganography (MB1 and MB2 each), Pixel
Value Differencing (PVD) respectively, 15 cover and 15 stego images for JPHS and 10 cover and
10 stego images for Steghide algorithm.
Table 1 shows the classification accuracy of the neighboring joint density of intra block feature
based steganalysis, Table 2 shows the classification accuracy of the neighboring joint density of
inter block feature based steganalysis and Table 3 shows the classification accuracy of the
neighboring joint density of intra block and inter block feature based steganalysis. All the
individual features and the combined features are used for steganalysis. The neighboring joint
density of intra and inter block features will combined and used for feature based steaganalysis
which will give better result when compared to other feature based steganalysis. Different
payload can be embedded and used for steganalysis. While for lower payload also this feature
based steganalysis gives better results than other features. Strong steganographic algorithms like
steghide and JPHS will also gives better result in these features than other.
Table 1. Classification accuracy of Neighboring joint density of intra block features
Algorithms Payload Classification Accuracy (%)
PVD 25-50-75-100 100
F5 25-50-75-100 100
MB1 25-50-75-100 100
MB2 25-50-75-100 88
JPHS 10-20 70
Steghide 10-20 95
Table 2. Classification accuracy of Neighboring joint density of inter block features
Algorithms Payload Classification Accuracy (%)
PVD 25-50-75-100 96
F5 25-50-75-100 94
MB1 25-50-75-100 100
MB2 25-50-75-100 96
JPHS 10-20 63.33
Steghide 10-20 90
7. International Journal on Soft Computing (IJSC) Vol.3, No.2, May 2012
69
Table 3. Classification accuracy of Combined neighboring joint density of intra and inter block
features
Algorithms Payload Classification Accuracy (%)
PVD 25-50-75-100 98
F5 25-50-75-100 100
MB1 25-50-75-100 100
MB2 25-50-75-100 98
JPHS 10-20 73.33
Steghide 10-20 100
6. CONCLUSIONS AND FUTURE WORKS
From the above experiments we concluded that the combination of all neighboring joint density
features used steganalysis will gives better result when compared with other features. For strong
steganographic algorithms like steghide which uses graph theoretical approach for embedding this
feature based steganalysis performs better detection. The results of this paper demonstrate that,
with judicious and sophisticated feature mining, it is possible to simultaneously achieve faster
detection time, and higher detection performance for JPEG image steganography.
The future work is to do the feature selection by ranking the feature vector using some ranking
algorithms and the optimum features has to be discovered out. These optimum features can
reduce the miss classification. Feature selection can also be applied using projection pursuit
algorithms to improve the detection efficiency. More embedding schemes can be used to analyse
the features efficiency.
REFERENCES
[1] Gireesh Kumar T, Jithin R, Deepa D Shankar, (2010) “ Feature Based Steganalysis using Wavelet
Decomposition and Magnitude Statistics”, International Conference on Advances in Computer
Engineering.
[2] Deepa.D.Shankar, Gireeshkumar T, ( 2010) “Feature Based Classification System for Image
Steganalysis”, International Conference on Computer Communications and Networks(CCN-10).
[3] J. Fridrich, (2004) “ Feature-based steganalysis for JPEG images and its implications for future design
of steganographic schemes”, Information Hiding, 6th
International Workshop,LNCS 3200 ,pages 67-
81.
[4] D.Fu, Y.Q.Shi and D.Zou, (2006) “JPEG steganalysis using empirical transition matrix in block DCT
domain”, International Workshop on Multimedia Signal Processing , Victoria, BC, Canada.
[5] T.Pevny and J.Fridrich, (2007) “ Merging Markov and DCT features for multi-class JPEG
steganalysis”, Proceedings of SPIE Electronic Imaging, Security, Steganography and Watermarking
of Multimedia Contents IX, volume 6505, pages 650503-1 to 650503-13
[6] Qingzhong Liu, Andrew H Sung, Mengyu Qiao, (2011) “ Neighboring Joint Density-Based JPEG
Steganalysis”, ACM Transactions on Intelligent Systems and Technology. volume 2, No 2, Article 16.
8. International Journal on Soft Computing (IJSC) Vol.3, No.2, May 2012
70
Authors
Arun R. received B.Tech degree in Computer Science and Engineering from St
Josephs College of Engineering and Technology Palai, Mahatma Gandhi University,
Kerala in 2009. He is pursuing his M.Tech in Cyber Security at Amrita Vishwa
Vidhyapeetham University, Coimbatore. His research interests are Steganography,
Cryptography and Image Processing
Nithin Ravi S. received B.Tech degree in Computer Science and Engineering from
Government Engineering College Wayanad, Kannur University, Kerala in 2009. He is
pursuing his M.Tech in Cyber Security at Amrita Vishwa Vidhyapeetham University,
Coimbatore. His research interests are Steganography and Image Processing
Thiruppathi K. received B.Tech degree in Computer Science and Engineering from
Velammal Engineering College Chennai, Anna University, Tmil Nadu in 2010. He is
pursuing his M.Tech in Cyber Security at Amrita Vishwa Vidhyapeetham University,
Coimbatore. His research interests are Steganography and Image Processing