In this paper, chaotic block image scheme using large key space and message digest algorithm. Cat map intended for confusion and 2D-Sine Tent Composite map (2D-STCM) key generator intended for diffusion. Confusion is implemented by 2D Cat map with arbitrary block size. In the first tendency, 2D cat map use for local shuffling of indexes inside blocks, while in the second tendency, 2D cat map used for global shuffling of whole image indexes. The designed algorithm executes two confusions and one diffusion in each iteration. To increase the security level, the message digestion algorithm is used as a fingerprint for the plain image that creates the initial value of the key. After that 2D-STCM generates a large key stream. Diffusion implementation takes place by XOR operation; between a key stream and confused image. Experimental results, show that security level increases due to integration of confusion and diffusion. On the other side large key space and the high sensitivity of secret keys have been given a guarantee for the performance of the security. Performance measures reach to the top value among those in the similar researches. To verify the obtained results, authors implemented inverse chaos. All the tests are processed by MATLAB 2015a.
IRJET- LS Chaotic based Image Encryption System Via Permutation ModelsIRJET Journal
ย
This document proposes an image encryption system using logistic sine map and permutation models. The system works as follows:
1. A plain image is converted to grayscale and decomposed into 8 bit planes.
2. Each bit plane is randomly scrambled.
3. A logistic sine map is used to generate a key to partially encrypt each bit plane.
4. The bits planes are then permuted to obtain the final encrypted image. Logistic sine maps are well-suited for this approach due to their sensitivity to initial parameter values and ability to generate seemingly random outputs. The system aims to increase security by efficiently scrambling and permuting the bit plane values of the input image.
The document proposes and evaluates a new digital image security scheme that uses Residue Number System (RNS) encoding/decoding and a modified Arnold transform algorithm. Key points:
- The encryption process encodes the plain image into residual images using RNS, then encrypts them by applying the modified Arnold transform multiple times.
- The decryption process decrypts the cipher image by applying the inverse Arnold transform, then decodes the residual images back into the plain image using RNS and the Chinese Remainder Theorem.
- Experimental results on images of different sizes show the scheme can encrypt/decrypt without information loss. Security analysis indicates resistance to statistical attacks like histograms and strong sensitivity to encryption keys.
Image confusion and diffusion based on multi-chaotic system and mix-columnjournalBEEI
ย
In this paper, a new image encryption algorithm based on chaotic cryptography was proposed. The proposed scheme was based on multiple stages of confusion and diffusion. The diffusion process was implemented twice, first, by permuting the pixels of the plain image by using an Arnold cat map and, the second time by permuting the plain image pixels via the proposed permutation algorithm. The confusion process was performed many times, by performing the XOR operation between the two resulted from permuted images, subtracted a random value from all pixels of the image, as well as by implementing the mix column on the resulted image, and by used the Lorenz key to obtain the encrypted image. The security analysis tests that used to exam the results of this encryption method were information entropy, key space analysis, correlation, histogram analysis UACI, and NPCR have shown that the suggested algorithm has been resistant against different types of attacks.
Chaos Image Encryption using Pixel shuffling cscpconf
ย
This document proposes a chaos-based image encryption algorithm using pixel shuffling. It uses elements from a chaotic map like the Henon map or Lorentz map to shuffle the pixel positions of an image. The chaotic elements are divided into blocks corresponding to the RGB channels. Pixel positions are reordered according to the sorted indices of each block. Encryption scrambles the pixel positions, while decryption restores the original positions using the same chaotic map. Experimental results on brain and Lena images show the encrypted images have very low correlation with the originals. Slight key changes also result in completely different decryptions, demonstrating key sensitivity of the algorithm.
This document presents a digital image encryption technique based on a compound sine and cosine chaotic map. The map is used to generate random bits for encryption. The image is first converted to binary and separated into bit planes. Each bit plane is encrypted using XOR operations with bits from the chaotic map. Keys are generated from input ASCII codes which set initial conditions and parameters for the chaotic map, increasing security. Experiments were conducted encrypting color images in MATLAB to evaluate the encryption performance.
Design a New Image Encryption using Fuzzy Integral Permutation with Coupled C...IJORCS
ย
This document proposes a new image encryption algorithm combining DNA sequence addition and coupled chaotic maps. The algorithm has two parts: 1) A DNA sequence matrix is obtained by encoding image pixels and divided into blocks that are added using Sugeno fuzzy integral, 2) The modified color components are encrypted using coupled two-dimensional piecewise nonlinear chaotic maps to strengthen security. Experimental results on image databases show the algorithm effectively protects digital image security over the internet.
HYBRIDIZATION OF DCT BASED STEGANOGRAPHY AND RANDOM GRIDSIJNSA Journal
ย
The document discusses a hybrid approach to steganography and visual cryptography for improved data security. It proposes combining principles of steganography, which hides data in a cover media, and visual cryptography, which encrypts images in a way that can be decrypted by human vision without algorithms. Specifically, it describes generating two random grids from a secret image that reveal the image when overlaid but hide it individually. The random grids are created by inverting or substituting pixels based on the secret image. This hybrid approach aims to provide stronger security than either technique alone by incorporating advantages of both.
IRJET-An Effective Strategy for Defense & Medical Pictures Security by Singul...IRJET Journal
ย
The document proposes an effective strategy for encrypting medical and defense images using singular value decomposition (SVD) and fractional Fourier transform (FrFT). It discusses generating shares of an image by applying FrFT to the SVD components, specifically the S matrix of singular values. Multiple shares are created using a sharing matrix. The strategy aims to securely transmit images over unreliable networks. Quantitative analysis shows the approach encrypts images quickly, with high sensitivity to changes and resistance to differential attacks, making it effective for encrypting sensitive images.
IRJET- LS Chaotic based Image Encryption System Via Permutation ModelsIRJET Journal
ย
This document proposes an image encryption system using logistic sine map and permutation models. The system works as follows:
1. A plain image is converted to grayscale and decomposed into 8 bit planes.
2. Each bit plane is randomly scrambled.
3. A logistic sine map is used to generate a key to partially encrypt each bit plane.
4. The bits planes are then permuted to obtain the final encrypted image. Logistic sine maps are well-suited for this approach due to their sensitivity to initial parameter values and ability to generate seemingly random outputs. The system aims to increase security by efficiently scrambling and permuting the bit plane values of the input image.
The document proposes and evaluates a new digital image security scheme that uses Residue Number System (RNS) encoding/decoding and a modified Arnold transform algorithm. Key points:
- The encryption process encodes the plain image into residual images using RNS, then encrypts them by applying the modified Arnold transform multiple times.
- The decryption process decrypts the cipher image by applying the inverse Arnold transform, then decodes the residual images back into the plain image using RNS and the Chinese Remainder Theorem.
- Experimental results on images of different sizes show the scheme can encrypt/decrypt without information loss. Security analysis indicates resistance to statistical attacks like histograms and strong sensitivity to encryption keys.
Image confusion and diffusion based on multi-chaotic system and mix-columnjournalBEEI
ย
In this paper, a new image encryption algorithm based on chaotic cryptography was proposed. The proposed scheme was based on multiple stages of confusion and diffusion. The diffusion process was implemented twice, first, by permuting the pixels of the plain image by using an Arnold cat map and, the second time by permuting the plain image pixels via the proposed permutation algorithm. The confusion process was performed many times, by performing the XOR operation between the two resulted from permuted images, subtracted a random value from all pixels of the image, as well as by implementing the mix column on the resulted image, and by used the Lorenz key to obtain the encrypted image. The security analysis tests that used to exam the results of this encryption method were information entropy, key space analysis, correlation, histogram analysis UACI, and NPCR have shown that the suggested algorithm has been resistant against different types of attacks.
Chaos Image Encryption using Pixel shuffling cscpconf
ย
This document proposes a chaos-based image encryption algorithm using pixel shuffling. It uses elements from a chaotic map like the Henon map or Lorentz map to shuffle the pixel positions of an image. The chaotic elements are divided into blocks corresponding to the RGB channels. Pixel positions are reordered according to the sorted indices of each block. Encryption scrambles the pixel positions, while decryption restores the original positions using the same chaotic map. Experimental results on brain and Lena images show the encrypted images have very low correlation with the originals. Slight key changes also result in completely different decryptions, demonstrating key sensitivity of the algorithm.
