Image Compression-encryption Scheme Research Articles

An efficient image compression encryption scheme based on BP neural network and DNA mutation

In this paper, an efficient image compression encryption scheme is proposed. Firstly, compressing the image by using BP neural network before encryption, which not only saves the subsequent encryption and decryption time but also minimizes storage and transmission space. Secondly, Chialvo neuron model is used to generate chaotic sequences for the encryption process in the scheme, confusing the compressed image according to a confusion algorithm combining single-plane and cross-plane. Next, diffusing the confused image. Subsequently, the diffused image and chaotic sequences are DNA encoded, performing DNA same or operation (DNA mutual mutations) and DNA mutations (DNA self-mutations). Finally, the DNA mutations results are decoded to obtain the cipher image. Security tests and compression performance analysis show that the scheme can efficiently and securely compress and encrypt images of different sizes with higher reconstruction quality compared to other compression schemes.

An image compression-encryption scheme based on compressive sensing and hyperchaotic system

An image compression-encryption scheme based on compressive sensing and hyperchaotic system

An image compression encryption scheme based on chaos and SPECK-DCT hybrid coding

An image compression encryption scheme based on chaos and SPECK-DCT hybrid coding

High-quality color image compression-encryption using chaos and block permutation

This paper proposes a high-quality color image compression-encryption scheme based on chaos and block permutation. In this scheme, the color digital image is first converted and sampled in the YCbCr gamut, and then the coefficients in the sub-blocks are extracted for compression coding after 8×8 post-blocking discrete cosine transformation (DCT) to the frequency domain. Then, an image encryption algorithm using chaos-based block permutation and two-round row-column diffusion is designed for the compressed frequency domain information. In terms of security, a chaos-based block permutation module is designed, which includes the encryption operations of block scrambling, block rotation and inversion, pixel value complement and color component interchange to enhance security. In addition, a mechanism for associating plaintext and intermediate ciphertext is introduced to generate dynamic chaotic sequences to effectively resist various cryptographic attacks. Moreover, the joint compression-encryption reduces the computational complexity of the encrypted object significantly and improves the efficiency. The simulation results show that the joint compression-encryption scheme has the advantages of high compression ratio and high image recovery quality, and has a fairly high security level to resist common cryptographic attacks. Therefore, the compression-encryption scheme proposed in this paper is a preferred and promising method for protecting the privacy of digital images.

A novel image compression and encryption scheme based on conservative chaotic system and DNA method

An image compression encryption scheme on the strength of a conservative chaotic map, compressed sensing (CS) and DNA method is proposed. Built on the hash algorithm, a novel algorithm for generating the initial state of the chaotic map is designed, which makes the algorithm resistant to chosen-plaintext and known-plaintext attacks. The pseudo-random sequences which are produced by the conservative chaotic map are devoted to constructing the measurement matrix of CS and control the encoding and operation mode of DNA, which improves the complexity of the cryptosystem. In addition, a novel Integrated Cyclic Crossover and Mutation algorithm (ICCM) based on DNA method is proposed. ICCM simultaneously completes the scrambling and diffusion of images based on the loop structure, which greatly enhances the security of the algorithm. Experimental results and a number of security analyses demonstrate that the scheme has excellent performance and robustness, and has good resistance to various attacks.

A new image compression-encryption scheme based on compressive sensing & classical AES algorithm

A new image compression-encryption scheme based on compressive sensing & classical AES algorithm

A new fast image compression–encryption scheme based on compressive sensing and parallel blocks

A new fast image compression–encryption scheme based on compressive sensing and parallel blocks

Secure image block compressive sensing using complex Hadamard measurement matrix and bit‐level XOR

This paper proposes a novel image compression-encryption scheme based on compressive sensing and bit-level XOR. In the proposed scheme, the encrypted sequences are generated from a 4-D hyper-chaotic Lorenz system and a Logistic system. First, an original image is sampled by a secure block parallel compressive sensing (PCS) scheme. In the PCS phase, a key-controlled discrete cosine transform sparse basis matrix and a key-controlled complex Hadamard measurement matrix are employed. Next, the real part and imaginary part of the resulting complex-valued data are quantized and transformed into bit streams, respectively. After that, the two generated bit streams are combined into one bit stream. Then, the resulting bit stream is further encrypted by a bit-level XOR operation, where the encrypted key is generated from a chaotic system. The experiment results show the effectiveness and reliability of the proposed joint compression-encryption scheme. The proposed scheme can not only enhance the security of the compressed image but also improve the reconstructed quality of the compressed image.

