Compression-encryption Scheme Research Articles

Multi-medical image protection: compression–encryption scheme based on TLNN and mask cubes

Multi-medical image protection: compression–encryption scheme based on TLNN and mask cubes

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

Image privacy protection scheme based on high-quality reconstruction DCT compression and nonlinear dynamics

The protection of digital image privacy in Big Data environment is a hot issue of increasing concern. To address the contradiction between efficiency and security for privacy protection, this paper proposes a high-quality restored image privacy protection scheme based on Discrete Cosine Transform (DCT) frequency domain compression and nonlinear dynamics. First, the RGB space of the color image is converted to YCbCr domain with 4:2:0 sampling. Then, the image is divided into sub-blocks in the spatial domain, followed by block-wise DCT and quantization into frequency domain information. Next, both Alternating Current (AC) and Direct Current (DC) coefficients of all sub-blocks are individually extracted and subjected to compression encoding. Finally, a Rubik’s Cube permutation and filtering diffusion encryption algorithm based on a nonlinear dynamical chaotic system is designed. Moreover, a mechanism for generating dynamic chaotic sequences associated with plaintext is introduced to effectively counter cryptographic attacks. The combined approach of compression and encryption significantly reduces computational complexity while improving encryption efficiency. Through experimental simulation results, the compression encryption scheme demonstrates high compression ratios as well as excellent image recovery quality while providing enhanced security against common cryptographic attacks. The work in this paper provides a preferred joint compression and encryption technical solution for digital image privacy protection in Big Data network.

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

Optimal algorithm for color medical encryption and compression images based on DNA coding and a hyperchaotic system in the moments

Currently, visual data security plays a significant role in various fields, especially in medical imaging. Addressing the challenges associated with limited key space and vulnerability to different types of attacks within current encryption schemes, this work proposes an optimal compression-encryption scheme for large medical images that incorporates elements of Archimedes' optimization algorithm, discrete orthogonal Hahn moments, chaotic systems, and DNA coding. The primary aim of this study is to develop an optimal and exceptionally resilient compression-encryption scheme capable of countering various attack types effectively. This approach is structured into three principal phases: a compression phase harnessing the efficiencies of Hahn's discrete orthogonal moments (HMs) in signal and image representation, coupled with the Archimedes optimization algorithm (AO) to ensure optimal tuning of polynomial parameters (a, b) for superior image reconstruction quality. The encryption phase is performed on the compressed image, using hyperchaotic memristive 4-D (HCM-4D), adapted logistics map (ALM) and DNA coding. Initially, the adapted logistics map is responsible for generating random sequences linked to the compressed image. Subsequently, chaotic sequences originating from the hyperchaotic 4-D memristive system govern both random sequences and DNA processes. The optimization phase, facilitated by the AO algorithm, focuses on minimizing the value of the objective function (correlation) on the compressed and encrypted images. Ultimately, the image with the lowest correlation value is designated as the optimal compressed-encrypted representation.The simulation results clearly illustrate the resilience of the AO algorithm when juxtaposed with other optimization algorithms, especially with respect to convergence speed and computational efficiency. On the other hand, the proposed compression approach demonstrated exceptional efficiency in compressing medical images, offering us the possibility to achieve impressive compression ratio (CR) as well as exceptional quality in decompressed images, evident thanks to high PSNR values. In addition, security analyze demonstrate that the proposed compression-encryption benefits from a larger key space and superior resistance against different types of attacks. Furthermore, our approach was subjected to a comparative analysis alongside various encryption method. These comparisons demonstrate that our encryption algorithm surpasses others in terms of both security and effectiveness.

Multi-color-image compression-encryption scheme in the quaternion discrete Fresnel domain

A multiple color images compression-encryption scheme is designed with compressive sensing in the quaternion discrete Fresnel transform. To tackle multiple color images in a holistic manner, the discrete Fresnel transform is extended into the quaternion domain and the images are encrypted with the quaternion discrete Fresnel transform. In this scheme, the RGB color components of plaintext images are simultaneously compressed and encrypted in three mutually independent channels. Then the red, green and blue components are scrambled respectively by a chaos sequence generated by the 2D logistic-sine-coupling map. Each color component matrix is compressed with sparse representation and matrix measurement. Subsequently, the compressed matrices are integrated into the quaternion algebras and re-encrypted by the defined quaternion discrete Fresnel transform. The devised nonlinear cryptosystem originates from the asymmetric phase truncation operation. In decryption, the original color images are reconstructed by the gradient descent with a sparsification algorithm. The proposed multiple color images compression-encryption algorithm is feasible, effective, secure and robust.

CESCAL: A joint compression-encryption scheme based on convolutional autoencoder and logistic map

CESCAL: A joint compression-encryption scheme based on convolutional autoencoder and logistic map

Color-Gray Multi-Image Hybrid Compression-Encryption Scheme Based on BP Neural Network and Knight Tour.