This document presents a digital image encryption technique based on a compound sine and cosine chaotic map. The map is used to generate random bits for encryption. The image is first converted to binary and separated into bit planes. Each bit plane is encrypted using XOR operations with bits from the chaotic map. Keys are generated from input ASCII codes which set initial conditions and parameters for the chaotic map, increasing security. Experiments were conducted encrypting color images in MATLAB to evaluate the encryption performance.
Design a New Image Encryption using Fuzzy Integral Permutation with Coupled C...IJORCS
ย
This document proposes a new image encryption algorithm combining DNA sequence addition and coupled chaotic maps. The algorithm has two parts: 1) A DNA sequence matrix is obtained by encoding image pixels and divided into blocks that are added using Sugeno fuzzy integral, 2) The modified color components are encrypted using coupled two-dimensional piecewise nonlinear chaotic maps to strengthen security. Experimental results on image databases show the algorithm effectively protects digital image security over the internet.
HYBRIDIZATION OF DCT BASED STEGANOGRAPHY AND RANDOM GRIDSIJNSA Journal
ย
The document discusses a hybrid approach to steganography and visual cryptography for improved data security. It proposes combining principles of steganography, which hides data in a cover media, and visual cryptography, which encrypts images in a way that can be decrypted by human vision without algorithms. Specifically, it describes generating two random grids from a secret image that reveal the image when overlaid but hide it individually. The random grids are created by inverting or substituting pixels based on the secret image. This hybrid approach aims to provide stronger security than either technique alone by incorporating advantages of both.
IRJET-An Effective Strategy for Defense & Medical Pictures Security by Singul...IRJET Journal
ย
The document proposes an effective strategy for encrypting medical and defense images using singular value decomposition (SVD) and fractional Fourier transform (FrFT). It discusses generating shares of an image by applying FrFT to the SVD components, specifically the S matrix of singular values. Multiple shares are created using a sharing matrix. The strategy aims to securely transmit images over unreliable networks. Quantitative analysis shows the approach encrypts images quickly, with high sensitivity to changes and resistance to differential attacks, making it effective for encrypting sensitive images.
STUDY OF TASK SCHEDULING STRATEGY BASED ON TRUSTWORTHINESS ijdpsjournal
ย
MapReduce is a distributed computing model for cloud computing to process massive data. It simplifies the
writing of distributed parallel programs. For the fault-tolerant technology in the MapReduce programming
model, tasks may be allocated to nodes with low reliability. It causes the task to be reexecuted, wasting time
and resources. This paper proposes a reliability task scheduling strategy with a failure recovery mechanism,
evaluates the trustworthiness of resource nodes in the cloud environment and builds a trustworthiness model.
By using the simulation platform CloudSim, the stability of the task scheduling algorithm and scheduling
model are verified in this paper.
AN ENHANCED SEPARABLE REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES USING SIDE M...Editor IJMTER
ย
This paper proposes a scheme for Enhanced Separable Reversible Data Hiding in
Encrypted images Using Side Match. In the first step the original image is encrypted using an
encryption key. Then additional data is embedded into the image by modifying a small portion of the
encrypted image using a data hiding key. With an encrypted image containing additional data, if a
receiver has the data hiding key, he can extract the additional data. If the receiver has the encryption
key, he can decrypt the image, but cannot extract the additional data. If the receiver has both the data
hiding key and encryption key, he can extract the additional data and recover the original content by
exploiting the spatial correlation in natural images. The accuracy of data extraction is improved by
using a better scheme for measuring the smoothness of the received image, and uses the Side Match
scheme to further decrease the error rate of extracted bits.
AN EFFICIENT M-ARY QIM DATA HIDING ALGORITHM FOR THE APPLICATION TO IMAGE ERR...IJNSA Journal
ย
Methods like edge directed interpolation and projection onto convex sets (POCS) that are widely used for image error concealment to produce better image quality are complex in nature and also time consuming. Moreover, those methods are not suitable for real time error concealment where the decoder may not have sufficient computation power or done in online. In this paper, we propose a data-hiding scheme for error concealment of digital image. Edge direction information of a block is extracted in the encoder and is embedded imperceptibly into the host media using quantization index modulation (QIM), thus reduces work load of the decoder. The system performance in term of fidelity and computational load is improved using M-ary data modulation based on near-orthogonal QIM. The decoder extracts the embedded
features (edge information) and those features are then used for recovery of lost data. Experimental results duly support the effectiveness of the proposed scheme.
IRJET- Data Embedding using Image SteganographyIRJET Journal
ย
This document presents a method for secure communication using both steganography and cryptography techniques. It discusses embedding encrypted text into an image using discrete wavelet transform (DWT). Specifically, it first encrypts a text message using the advanced encryption standard (AES) algorithm. It then embeds the encrypted text into an image by applying DWT to decompose the image into sub-bands and hiding the data in the high frequency sub-bands. MATLAB is used to implement a graphical user interface that allows a sender to encrypt a message, embed it into an image, and send the stego-image to a receiver. The receiver interface extracts the encrypted text from the stego-image and decrypts it using AES. The method aims to
This document discusses GPU-based image compression and interpolation using anisotropic diffusion. It presents a method for image compression using binary tree triangular coding to store pixel coordinates. For decompression and interpolation, a partial differential equation (PDE) method called Perona and Malik diffusion is used. Performance of the PDE-based interpolation algorithm is evaluated on CPU and GPU architectures, demonstrating that the GPU implementation significantly reduces computation time, especially for higher resolution images.
Today among various medium of data transmission or storage our sensitive data
are not secured with a third-party, that we used to take help of. Cryptography plays an
important role in securing our data from malicious attack. This paper present a partial
image encryption based on bit-planes permutation using Peter De Jong chaotic map for
secure image transmission and storage. The proposed partial image encryption is a raw data
encryption method where bits of some bit-planes are shuffled among other bit-planes based
on chaotic maps proposed by Peter De Jong. By using the chaotic behavior of the Peter De
Jong map the position of all the bit-planes are permuted. The result of the several
experimental, correlation analysis and sensitivity test shows that the proposed image
encryption scheme provides an efficient and secure way for real-time image encryption and
decryption.
Substitution-diffusion based Image CipherIJNSA Journal
ย
This document proposes a new image encryption scheme using substitution-diffusion based encryption. The algorithm partitions the image into dynamic blocks based on a 128-bit secret key. Each block then undergoes 8 rounds of diffusion and substitution processes. In diffusion, pixels within each block are rearranged in a zigzag pattern. In substitution, pixel values are replaced using row and column transformations based on differences in pixel values. Experimental results show the technique is efficient and provides high security against common attacks like differential and linear cryptanalysis due to the high order of substitution and diffusion.
FPGA Implementation of 2-D DCT & DWT Engines for Vision Based Tracking of Dyn...IJERA Editor
ย
Real time motion estimation for tracking is a challenging task. Several techniques can transform an image into frequency domain, such as DCT, DFT and wavelet transform. Direct implementation of 2-D DCT takes N^4 multiplications for an N x N image which is impractical. The proposed architecture for implementation of 2-D DCT uses look up tables. They are used to store pre-computed vector products that completely eliminate the multiplier. This makes the architecture highly time efficient, and the routing delay and power consumption is also reduced significantly. Another approach, 2-D discrete wavelet transform based motion estimation (DWT-ME) provides substantial improvements in quality and area. The proposed architecture uses Haar wavelet transform for motion estimation. In this paper, we present the comparison of the performance of discrete cosine transform, discrete wavelet transform for implementation in motion estimation.
CSC 347 โ Computer Hardware and MaintenanceSumaiya Ismail
ย
This is report format on CSC 347 โ Computer Hardware and Maintenance. It is for IUBAT university but as per I assume every University can use this format.