Image Encryption Scheme Based on Mixed Chaotic Bernoulli Measurement Matrix Block Compressive Sensing.

Many image encryption schemes based on compressive sensing have poor reconstructed image quality when the compression ratio is low, as well as difficulty in hardware implementation. To address these problems, we propose an image encryption algorithm based on the mixed chaotic Bernoulli measurement matrix block compressive sensing. A new chaotic measurement matrix was designed using the Chebyshev map and logistic map; the image was compressed in blocks to obtain the measurement values. Still, using the Chebyshev map and logistic map to generate encrypted sequences, the measurement values were encrypted by no repetitive scrambling as well as a two-way diffusion algorithm based on GF(257) for the measurement value matrix. The security of the encryption system was further improved by generating the Secure Hash Algorithm-256 of the original image to calculate the initial values of the chaotic mappings for the encryption process. The scheme uses two one-dimensional maps and is easier to implement in hardware. Simulation and performance analysis showed that the proposed image compression–encryption scheme can improve the peak signal-to-noise ratio of the reconstructed image with a low compression ratio and has good encryption against various attacks.

Hybrid image compression-encryption scheme based on multilayer stacked autoencoder and logistic map

Secure transmission of images over a communication channel, with limited data transfer capacity, possesses compression and encryption schemes. A deep learning based hybrid image compression-encryption scheme is proposed by combining stacked auto-encoder with the logistic map. The proposed structure of stacked autoencoder has seven multiple layers, and back propagation algorithm is intended to extend vector portrayal of information into lower vector space. The randomly generated key is used to set initial conditions and control parameters of logistic map. Subsequently, compressed image is encrypted by substituting and scrambling of pixel sequences using key stream sequences generated from logistic map. The proposed algorithms are experimentally tested over five standard grayscale images. Compression and encryption efficiency of proposed algorithms are evaluated and analyzed based on peak signal to noise ratio (PSNR), mean square error (MSE), structural similarity index metrics (SSIM) and statistical, differential, entropy analysis respectively. Simulation results show that proposed algorithms provide high quality reconstructed images with excellent levels of security during transmission.

Image compression–encryption scheme using SPIHT and chaotic systems

The world is moving towards digitization for all forms of data. Images are one of the most used data that contains a lot of information. With the increasing network technology, the sharing of data across the internet has also manifold. Certain images are confidential and require security for storing or transmitting on an insecure communication channel. Digital images usually have highly correlated data where compression can be performed to reduce the load on the transmission channel or storage memory. The paper proposes an algorithm to compress the digital images and encryption is performed on the compressed data to provide security. Compression of the image is performed using the Set partitioning in hierarchical trees algorithm. The compressed image is encrypted using a chaotic sequence generated using the Henon map and the cipher image is scrambled using a permutation table obtained from Piecewise Linear chaotic map. To reduce the computation while generating the chaotic sequence, a base conversion operation is used. The initial parameters of the chaotic systems are generated from the elliptic curve point are shared securely using ElGamal based elliptic curve cryptosystem. Security analyses are conducted along with a comparison with other existing systems. The proposed algorithm is secured against various attacks and outperforms existing techniques concerning various statistical and security aspects of an enciphering algorithm.

Image Encryption Scheme with Compressed Sensing Based on a New Six-Dimensional Non-Degenerate Discrete Hyperchaotic System and Plaintext-Related Scrambling.

Digital images can be large in size and contain sensitive information that needs protection. Compression using compressed sensing performs well, but the measurement matrix directly affects the signal compression and reconstruction performance. The good cryptographic characteristics of chaotic systems mean that using one to construct the measurement matrix has obvious advantages. However, existing low-dimensional chaotic systems have low complexity and generate sequences with poor randomness. Hence, a new six-dimensional non-degenerate discrete hyperchaotic system with six positive Lyapunov exponents is proposed in this paper. Using this chaotic system to design the measurement matrix can improve the performance of image compression and reconstruction. Because image encryption using compressed sensing cannot resist known- and chosen-plaintext attacks, the chaotic system proposed in this paper is introduced into the compressed sensing encryption framework. A scrambling algorithm and two-way diffusion algorithm for the plaintext are used to encrypt the measured value matrix. The security of the encryption system is further improved by generating the SHA-256 value of the original image to calculate the initial conditions of the chaotic map. A simulation and performance analysis shows that the proposed image compression-encryption scheme has high compression and reconstruction performance and the ability to resist known- and chosen-plaintext attacks.