In the research of multi-image encryption (MIE), the image type and size are important factors that limit the algorithm design. For this reason, the multi-image (MI) hybrid encryption algorithm that can flexibly encrypt color images and grayscale images of various sizes is proposed. Based on this, combining the back propagation (BP) neural network compression technology and the MI hybrid encryption algorithm, an MI hybrid compression-encryption (MIHCE) scheme can be obtained to reduce the pressure of simultaneous transmission and storage of multiple cipher images. Besides, two chaotic maps are used in the scheme design process. By plotting the phase diagrams under different parameter conditions, the rich variation of the behavior of the chaotic maps in the phase space is exhibited. The MIHCE scheme based on the chaotic maps consists of three parts: 1) compressing the MI cube by using the BP neural network; 2) scrambling the compressed MI cube based on the knight tour problem and chaotic sequences; and 3) diffusing the scrambled MI cube. After the MIHCE is completed, the obtained cipher images are stored and transmitted. Subsequently, the security analysis and compression performance analysis prove the feasibility and safety of the designed compression-encryption scheme.

Novel Duffing chaotic oscillator and its application to privacy data protection

Traditional Compressive Sensing (CS) achieves both compression and encryption of digital data. However, most existing compressive sensing methods present some shortcomings, including weak resistance to chosen-plaintext attacks and heavy key management burden. To overcome these shortcomings, this work presents a new combination of CS with optical transformation for digital image compression and encryption. The proposed compression-encryption scheme utilizes the interesting properties of CS and permutation-diffusion techniques to reduce the image size and encrypt the image data. A Novel Duffing Oscillator (NDO) is proposed, its dynamics is deeply analyzed, and its sequences are exploited to build a hardware-friendly measurement matrix for the CS process. This also contributes to reducing the total size of secret key sent to the receiving end. In addition, the final image compression-encryption output is obtained by applying one of the most significant optical encryption methods, namely Double Random Phase Encoding (DRPE). This contributes to further strengthen the security of the proposed scheme. Eventually, the experimental results imply that our scheme is effective in improving the resistance against various attacks, while guaranteeing good imperceptibility and reconstruction performance. It can then be employed in the information security communication field.

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.

An image compression encryption based on the semi-tensor product and the DFT measurement matrix

As the volume of image data increases, optical image encryption algorithms often require more time and space to operate. To solve the problem, compression is generally used, and compressed sensing algorithms are among the most frequently used compression algorithms recently. However, most existing compressed sensing algorithms suffer from low reconstruction quality and large measurement matrix size. To address this issue, we proposed a novel compression encryption scheme using the semi-tensor product, DFT (discrete Fourier transform) measurement matrix, and cascaded diffraction system. When the amount of image data grows, the key space grows according to the factorial growth rate without an upper limit, making brute-force attacks virtually impossible. Compared with algorithms without using a semi-tensor product, the storage space shrinks to one-fourth of its original size. Experiment results proved better reconstruction quality compared with existing compressed sensing algorithms. Furthermore, our approach is robust against various attacks, which is validated through rigorous testing.

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.

Development of an efficient real-time H.264/AVC advanced video compression encryption scheme

Multimedia is the combination of media such as text, graphics, video clips, and audio files. In today’s world, multimedia plays an important role in many applications that we use in our daily lives. It is used in educational software, animation, sound, and text, as well as multi-media software. H.264/AVC video compression is extremely efficient in terms of compression. Despite this, H.264/AVC requires a lot of processing and consumes a lot of power insdespite of the fact that its compression efficiency is lower than that of H.264/AVC. We examine the various methods of Video H.264 Advanced Video Compression Standard Encryption Schemes in this paper. The performance of all types of encryption techniques will be evaluated using parameters such as cost overhead, delay, and encryption quality. This will provide us with a detailed comparative analysis of video encryption schemes, allowing us to determine which one is far more efficient for H.264/AVC.

An efficient multimedia compression-encryption scheme using latin squares for securing Internet-of-things networks

The security of multimedia content and its applications have become a a very important task in digital communication technology. The extensive use of multimedia content over wireless networks makes the network applications exposed to different types of attacks. Cryptography is one of the main technologies that provide security and confidentiality to prevent unauthorised data access. A suitable design of an encryption system must be chosen to fully secure and maintain the privacy of data. On the other hand, the large size of data will be the prominent issue. In order to maintain a better performance of Internet-of-Things (IoT) applications, an appropriate compression-encryption scheme has to be used to ensure security and high speed without increasing the cryptosystem complexity in order to achieve a large communication capacity. Therefore, this paper presents a hybrid scheme of H.264/MVC compression and encryption process based on Latin Square Cipher (LSC) using symmetric keys. The Latin Square Whitening and Latin Square Substitution and Permutation operations are included in the encryption algorithm. The simulation results and analysis on different RGB video frames using MATLAB show that the proposed scheme achieves high protection, integrity, and security performance for different transmitted video frames. In addition, high robustness against different network attacks is achieved compared to the state-of-the-art algorithms.