Secret-Fragment-Visible Mosaic Image-Creation and Recovery via Colour Transfo...IJSRD
ย
Secret-fragment-visible mosaic image which automatically transforms the secret image into a meaningful mosaic image of the same size. The mosaic image looks like to an arbitrarily selected target image. It may be used as a camouflage of the secret image and yielded by dividing the secret image into fragments and transforming their color characteristics to the corresponding blocks of the target image. Some technologies are designed to conduct the color transformation process so that the secret image may be recovered. The information required for recovering the secret is embedding into the created mosaic image. Good experimental results are showing the feasibility of the proposed method.
Ieee a secure algorithm for image based information hiding with one-dimension...Akash Rawat
ย
ieee a secure algorithm for image based information hiding with one-dimensional chaotic systems.It used 1 dimensional chaotic system.ieee paper related for image encryption
This document summarizes a research paper that proposes a novel reversible data hiding scheme using AES encryption. The scheme consists of three phases: 1) AES encryption of the original image, 2) data embedding by modifying parts of the encrypted image, 3) data extraction and image recovery by decrypting the encrypted image and extracting the hidden data. The scheme aims to securely hide data in images while allowing perfect recovery of the original image. Experimental results show the decrypted image has a high PSNR value of 55.11dB and the hidden data can be successfully extracted.
Now days, Detection of human head play an very important role in pedestrian counting. Machine learning is one platform, where human being can train a machine to act without being explicitly programmed and gives more accurate result, even when there is no enough data. Convolution neural network is one which works well for multimedia communication such as Text, Audio and Video. In this paper convnets play an important role in human head detection. In this paper itโs going to explain the less number of layers with more accuracy in the results with less time consuming.
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.
Hybrid compression based stationary wavelet transformsOmar Ghazi
ย
This document presents a hybrid compression approach for images that uses Stationary Wavelet Transforms (SWT), Back Propagation Neural Network (BPNN), and Lempel-Ziv-Welch (LZW) compression. The approach involves: 1) preprocessing the image, 2) applying SWT, 3) converting to a 1D vector using zigzag scan, and 4) hybrid compression using BPNN vector quantization and LZW lossless compression. Experimental results show the SWT with BPNN and LZW achieves the highest compression ratios but the longest processing time, while SWT with Run Length encoding has a lower ratio but shorter time. The hybrid approach combines lossy and lossless compression techniques to obtain a
This document summarizes a research paper that proposes a new lossless image compression algorithm called Pixel Size Reduction (PSR). The PSR algorithm achieves compression by representing pixels using the minimum number of bits needed based on their frequency of occurrence in the image, rather than a fixed 8 bits per pixel. Experimental results on test images showed that the PSR algorithm achieved better compression ratios than other lossless compression methods like Huffman, TIFF, GPPM, and PCX. The paper compares the compressed file sizes of the PSR algorithm to these other methods on various synthetic images.
Deep Learning for Natural Language ProcessingIRJET Journal
ย
This document discusses the use of deep learning techniques in natural language processing. It begins by defining deep learning as a set of machine learning algorithms that use multiple layered models like neural networks to learn inputs. Deep learning aims to process complex data like text in a way that mimics the human brain. The document then discusses several deep learning methods that have been applied to natural language processing tasks, including stacked autoencoders, deep Boltzmann machines, and transfer learning. It provides examples of how these techniques are used to perform tasks like object recognition from text and speech recognition.
The document presents a new efficient color image compression technique that aims to improve the quality of decompressed images while achieving higher compression ratios. It does this by compressing important edge parts of the image differently than non-edge background parts. Specifically, it applies low-quality lossy compression to non-edge parts and high-quality lossy compression to edge parts. The technique uses edge detection, adaptive thresholding based on local variance and mean, and discrete cosine transform followed by quantization and entropy encoding. Experimental results on various images show it achieves better compression ratios, lower bit rates, and higher peak signal to noise ratios compared to non-adaptive methods.
COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMSIJNSA Journal
ย
This paper proposed a novel color image encryption scheme based on multiple chaotic systems. The ergodicity property of chaotic system is utilized to perform the permutation process; a substitution operation is applied to achieve the diffusion effect. In permutation stage, the 3D color plain-image matrix is converted to a 2D image matrix, then two generalized Arnold maps are employed to generate hybrid chaotic sequences which are dependent on the plain-imageโs content. The generated chaotic sequences are then applied to perform the permutation process. The encryptionโs key streams not only depend on the cipher keys but also depend on plain-image and therefore can resist chosen-plaintext attack as well as
known-plaintext attack. In the diffusion stage, four pseudo-random gray value sequences are generated by
another generalized Arnold map. The gray value sequences are applied to perform the diffusion process by bitxoring operation with the permuted image row-by-row or column-by-column to improve the encryption rate. The security and performance analysis have been performed, including key space analysis, histogram analysis, correlation analysis, information entropy analysis, key sensitivity analysis, differential analysis
etc. The experimental results show that the proposed image encryption scheme is highly secure thanks to its
large key space and efficient permutation-substitution operation, and therefore it is suitable for practical image and video encryption.
COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMSIJNSA Journal
ย
This document proposes a novel color image encryption scheme based on multiple chaotic systems. The scheme utilizes the ergodic properties of chaotic systems to perform pixel permutation and applies a substitution operation to achieve diffusion. In the permutation stage, two generalized Arnold maps are used to generate hybrid chaotic sequences to permute pixel positions. In the diffusion stage, four pseudo-random gray value sequences generated by another generalized Arnold map are used to diffuse the permuted image via bitwise XOR operations. Security analysis shows the scheme has a large key space and is highly secure against statistical attacks, differential attacks, and chosen/known plaintext attacks.
COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMSIJNSA Journal
ย
This paper proposed a novel color image encryption scheme based on multiple chaotic systems. The ergodicity property of chaotic system is utilized to perform the permutation process; a substitution
operation is applied to achieve the diffusion effect. In permutation stage, the 3D color plain-image matrix
is converted to a 2D image matrix, then two generalized Arnold maps are employed to generate hybrid chaotic sequences which are dependent on the plain-imageโs content. The generated chaotic sequences are then applied to perform the permutation process. The encryptionโs key streams not only depend on the
cipher keys but also depend on plain-image and therefore can resist chosen-plaintext attack as well as
known-plaintext attack. In the diffusion stage, four pseudo-random gray value sequences are generated by another generalized Arnold map. The gray value sequences are applied to perform the diffusion process by bitxoring operation with the permuted image row-by-row or column-by-column to improve the encryption rate. The security and performance analysis have been performed, including key space analysis, histogram analysis, correlation analysis, information entropy analysis, key sensitivity analysis, differential analysis etc. The experimental results show that the proposed image encryption scheme is highly secure thanks to its large key space and efficient permutation-substitution operation, and therefore it is suitable for practical image and video encryption.
STUDY OF TASK SCHEDULING STRATEGY BASED ON TRUSTWORTHINESS ijdpsjournal
ย
MapReduce is a distributed computing model for cloud computing to process massive data. It simplifies the
writing of distributed parallel programs. For the fault-tolerant technology in the MapReduce programming
model, tasks may be allocated to nodes with low reliability. It causes the task to be reexecuted, wasting time
and resources. This paper proposes a reliability task scheduling strategy with a failure recovery mechanism,
evaluates the trustworthiness of resource nodes in the cloud environment and builds a trustworthiness model.
By using the simulation platform CloudSim, the stability of the task scheduling algorithm and scheduling
model are verified in this paper.
AN ENHANCED SEPARABLE REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES USING SIDE M...Editor IJMTER
ย
This paper proposes a scheme for Enhanced Separable Reversible Data Hiding in
Encrypted images Using Side Match. In the first step the original image is encrypted using an
encryption key. Then additional data is embedded into the image by modifying a small portion of the
encrypted image using a data hiding key. With an encrypted image containing additional data, if a
receiver has the data hiding key, he can extract the additional data. If the receiver has the encryption
key, he can decrypt the image, but cannot extract the additional data. If the receiver has both the data
hiding key and encryption key, he can extract the additional data and recover the original content by
exploiting the spatial correlation in natural images. The accuracy of data extraction is improved by
using a better scheme for measuring the smoothness of the received image, and uses the Side Match
scheme to further decrease the error rate of extracted bits.