Secure and Efficient Image Compression-Encryption Scheme Using New Chaotic Structure and Compressive Sensing

The rapid development of the Internet leads to a surge in the amount of information transmission and brings many security problems. For multimedia information transmission, especially digital images, it is necessary to compress and encrypt at the same time. The emergence of compressive sensing solves this problem. Compressive sensing can compress and encrypt at the same time, which can not only reduce the transmission bandwidth of the network but also improve the security of the system. However, when using compressive sensing encryption, the whole measurement matrix needs to be stored, and the compressive sensing can be combined with a chaotic system, so only the generation parameters of the matrix need to be stored, and the security of the system can be further improved by using the sensitivity of the chaotic system. This paper introduces a secure and efficient image compression-encryption scheme using a new chaotic structure and compressive sensing. The chaotic map used in the scheme is generated by our new and universal chaotic structure, which not only expands the chaotic range of the chaotic system but also improves the performance of the chaotic system. After analyzing the performance comparison of traditional one-dimensional chaotic maps and some existing methods, the image compression-encryption scheme based on a new chaotic structure and compressive sensing has a good encryption effect and large keyspace, which can resist brute force attack and statistical attack.

Double image compression-encryption algorithm based on fractional order hyper chaotic system and DNA approach

In order to improve the efficiency and security of image compression-encryption algorithms, we propose a double image compression-encryption scheme based on fractional hyper-chaotic system and DNA approach. Firstly, two images are processed by discrete cosine transform. Secondly, the spectrums of the two images are sorted by Z-scan, so that the two images can be compressed and mixed into a new image. Finally, the resulting image is encrypted by using DNA coding. Different from traditional image encryption algorithms, the proposed algorithm provides a variety of DNA coding and operation modes. Chaotic sequences are used to control the coding and operation mode in order to improve the complexity of the encryption process. Fractional order and initial values of fractional order hyper-chaotic system are used as the key of the proposed algorithm, which greatly expands the complexity and the key space of the scheme. In the proposed scheme, by mixing two images and performing a compression-encryption operation on them simultaneously, the proposed algorithm can improve the complexity of encrypted images while providing good confusion. Experimental results and security analysis show that the proposed algorithm can effectively resist multiple attacks.

A secure image encryption scheme with compression-confusion-diffusion structure

This paper presents a novel image compression-encryption scheme, which has the compression-confusion-diffusion Structure. Firstly, based on Chebyshev chaotic map, a Gauss measurement matrix is constructed and optimized, which is applied to compressive sensing. Then, an image compression-encryption algorithm is proposed by using a six-dimensional discrete chaotic map. In the proposed scheme, the original image is transformed into a sparse coefficient matrix by discrete wavelet transform, and the sparse coefficients are measured by using the optimized Gauss measurement matrix to get the measured values. Then, the measured values are quantized into integer values and the compressed image is obtained. Furtherly, the compressed image is encrypted by using a six-dimensional chaotic map. In the process of encryption, the plaintext image is divided into two parts, when encrypting the second part, the first part is used as part of the key. While encrypting the first part, the ciphertext of the second part is used as part of the key. Thus, the algorithm has strong confusion and diffusion effect and makes ciphertext sensitive to plaintext. Experimental results such as effects of compression-encryption, key space analysis, key sensitivity analysis, differential analysis, histograms analysis, information entropy analysis, and correlation coefficients analysis show that the proposed scheme is secure and has high application potential.