AN EFFICIENT M-ARY QIM DATA HIDING ALGORITHM FOR THE APPLICATION TO IMAGE ERR...IJNSA Journal
ย
Methods like edge directed interpolation and projection onto convex sets (POCS) that are widely used for image error concealment to produce better image quality are complex in nature and also time consuming. Moreover, those methods are not suitable for real time error concealment where the decoder may not have sufficient computation power or done in online. In this paper, we propose a data-hiding scheme for error concealment of digital image. Edge direction information of a block is extracted in the encoder and is embedded imperceptibly into the host media using quantization index modulation (QIM), thus reduces work load of the decoder. The system performance in term of fidelity and computational load is improved using M-ary data modulation based on near-orthogonal QIM. The decoder extracts the embedded
features (edge information) and those features are then used for recovery of lost data. Experimental results duly support the effectiveness of the proposed scheme.
IRJET- Data Embedding using Image SteganographyIRJET Journal
ย
This document presents a method for secure communication using both steganography and cryptography techniques. It discusses embedding encrypted text into an image using discrete wavelet transform (DWT). Specifically, it first encrypts a text message using the advanced encryption standard (AES) algorithm. It then embeds the encrypted text into an image by applying DWT to decompose the image into sub-bands and hiding the data in the high frequency sub-bands. MATLAB is used to implement a graphical user interface that allows a sender to encrypt a message, embed it into an image, and send the stego-image to a receiver. The receiver interface extracts the encrypted text from the stego-image and decrypts it using AES. The method aims to
This document discusses GPU-based image compression and interpolation using anisotropic diffusion. It presents a method for image compression using binary tree triangular coding to store pixel coordinates. For decompression and interpolation, a partial differential equation (PDE) method called Perona and Malik diffusion is used. Performance of the PDE-based interpolation algorithm is evaluated on CPU and GPU architectures, demonstrating that the GPU implementation significantly reduces computation time, especially for higher resolution images.
Today among various medium of data transmission or storage our sensitive data
are not secured with a third-party, that we used to take help of. Cryptography plays an
important role in securing our data from malicious attack. This paper present a partial
image encryption based on bit-planes permutation using Peter De Jong chaotic map for
secure image transmission and storage. The proposed partial image encryption is a raw data
encryption method where bits of some bit-planes are shuffled among other bit-planes based
on chaotic maps proposed by Peter De Jong. By using the chaotic behavior of the Peter De
Jong map the position of all the bit-planes are permuted. The result of the several
experimental, correlation analysis and sensitivity test shows that the proposed image
encryption scheme provides an efficient and secure way for real-time image encryption and
decryption.
Substitution-diffusion based Image CipherIJNSA Journal
ย
This document proposes a new image encryption scheme using substitution-diffusion based encryption. The algorithm partitions the image into dynamic blocks based on a 128-bit secret key. Each block then undergoes 8 rounds of diffusion and substitution processes. In diffusion, pixels within each block are rearranged in a zigzag pattern. In substitution, pixel values are replaced using row and column transformations based on differences in pixel values. Experimental results show the technique is efficient and provides high security against common attacks like differential and linear cryptanalysis due to the high order of substitution and diffusion.
FPGA Implementation of 2-D DCT & DWT Engines for Vision Based Tracking of Dyn...IJERA Editor
ย
Real time motion estimation for tracking is a challenging task. Several techniques can transform an image into frequency domain, such as DCT, DFT and wavelet transform. Direct implementation of 2-D DCT takes N^4 multiplications for an N x N image which is impractical. The proposed architecture for implementation of 2-D DCT uses look up tables. They are used to store pre-computed vector products that completely eliminate the multiplier. This makes the architecture highly time efficient, and the routing delay and power consumption is also reduced significantly. Another approach, 2-D discrete wavelet transform based motion estimation (DWT-ME) provides substantial improvements in quality and area. The proposed architecture uses Haar wavelet transform for motion estimation. In this paper, we present the comparison of the performance of discrete cosine transform, discrete wavelet transform for implementation in motion estimation.
CSC 347 โ Computer Hardware and MaintenanceSumaiya Ismail
ย
This is report format on CSC 347 โ Computer Hardware and Maintenance. It is for IUBAT university but as per I assume every University can use this format.
Secret-Fragment-Visible Mosaic Image-Creation and Recovery via Colour Transfo...IJSRD
ย
Secret-fragment-visible mosaic image which automatically transforms the secret image into a meaningful mosaic image of the same size. The mosaic image looks like to an arbitrarily selected target image. It may be used as a camouflage of the secret image and yielded by dividing the secret image into fragments and transforming their color characteristics to the corresponding blocks of the target image. Some technologies are designed to conduct the color transformation process so that the secret image may be recovered. The information required for recovering the secret is embedding into the created mosaic image. Good experimental results are showing the feasibility of the proposed method.
Ieee a secure algorithm for image based information hiding with one-dimension...Akash Rawat
ย
ieee a secure algorithm for image based information hiding with one-dimensional chaotic systems.It used 1 dimensional chaotic system.ieee paper related for image encryption
This document summarizes a research paper that proposes a novel reversible data hiding scheme using AES encryption. The scheme consists of three phases: 1) AES encryption of the original image, 2) data embedding by modifying parts of the encrypted image, 3) data extraction and image recovery by decrypting the encrypted image and extracting the hidden data. The scheme aims to securely hide data in images while allowing perfect recovery of the original image. Experimental results show the decrypted image has a high PSNR value of 55.11dB and the hidden data can be successfully extracted.
Now days, Detection of human head play an very important role in pedestrian counting. Machine learning is one platform, where human being can train a machine to act without being explicitly programmed and gives more accurate result, even when there is no enough data. Convolution neural network is one which works well for multimedia communication such as Text, Audio and Video. In this paper convnets play an important role in human head detection. In this paper itโs going to explain the less number of layers with more accuracy in the results with less time consuming.
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.
Hybrid compression based stationary wavelet transformsOmar Ghazi
ย
This document presents a hybrid compression approach for images that uses Stationary Wavelet Transforms (SWT), Back Propagation Neural Network (BPNN), and Lempel-Ziv-Welch (LZW) compression. The approach involves: 1) preprocessing the image, 2) applying SWT, 3) converting to a 1D vector using zigzag scan, and 4) hybrid compression using BPNN vector quantization and LZW lossless compression. Experimental results show the SWT with BPNN and LZW achieves the highest compression ratios but the longest processing time, while SWT with Run Length encoding has a lower ratio but shorter time. The hybrid approach combines lossy and lossless compression techniques to obtain a
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COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMSIJNSA Journal
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This paper proposed a novel color image encryption scheme based on multiple chaotic systems. The ergodicity property of chaotic system is utilized to perform the permutation process; a substitution
operation is applied to achieve the diffusion effect. In permutation stage, the 3D color plain-image matrix
is converted to a 2D image matrix, then two generalized Arnold maps are employed to generate hybrid chaotic sequences which are dependent on the plain-imageโs content. The generated chaotic sequences are then applied to perform the permutation process. The encryptionโs key streams not only depend on the
cipher keys but also depend on plain-image and therefore can resist chosen-plaintext attack as well as
known-plaintext attack. In the diffusion stage, four pseudo-random gray value sequences are generated by another generalized Arnold map. The gray value sequences are applied to perform the diffusion process by bitxoring operation with the permuted image row-by-row or column-by-column to improve the encryption rate. The security and performance analysis have been performed, including key space analysis, histogram analysis, correlation analysis, information entropy analysis, key sensitivity analysis, differential analysis etc. The experimental results show that the proposed image encryption scheme is highly secure thanks to its large key space and efficient permutation-substitution operation, and therefore it is suitable for practical image and video encryption.
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Performance analysis of transformation and bogdonov chaotic substitution base...IJECEIAES
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In this article, a combined Pseudo Hadamard transformation and modified Bogdonav chaotic generator based image encryption technique is proposed. Pixel position transformation is performed using Pseudo Hadamard transformation and pixel value variation is made using Bogdonav chaotic substitution. Bogdonav chaotic generator produces random sequences and it is observed that very less correlation between the adjacent elements in the sequence. The cipher image obtained from the transformation stage is subjected for substitution using Bogdonav chaotic sequence to break correlation between adjacent pixels. The cipher image is subjected for various security tests under noisy conditions and very high degree of similarity is observed after deciphering process between original and decrypted images.
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The document proposes and evaluates a new digital image security scheme that uses Residue Number System (RNS) encoding/decoding and a modified Arnold transform algorithm. Key points:
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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.
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The media content shared on the internet has increased tremendously nowadays. The streaming service has major role in contributing to internet traffic all over the world. As the major content shared are in the form of images and rapid increase in computing power a better and complex encryption standard is needed to protect this data from being leaked to unauthorized person. Our proposed system makes use of chaotic maps, deoxyribonucleic acid (DNA) coding and ribonucleic acid (RNA) coding technique to encrypt the image. As videos are nothing but collection of images played at the rate of minimum 30 frames/images per second, this methodology can also be used to encrypt videos. The complexity and dynamic nature of chaotic systems makes decryption of content by unauthorized personal difficult. The hybrid usage of chaotic systems along with DNA and RNA sequencing improves the encryption efficiency of the algorithm and also makes it possible to decrypt the images at the same time without consuming too much of computation power.
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The document summarizes a proposed user-friendly image sharing scheme that uses JPEG-LS prediction and LSB matching functions. The scheme encodes a secret image into meaningful shadow images using different prime numbers for different blocks, as determined by JPEG-LS prediction. It hides the prime number indicators in the least significant bits of pixels using LSB matching to prevent image degradation. The experimental results showed the reconstructed image quality was higher than previous schemes, making it suitable for applications requiring high quality images like medicine, military, or art.
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In this work, a new scheme of image encryption based on chaos and Fast Walsh Transform (FWT) has been proposed.
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Similar to Chaotic Block Image Scheme using Large Key Space and Message Digest Algorithm (20)
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Chaotic Block Image Scheme using Large Key Space and Message Digest Algorithm
1. Fahmi Nasser Ali & Mussa Mohamed Ahmed
International Journal of Computer Science and Security (IJCSS), Volume (13) : Issue (4) : 2019 135
Chaotic Block Image Scheme using Large Key Space and
Message Digest Algorithm
Fahmi Nasser Ali fahminasser_88@yahoo.com
Department of information technology
University of Aden
Aden, Yemen
Mussa Mohamed Ahmed mussa_m7@yahoo.com
Department of electronics and communication
University of Aden
Aden, Yemen
Abstract
In this paper, chaotic block image scheme using large key space and message digest algorithm.
Cat map intended for confusion and 2D-Sine Tent Composite map (2D-STCM) key generator
intended for diffusion. Confusion is implemented by 2D Cat map with arbitrary block size. In the
first tendency, 2D cat map use for local shuffling of indexes inside blocks, while in the second
tendency, 2D cat map used for global shuffling of whole image indexes. The designed algorithm
executes two confusions and one diffusion in each iteration. To increase the security level, the
message digestion algorithm is used as a fingerprint for the plain image that creates the initial
value of the key. After that 2D-STCM generates a large key stream. Diffusion implementation
takes place by XOR operation; between a key stream and confused image. Experimental
results, show that security level increases due to integration of confusion and diffusion. On the
other side large key space and the high sensitivity of secret keys have been given a guarantee
for the performance of the security. Performance measures reach to the top value among those
in the similar researches. To verify the obtained results, authors implemented inverse chaos.
All the tests are processed by MATLAB 2015a.
Keywords: Image Encryption, 2D-STCM, Cat Map, MD5, Confusion, Diffusion.
1. INTRODUCTION
The converting of information image, video, audio and text from insecure form into secure form
and transmitting it over the insecure network is known as cryptography [2]. In cryptography,
there are many traditional encryption schemes such as Advanced Encryption Standard (AES),
Data Encryption Standard (DES), 3DES and Rivest-Shamir-Adleman algorithm (RSA) etc. [2-
3]. Traditional encryption schemes are not suitable for image encryption because there are
inherent features in image like bulk data capacity, correlation among adjacent pixels and high
redundancy so the image encryption differs from text encryption.
System Chaos is the best in encryption and decryption for multimedia. Chaos system was
discovered by Edward Lorenz in 1963 [5]. Chaos system properties are pseudo-random, non-
periodicity, ergodicity and highly sensitive to system parameters and initial conditions [6]. Chaos
systems are based on initial condition and control parameter. Therefore, a little change in initial
condition or control parameter cause a completely change. Chaotic maps can be categorized
into two groups: 1D chaotic maps such as Logistic map, Sine map and Tent map etc. and a
high-dimensional chaotic map such as Arnold map, Exponential map and Henon map etc. The
combining of chaotic theory and cryptography are important fields in multimedia security to
achieve high security. Therefore, the level of security is high when it achieves grouping of
confusion and diffusion [7]. The concept of confusion is to change the pixel's position of plain
image and diffusion is to change the gray level value of confused image.
Rest of the paper is discussed as follows. Section 2 explores the related work in the area of
chaotic block Image scheme using large key space and message digest algorithm, discuss
2. Fahmi Nasser Ali & Mussa Mohamed Ahmed
International Journal of Computer Science and Security (IJCSS), Volume (13) : Issue (4) : 2019 136
Arnold Cat Map, MD5 and 2D-STCM. Section 3 represents a detail description of proposed
designed scheme. Section4 introduces the experimental results, compares varies image
encryption techniques and discusses the outcomes from several algorithms. Section5 is
conclusion and future work.
2. RELATED WORK
There are many researches in digital image encryption based on chaotic maps and key stream
generators. In [8], cat map and significant bit-planes are executed. The cat map is used to
scramble the positions of image pixels and the significant bit-planes can diffuse the image
pixels, but is not robust against differential attack because NPCR is not approaching to
100%and UACI is not approaching to 33% In [9], Arnold cat map and 1D Logistic map are
applied. The Arnold cat map is used to shuffle the pixels' positions of image and the 1D Logistic
map diffused the image pixels to get encrypted image, but is not robust against brute-force
attack because key space of encryption algorithm is not large. In [10], the three-dimensional
Henon chaotic map and the Cat chaotic map are executed. The cat chaotic map is used to
confuse the positions of image pixels and the three-dimensional Henon chaotic map can diffuse
the image pixels. Then, repeat the confusing and diffusing process to increase the resistance
to statistical and related attacks. The disadvantages of this method are the low encryption
speed. In [11], the 2D-sine tent composition map (2D-STCM) and the chaotic circular pixel
shuffling (CCPS) are applied. 2D-STCM is used to generate a key sequence and CCPS is used
to shuffle the pixels' positions of image based on 2D-STCM. In [12], algorithm based on one-
time keys and robust chaotic maps. Generate pseudo-random key sequence by a piecewise
linear chaotic map. The initial conditions were generated by the MD5 of the mouse positions.
This method is robust against brute-force attack because key space of encryption algorithm is
very large, but is not strong in resisting statistical attack because correlation coefficients of
ciphered image is not good. In [16], created new chaotic maps based on Beta function. It
executes three phases: permutation, diffusion and substitution. In [17], new chaotic map, strong
S-boxes and permutation function are implemented. It executes four phases: diffusion,
substitution, diffusion and permutation. The diffusion process is based on a new chaotic map.
The substitution process is based on a strong S-boxes. The permutation process is based on
a permutation function. In [19], cat map, logistic and tent maps are applied. The cat map is used
to permutation process, and then the logistic and tent maps diffused the image pixels to get the
encrypted image, but is not robust against statistical attack because correlation coefficients of
ciphered image is not good. In [20], combined 2-D Sine Logistic modulation map, derived from
the Logistic and Sine maps with a chaotic magic transform, aim was to resist various attacks
but, without mention target or specific types. In [22], logistic map, cat map, SHA-3 hash function
and auto-updating system are executed. Algorithm acts like one-time pad. The logistic map and
SHA-3 hash function are used to permutation process, and then the cat map and SHA-3 hash
function diffused the image pixels to get the encrypted image, but it is not strong against
statistical attack because correlation coefficients of ciphered image are not too low.
In this paper, the idea of encryption scheme proposes to achieve confusion and diffusion. First,
the whole image is divided into blocks, random change of block size in each iteration. Then,
Arnold cat map is Implemented to obtain confused image. Second, the key stream is generated
by 2D-STCM. Then, implement bit-XOR operation between key stream of 2D-STCM and
confused image. the previous steps are repeated two or more times to obtain the final encrypted
image.