Image Compression-Encryption Scheme via Fiber Specklegram-Based Compressive Sensing and Double Random Phase Encoding

An image compression-encryption scheme utilizing the combination of fiber specklegram-based compressive sensing (CS) and double random phase encoding (DRPE) was demonstrated. The original image is compressed and encrypted by CS and then re-encrypted by DRPE. The measurement matrices for CS are constructed from multimode fiber specklegrams resulting from modes excitation and interference, which show better performance compared with those based on Gaussian random variables. Since fiber specklegrams have strong dependence on configuration, such measurement matrix can be constructed either by one specklegram or by specklegrams from different launched wavelengths. The double-encrypted image demonstrated good safety and reliability in several common attacks and presented high key sensitivity, which indicates good performance of the proposed scheme.

A novel compressive sensing-based framework for image compression-encryption with S-box

In this paper, we find that compressive sensing (CS) with the chaotic measurement matrix has a strong sensitivity to plaintext. Because of the quantification executed after CS, however, the plaintext sensitivity produced by CS may be weakened greatly. Thus, we propose a novel CS-based compression-encryption framework (CS-CEF) using the intrinsic property of CS to provide a strong plaintext sensitivity for the compression-encryption scheme, which takes a low additional computation cost. Meanwhile, a simple and efficient Substitution box (S-box) construction algorithm (SbCA) based on chaos is designed. Compared with the existing S-box construction methods, the simulation results prove that the proposed S-box has stronger cryptographic characteristics. Based on the above works, we develop an efficient and secure image compression-encryption scheme using S-box (CSb-CES) under the proposed CS-CEF. The simulations and security analysis illustrate that the proposed CSb-CES has the higher efficiency and security compared with the several state-of-the-art CS-based compression-encryption schemes.

An effective image compression–encryption scheme based on compressive sensing (CS) and game of life (GOL)

At present, information entropies of cipher images gotten by some CS-based image cryptosystems are lower than 7, which make them vulnerable to entropy attack. To cope with this problem, we propose a novel image compression–encryption method based on compressive sensing (CS) and game of life (GOL). Encryption architecture of permutation, compression and diffusion is utilized. Firstly, a plaintext-dependent game-of-life-based scrambling method is presented to shuffle the sparse coefficient matrix of plain image, and the permutation matrix is constructed by rules of GOL, which may effectively reduce the adjacent pixel correlation and enhance the scrambling effect. Secondly, the confused matrix is compressed by CS and diffused using a key matrix to get the cipher image. Additionally, a five-dimensional (5D) memristive hyperchaotic system is used to generate chaotic sequences. They are utilized to construct measurement matrix, to generate initial cell matrix of GOL and to produce key matrix. Information entropy of plain image and external key parameters are combined to compute initial values of the hyperchaotic system. Therefore, our algorithm has high sensitivity to original image and it may resist against known-plaintext attack and chosen-plaintext attack. Experimental results and performance analyses demonstrate that the proposed encryption algorithm is effective to withstand various typical attacks, and it may be applied for image secure communication.

Novel image compression–encryption hybrid scheme based on DNA encoding and compressive sensing

A novel and effective image compression–encryption scheme based on deoxyribonucleic acid (DNA)-encoding theory and compressed sensing (CS) theory is proposed. The logistic map is applied to control key image and measurement matrices, and to control DNA encoding and decoding rules used to encode and decode each row of the plain image and the key image. The plain image first forms a disordered image through DNA encryption stage, sorts it to obtain an ordered image through sorting stage, and then passes the ordered image through CS encryption stage to form a cipher image. Simulation results and attack analysis verify the validity and feasibility of the encryption scheme, whose compression and security is acceptable.

Image compression-encryption scheme based on fractional order hyper-chaotic systems combined with 2D compressed sensing and DNA encoding

An image compression-encryption scheme is proposed based on fractional order hyper-chaotic systems combined with 2D compressed sensing (CS) and DNA encoding. This scheme adopts the CS technique to cut down the size of the original image proportionally in the encryption process and the construction parameters of the CS measurement matrix are controlled by fractional order hyper-chaotic systems. The chaotic sequences generated by fractional order hyper-chaotic systems are used to dynamically control DNA encoding and DNA operation mode to improve the complexity of encryption process. The fractional order and initial value of the fractional hyper-chaotic system are used as the key of the encryption scheme, which greatly expands the key space of the proposed scheme. The proposed scheme maximizes the utilization of chaotic sequences generated by fractional order hyper-chaotic systems, and the chaotic sequences fit perfectly with the whole process of the scheme. The experimental results and security analysis show the security of the scheme against multiple attacks.