Below Arnold Cat Map, MD5 and 2D-STCM Key are analyzed.
2.1 Arnold Cat Map
Arnold Cat Map [9] is two-dimensional discrete map. This map is used to shuffle the pixel's
positions of image.
[Xโฒ
Yโฒ
] = [
1 a
b ab + 1
] [
X
Y
] mod(N) (1)
The Arnold cat map uses two parameters (a, b); the a, b are control parameters which change
the pixels' positions in image, where (a, b) are positive integers stand for the control parameters,
(x, y) are the old pixel position and (x', y') are the new pixel position.
3. Fahmi Nasser Ali & Mussa Mohamed Ahmed
International Journal of Computer Science and Security (IJCSS), Volume (13) : Issue (4) : 2019 137
The disadvantage of Arnold map, that the same original image appears after several iterations;
which concludes that Arnold map has periodic behavior. To solve this, issue the parameters (a,
b) are generated randomly by using 2D-STCM or manual change of parameters (a, b) in each
iteration.
2.2 Message Digest Algorithm
The message digest algorithm (MD5) takes any arbitrary length as input data, and produces
output of 128-bit (32-digit hexadecimal) as fingerprint [15]. Each message produces a special
MD5. It has been widely used to verify data or image integrity. As can be seen, in proposed
scheme Fig.2, MD5 produces initial value. More details, explanations and applications of MD5
are given in section 3.1.
2.3 Two Dimensional-Sine Tent Composite Map
The 2D-Sine Tent composite map [11] is a discrete-time dynamical system. It is built from 1D
Sine map and 1D Tent map defined in equations (3) and (4) respectively:
๐ฅ ๐+1 = ๐ ร sin(๐๐ฅ ๐) (3)
๐ฅ ๐+1 = {
๐๐ฅ ๐ . 0 โค ๐ฅ < 0.5
๐(1 โ ๐ฅ ๐) . 0.5 โค ๐ฅ โค 1
(4)
Here ฮผ and r are control parameters lies in the range (0, 4] [11]. To overcome the limitations of
1D chaotic map, combined Sine and Tent maps. It can be defined as:
๐ฅ ๐+1 =
{
((sin( ๐๐ฆ๐) + 3) ร
๐ฅ ๐
2
) ๐๐๐ 1. ๐ฅ๐ < 0.5
((sin( ๐๐ฆ๐) + 3) ร
(1โ๐ฅ ๐)
2
) ๐๐๐ 1 ๐ฅ๐ โฅ 0.5
(5)
๐ฆ ๐+1 =
{
((sin( ๐๐ฅ ๐) + 3) ร
๐ฆ ๐
2
) ๐๐๐ 1. ๐ฆ๐ < 0.5
((sin( ๐๐ฅ ๐) + 3) ร
(1โ๐ฆ ๐)
2
) ๐๐๐ 1. ๐ฆ๐ โฅ 0.5
(6)
A trajectory of 2D-Sine Tent composite map is excellent chaotic behavior. Figure 1 compares
the trajectories of 2D-STCM, 2D-SLMM and 2D-LM [11]. From trajectory figure1(a) of 2D-STCM
covers a large region compared with other two. So, it proves that the proposed chaotic function
has better ergodicity and randomness property.
(a) (b) (c)
FIGURE 1: Trajectories of (a) 2D-STCM Map; (b) 2D-SLMM Map and (c) 2D-Logistic Map [11].
4. Fahmi Nasser Ali & Mussa Mohamed Ahmed
International Journal of Computer Science and Security (IJCSS), Volume (13) : Issue (4) : 2019 138
3. PROPOSED DESIGNED SCHEME
The proposed image encryption scheme has two or more iterations, in each iteration there is
group of confusion and diffusion processes. In first confusion process, an Image of size NรN is
random decomposed into MรM block, 2D-cat map is used for local shuffling of indexes inside
blocks. In second confusion process, 2D-cat map is used for global shuffling of whole image
indexes. In diffusion process, XOR operation between key stream {Ki} of 2D-STCM and
confused image is implemented.
3.1 Initial Value (X) Generation using MD5
Steps to generate the initial value (x): First, extract the value of MD5 hexadecimal from plain
image. Second, convert the hexadecimal MD5 value to a decimal value. Third, use the
summation operation to sum decimal values to obtain a single value. Finally, convert the
decimal value to a fractional value by dividing by 1000.
Let's take a look at this example, on a system that has an image called " Lena.TIF " the MD5
hash would look as follows:
Image name Hash Value
C: Lena.TIF 5649b212f4f9e5c781a9e4f8c75208e5
Hexadecimal Value Decimal Value
5649b212f4f9e5c781a9e4f8c75208e5
5.6.4.9.11.2.1.2.15.4.15.9.14.5.12.7.8.
1.10.9.14.4.15.8.12.7.5.2.0.8.14.5
Sum (Decimal Value) = 243. Initial value (x) = 243/10000 = 0.0243.
As shown in Fig.2 y as second initial value could be given by user directly or by using indirect
different methods from given image.
3.2 Key Sequence Generation using 2D-STCM
The equations (5) and (6) of 2D-STCM are repetitive for generate two key sequences {K1} and
{K2} which as original image size. The initial values (x, y) are a fractional value which passes
to the 2D-STCM equations to generate two keys sequences. In the next iteration, we change
the initial values (x, y). The random change of initial values (โx, โy) in each iteration increasing
the key space that resist brute-force attack. In [14], shows preprocessing of fractional key to
obtain integer key with 8 bits suitable for image encryption as following:
xn = (floor (xn ร 232))mod 256. xn = k1 (7)
yn = (floor (yn ร 232))mod 256. yn = k2 (8)
where Floor (๐ฅ ๐, ๐ฆ๐) returns the values of (๐ฅ ๐, ๐ฆ๐) to the nearest integer and mod (๐ฅ ๐, ๐ฆ๐) returns
the rest of the division. Preprocess leads to a better distribution.
3.3 Image Encryption Process
In this section, the proposed image encryption process is explained.
Step 1: Read two dimensional 8-bit gray scale image {๐ผ๐ร๐} of size NรN, where N = (256, 512
or 1024).
Step 2: Decompose the whole image {๐ผ๐ร๐} into the MรM size blocks, where M = (64, 16,128โฆ).
Step 3: Apply a 2D-cat map with each block using the control parameters (๐๐ , ๐๐) to shuffle the
pixel's positions with each block and perform this step two or more times to get first confused
image I1(๐ร๐)
.
5. Fahmi Nasser Ali & Mussa Mohamed Ahmed
International Journal of Computer Science and Security (IJCSS), Volume (13) : Issue (4) : 2019 139
Step 4: Apply the 2D-cat map with first confused image {I1(๐ร๐)
} using the control parameters
(๐๐ , ๐๐) to shuffle the pixel's positions of first confused image and perform this step two or more
times to get a second confused image {I2(๐ร๐)
}.
Step 5: In diffusion process. The 2D-STCM keys stream {๐1, ๐2} are converted to two
dimensions { k1(๐ร๐)
, k2(๐ร๐)
} as the size of confused image. Then select one key used in the
diffusion process. Final, apply a bit-XOR operation between one key stream { k1 ๐๐ 2(๐ร๐)
} from
2D-STCM and the second confused image {I2(๐ร๐)
}, to get the final encrypted image.
{๐ธ(๐ร๐)} = {I2(๐ร๐)
} โ { k1 ๐๐ 2(๐ร๐)
} (9)
Step 6: In the next iterations, the previous steps are repeated to obtain the encrypted image.
3.4 Image Decryption Process
In this section decryption process of the encrypted image is explained.
Step 1: In diffusion process. 2D-STCM keys stream {๐1, ๐2} are converted to two dimensions
{ k1(๐ร๐)
, k2(๐ร๐)
} as the size of encrypted image. Then chose the same key that was used in the
encryption process, it should be used in the decryption process. Apply bit-XOR operation
between key stream { k1 ๐๐ 2(๐ร๐)
} from 2D-STCM and the encrypted image {๐ธ(๐ร๐)}, to get the
second confused image {I2(๐ร๐)
}.
In diffusion process. The 2D-STCM keys stream {๐1, ๐2} are converted to two dimensions {
k1(๐ร๐)
, k2(๐ร๐)
} as the size of confused image. Then select one key used in the diffusion process.
Final, apply a bit-XOR operation between one key stream { k1 ๐๐ 2(๐ร๐)
} from 2D-STCM and the
second confused image {I2(๐ร๐)
}, to get the final encrypted image.
{I2(๐ร๐)
} = {๐ธ(๐ร๐)} โ { k1 ๐๐ 2(๐ร๐)
} (10)
Step 2: Apply the 2D-cat map with second confused image {I2(๐ร๐)
} using the control parameters
(๐๐ , ๐๐) to shuffle the pixel's positions of second confused image and perform this step two or
more times to get a first confused image {I1(๐ร๐)
}.
Step 3: Decompose the first confused image I1(๐ร๐)
into the MรM size blocks, where M = (64,
16,128,).
Step 5: Apply a 2D-cat map with each block using the control parameters (๐๐ , ๐๐) to shuffle the
pixel's positions with each block and perform this step two or more times to get a decrypted
image.
Step 6: In the next iterations, the previous steps are repeated to obtain original image.
Looking at the left-hand side of the figure 2, we can see that the processing of the key in three
phases. First, put an initial value (x, y) as a fractional value. Second, random change of the
initial values (x, y) in each iterate. Finally, the initial values (x, y) enter to 2D-STCM equations
which generate a two sequence keys as the size of the encryption image. These keys are used
in the cat map and diffusion process. The right-hand portion of the figure 2, shows the
encryption steps in four phases. First, divided the whole image into the blocks. Second, Apply
a 2D-cat map with each block. Third, apply a 2D-cat map with whole image. Finally, implement
a bit-XOR operation between key stream {Ki} of 2D-STCM and confused image.
6. Fahmi Nasser Ali & Mussa Mohamed Ahmed
International Journal of Computer Science and Security (IJCSS), Volume (13) : Issue (4) : 2019 140
FIGURE 2: Block Diagram of The Proposed Encryption Scheme.
4. EXPERIMENTAL RESULT
The algorithm proposal encrypts the original image through confusion and diffusion using
multiple steps. The algorithm is implemented using the MATLAB 2015a simulation program,
several gray images from University of Southern California - Signal and Image Processing
Institute (USC-SIPI) and system configuration Intelยฎ coreโข i3 CPU M330- @2.13 GHz and 4
GB RAM, operating system 64 bits. A comparison is done with the many other algorithms in
[16, 17, 18, 19, 20, 21]. The results showed that our algorithm performs better or similar than
other algorithms.
4.1 Key Space
The key space of encryption algorithm should be large enough to resist brute-force attack.
Diffusion Key has five parameters (x. y. โx. โy. n); four parameters (x, y, โx, โy) represented by
a double data type, each parameter is 64 bits and parameter (n) is real number represented by
Plain
image
4. Divide image
into M*M blocks
(64,16.128,..)
5. Confusion with
each block by cat
map
7. Confusion with
whole image by
cat map
8. Diffusion by
bit-XOR
cipher
image
Put
Initial
value y
1. random change of
initial values (x, y) in
each iteration
3. 2D-
STCM
generate
(a, b) key
use with cat
map
2. 2D-
STCM use
to generate
a two keys
stream
Two or more
iteration.
Two or more
iteration.
Two or more
iteration.
Get
MD5
Get Initial
value x
7. Fahmi Nasser Ali & Mussa Mohamed Ahmed
International Journal of Computer Science and Security (IJCSS), Volume (13) : Issue (4) : 2019 141
8 bits. Diffusion key space size in this paper is 2264
and it is large enough to resist brute-force
attack.
In addition, there are confusion keys in this paper represented by key (a, b) of cat map, number
of internal iterations and block size change, they are longer than 240
. Total key space is longer
than 2304.
4.2 Key Sensitivity
A small change in security key causes large change in cipher image and a small change in
security key can't be recover an original image as show in figures below.
Encryption Key 1: x0=0.0058052, y0=0.0352572, โx = 0.01, โy = 0.01, n=5.
Decryption Key 2: x0=0.0058052, y0=0.0352571, โx = 0.01, โy = 0.01, n=5.
FIGURE 3: An Original Image Encryption by Key 1 and Decryption by Key 2.
4.3 Histogram
The histogram plots the occurrences frequency of image pixel value that show statistical
features of images. Y-axis represents number of image pixels and X-axis represents intensity
level. The histogram of plain image is not distributed uniformly, this means statistically pixels
can be predicted, but histogram of cipher image is distributed uniformly, this means statistically
pixels can't be predicted. Analyzed histograms of three plain images and their ciphered images.
Figure 4 gives the plain images of โCameramanโ, โLenaโ, โCoupleโ, and their histograms,
respectively. Figure 5 gives the ciphered images of โCameramanโ, โLenaโ, โCoupleโ, and their
histograms respectively. The results shown in Figures 4 and 5 [images from USC-SIPI] indicate
that our algorithm resists statistical attack.
FIGURE 4: Plain images of โCameramanโ, โLenaโ, โCoupleโ, and their histograms respectively.
8. Fahmi Nasser Ali & Mussa Mohamed Ahmed
International Journal of Computer Science and Security (IJCSS), Volume (13) : Issue (4) : 2019 142
FIGURE 5: Ciphered images of โCameramanโ, โLenaโ, โCoupleโ, and their histograms respectively.
4.4 Pixel Correlation
This analysis is used to study the correlation between two adjacent pixels in a plain image and
a cipher image. In the plain image, the correlation between adjacent pixels are high, but in the
cipher image should be low correlation between the adjacent pixels to resist statistical attacks.
The below equations calculate the correlation coefficient as follows:
Ex =
1
N
โ xi
n
i=1 . (11)
Dx =
1
N
โ (xi
n
i=1 โ EX)2
. (12)
cov(x, y) =
1
N
โ (xi
n
i=1 โ EX)(yi โ Ey) . (13)
rxy =
cov(x,y)
โDxโDy
. (14)
In the above equations, in equation (11) calculates mean, in equation (12) calculates stander
division, in equation (13) calculates covariance and in equation (14) calculates correlation
coefficient. In below Table 1 illustrates correlation coefficient values of the plain image and its
cipher image.
4.5 Information Entropy
Entropy is a statistical measure that use to randomness test in the plain image and ciphered
image. The information of entropy is given by equation (15):
H(X) = โ โ Pr(2N
i=1 xi) log2 Pr(xi) (15)
Here H(X) is the entropy value, Pr(xi) is the probability of symbol xi, and N is the number of
bits to represent a symbol xi. When H(X) = N this ideal entropy value. The results shown in
Tables 2 indicate that our algorithm resists entropy attack.
4.6 NPCR and UACI Tests
NPCR (Number of pixels change rate) and UACI (Unified average changing intensity) are used
in plain image sensitivity tests. NPCR is approaching to 100%, when changing one pixel in the
plain image, resulting in a complete change to the cipher image. Let's take a look at this
example. I1 is a plain original image, we change one pixel to get another plain image I2, the
result after the encryption process is two completely different C1 and C2 ciphered images.
9. Fahmi Nasser Ali & Mussa Mohamed Ahmed
International Journal of Computer Science and Security (IJCSS), Volume (13) : Issue (4) : 2019 143
Arithmetically, the value of the bipolar array D(i,j) can be calculated as follows:
D(i.j) = {
0. if C1(i.j) = C2(i.j)
1. if C1(i.j) โ C2(i.j)
(16)
Then NPCR can be calculated as follows:
NPCR =
1
NรM
โ โ D(i.j) ร 100% (17)N
j=1
M
i=1
plain image sensitivity is better when the NPCR value is close to 100%. UACI measures the
average intensity between the C1 and C2 ciphered images, obtained from plain images I1 and
I2. It can be defined as follows:
UACI =
1
NรM
[โ โ
C1(i.j)โ C2(i.j)
255
N
j=1
M
i=1 ] ร 100% (18)
plain image sensitivity is better when the UACI value is close to 33%. The results shown in
Tables 3.4 indicate that our algorithm resists differential attack.
4.7 Comparison of Various Image Encryption Techniques
Comparison is done on the basis of; direction, plain image and encrypted image of our scheme
with various image encryption algorithms
Image Direction
Plain
image
Encrypted image
Our
scheme
Ref.
[16]
Ref.
[17]
Ref.
[18]
Ref.
[19]
Ref.
[20]
Ref.
[21]
Airplane Horizontal 0.957 0.0075 0.0033 โ0.0002 0.0011 โ0.0164 0.0004 0.0045
Vertical 0.9365 -0.001 0.0002 โ0.0090 โ0.0035 โ0.0181 0.0061 โ0.0215
Diagonal 0.8926 0.0037 โ0.0019 โ0.0066 โ0.0029 0.0004 โ0.0079 โ0.0015
Airport1 Horizontal 0.9099 -0.0018 โ0.0021 โ0.0275 0.004 โ0.0061 0.0094 โ0.0142
Vertical 0.9033 0.0008 0.0002 โ0.0154 โ0.0174 โ0.0079 โ0.0107 โ0.0141
Diagonal 0.859 -0.0006 0.0003 โ0.0187 โ0.0135 โ0.0001 โ0.0007 0.0002
Barbara Horizontal 0.8953 0.0005 0.0033 โ0.0033 โ0.0052 โ0.0212 โ0.0187 0.0037
Vertical 0.9588 -0.0004 0.0032 โ0.0269 โ0.0067 โ0.0161 โ0.0016 โ0.0202
Diagonal 0.883 0.0015 0.0025 โ0.0121 0.0068 โ0.0110 0.0001 0.0046
Boat Horizontal 0.9381 0.0005 0.0025 โ0.0100 โ0.0054 โ0.0189 โ0.0295 โ0.0138
Vertical 0.9713 -0.0026 0.0018 โ0.0124 โ0.0009 0.0003 โ0.0150 โ0.0199
Diagonal 0.9221 0.0022 0.0005 โ0.0185 0.0026 โ0.0204 โ0.0224 โ0.0057
Camera
man
Horizontal 0.9334 0.0035 0.0047 โ0.0095 โ0.0211 0.0063 โ0.0047 โ0.0009
Vertical 0.9592 -0.0002 โ0.0054 โ0.0170 โ0.0103 โ0.0142 โ0.0195 โ0.0223
Diagonal 0.9086 -0.0025 0.0016 โ0.0119 0.0054 0.0168 0.0279 0.0025
Chemical
plant
Horizontal 0.9466 -0.0014 โ0.0030 โ0.0134 โ0.0073 โ0.0069 โ0.0091 0.0072
Vertical 0.8984 0.0025 โ0.0019 โ0.0005 โ0.0073 โ0.0100 โ0.0029 โ0.0015
Diagonal 0.8529 -0.004 โ0.0063 โ0.0033 โ0.0115 โ0.0078 โ0.0092 โ0.0040
Clock Horizontal 0.9564 0.0078 0.0008 0.0024 โ0.0140 โ0.0248 โ0.0143 โ0.0123
Vertical 0.974 0.0059 0.0013 โ0.0246 โ0.0139 โ0.0172 0.0097 โ0.0041
Diagonal 0.9389 0.0017 โ0.009 โ0.0081 โ0.0175 โ0.0025 0.012 โ0.0027
11. Fahmi Nasser Ali & Mussa Mohamed Ahmed
International Journal of Computer Science and Security (IJCSS), Volume (13) : Issue (4) : 2019 145
Image name
Our
scheme
Ref. [16] Ref. [17] Ref. [18] Ref. [19] Ref. [20] Ref. [21]
Airplane 99.62 99.5849 99.615 99.6107 99.5662 99.618 99.6201
Airport 99.5984 99.6459 99.6083 99.6077 99.5565 99.6231 99.5499
Barbara 99.6307 99.6215 99.6092 99.6162 99.5227 99.6402 99.5178
Boat 99.6074 99.6227 99.6102 99.6281 99.5609 99.6209 99.5743
Camera man 99.6154 99.6124 99.6205 99.6292 99.5749 99.6105 99.6216
Chemical plant 99.617 99.62 99.6121 99.6131 99.5325 99.6258 99.6185
Clock 99.62 99.5727 99.6102 99.6218 99.591 99.6126 99.5728
Couple 99.612 99.6185 99.6399 99.6292 99.5606 99.6312 99.5468
Lena 99.5914 99.6253 99.6228 99.6146 99.5511 99.6092 99.6231
Man 99.6057 99.6189 99.607 99.6084 99.5417 99.6211 99.5514
Moon surface 99.6429 99.6276 99.6139 99.6168 99.5684 99.6172 99.6033
Tank 99.6059 99.6307 99.629 99.6131 99.5794 99.6687 99.6078
Average 99.6139 99.61676 99.61650833 99.6174083 99.55882 99.624875 99.58395
TABLE 3: Comparing the NPCR of our algorithm with other algorithms.
Image name
Our
scheme
Ref. [16] Ref. [17] Ref. [18] Ref. [19] Ref. [20] Ref. [21]
Airplane 33.4188 33.6829 33.5625 33.6632 33.3716 33.6183 33.5961
Airport 33.4354 33.4188 33.5359 33.4143 33.4063 33.5817 33.664
Barbara 33.4914 33.4691 33.7431 33.5776 33.389 33.6714 33.5052
Boat 33.4665 33.5137 33.5367 33.6145 33.4176 33.6018 33.3975
Camera man 33.4165 33.6551 33.7786 33.705 33.3691 33.6862 33.7326
Chemical
plant
33.3856 33.4291 33.6068 33.8543 33.3872 33.3825 33.5831
Clock 33.4893 33.4444 33.582 33.4836 33.4147 33.5793 33.9272
Couple 33.522 33.3615 33.8085 33.6446 33.3723 33.7252 33.8911
Lena 33.4699 33.4807 33.7041 33.5561 33.3461 33.6322 33.8144
Man 33.4834 33.4815 33.7905 33.6744 33.3684 33.472 33.6949
Moon surface 33.2323 33.4408 33.6898 33.5333 33.3758 33.6993 33.8063
Tank 33.4714 33.5212 33.9213 33.7155 33.4045 33.5224 33.7415
Average 33.4402 33.49157 33.68831 33.6197 33.3852 33.59769 33.6961
TABLE 4: Comparing the UACI of Our Algorithm with Other Algorithms.
5. CONCLUSION AND FUTURE WORK
This paper supports a set of confusion and diffusion methods, and avoids poor key
management processes to achieve high security. The process of confusion deals with changing
the position of the pixel by applying a chaotic map (the cat). The diffusion process deals with
changing the pixel value by performing a bit-XOR operation between the 2D-STCM and the
confused image. In the algorithm, confusion and diffusion pass through two or more iterations,
in each iteration new key sequences are created by 2D-STCM. Therefore, the total key space
is long enough to resist brute-force attack and increase security level.
12. Fahmi Nasser Ali & Mussa Mohamed Ahmed
International Journal of Computer Science and Security (IJCSS), Volume (13) : Issue (4) : 2019 146
From experimental results the key space analysis, key sensitivity analysis, histogram,
correlation coefficient, information entropy, NPCR and UACI show very good result. As a result,
the encryption algorithm is resistant to attacks such as statistical and differential attacks. The
cipher image has a high security level not only because of confusion and diffusion methods,
but also any key should only be used once. After comparison it is found that in our paper
correlation coefficient was reduced and reach 29% fewer than the results of similar references
in contrast, entropy information is higher than the listed papers (tends to 8) and NPCR is similar
or higher than the listed papers. The obtained results show that the submitted image information
becomes random. Future work will deal with color encryption schemes using different methods
of image fusion.
6. REFERENCES
[1] F. Nasser and M. Mohamed. โImprovement of Images Encryption Based on Multiple
Chaotic Maps and 2D-STCM Key Space,โ presented at the 3rd
Int. Conf. Computer
Science and Engineering Information technology, Aden, Yemen, 2019.
[2] W. Stallings. Cryptography and Network Security Principles and Practice, 4th ed. USA:
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