Image Compression-encryption Scheme Research Articles

A robust image encryption algorithm based on Chua's circuit and compressive sensing

In terms of Chua's circuit system, compressive sensing (CS) and Haar wavelet, a novel image compression-encryption scheme (CES) is proposed in this paper. Firstly, the plaintext image is decomposed into approximate component and detail components through Haar wavelet. Then the approximate component is diffused by the threshold processing of local binary patterns (LBP) operator-based chaotic sequence which is produced by the combination of Chua's circuit and Logistic map. Next, the Lissajous map is applied to generate the chaos-combined asymptotic deterministic random measurement matrices (CADRMM) which are employed to measure the detail components in different compression ratios. In addition, the combination of mapped approximate and detail components is shuffled by the Logistic map. The experimental results and simulation analysis prove that the proposed cryptosystem is capable of reducing data for transmission and has good security performance under various attacks, especially for the shear and noise attacks.

A new image compression-encryption scheme based on compressive sensing and cyclic shift

This paper proposes a digital image compression-encryption scheme based on the theory of compressive sensing and cyclic shift, which use random Gauss matrix and sparse transform to compress the digital image, and then cyclic shift and diffusion operation are developed subsequently to the compressive sensing (CS) phase. The algorithm has three advantages: First, the measurement matrix used in the algorithm is generated by Chebyshev mapping, which increases the key space and reduces the burden of key transmission. Second, The Sigmoid function is used to transform the range of compressed data to 0~255, which are stored as 8-bit binary data, thus further reducing the amount of data transmission and avoiding the expansion of encrypted data. Third, the generation of key stream in encryption phase is related to ciphertext, there are different key streams for different plain images. Thus, our algorithm can resist against the chosen-plaintext and known-plaintext attacks effectively. In addition, the implementation of cyclic shift and diffusion operation further enhances the security of the system. Each pixel of the encrypted image is output in the form of 8-bit integer to facilitate data storage, display and transmission. The experimental results and security analysis show that the algorithm has the advantages of large key space, no obvious statistical characteristics of ciphertext, sensitive to plaintext and key, and able to resist chosen-plaintext attack.

Joint image compression–encryption scheme using entropy coding and compressive sensing

Recently, compressive sensing (CS)-based joint compression–encryption schemes have been widely investigated due to their high efficiency and good security for images. However, the existing schemes typically have a lower compression ratio (CR), and there may be a flaw during their compression processes. Therefore, in this paper, according to the intrinsic features of images, we propose a novel compression architecture to enhance the CR. Meanwhile, based on this architecture, a joint image compression–encryption scheme using entropy coding and CS is designed to implement a complete compression and encryption process. In this joint scheme, a presented bit-level lossless compression–encryption algorithm based on entropy coding for the higher bit-planes is incorporated to improve the quality of the reconstructed image and ensure the security. Alternately, this joint scheme also contains an improved CS-based lossy compression–encryption algorithm for the lower bit-planes, which can guarantee the efficiency and security. Through the cooperation between the proposed lossless and lossy coding, the higher reconstruction performance can be achieved. SHA-256 is combined with all initial keys in the proposed joint scheme to generate the updated keys for chaos cryptosystem to maintain high security and resist some common attacks. Experimental and analytical results illustrate the superiority of the proposed joint scheme compared with the existing compression–encryption schemes and JPEG, as well as good encryption performance.

A Novel Image Compression-Encryption Scheme Based on Chaos and Compression Sensing

In this paper, a novel image compression–encryption hybrid algorithm is proposed. First, a Gauss random matrix and a random scrambling matrix are generated by using Chebyshev mapping and Logistic mapping, respectively. Then, based on the principle that a scrambling Gauss matrix is still a Gauss matrix, a compression scheme for ciphertext images is designed. It is dependent on the Gauss random matrix and the random scrambling matrix, which mainly consists of three parts: the permutation-based encryption using random scrambling matrix by Alice, the encoding with Gauss random matrix by Charlie, and the joint decryption and decoding by Bob. It has a special application scenario, that is, Alice requires semi integrity Charlie to transmit images to Bob through a channel. Experimental results show that the scheme has strong robustness against noise and chosen-plaintext attack, and further the peak signal-to-noise ratio (PSNR) and subjective visual quality of reconstructed images can be improved by comparing with the similar methods.

Image compression-encryption scheme combining 2D compressive sensing with discrete fractional random transform

Most of the existing image encryption algorithms based on compressive sensing are too complex to operate. An image compression-encryption scheme with simple operation is presented based on 2D compressive sensing and discrete fractional random transform (DFrRT). To accomplish compression and encryption simultaneously, the original image is expressed in a 2D discrete cosine domain and measured by the measurement matrices in two orthogonal directions during the encryption process, where the matrices are constructed with Logistic chaos map to control the row vectors of Hadamard matrix. Then the intermediate resulting image is re-encrypted by taking discrete fractional random transform. Decryption process includes the inverse operation of the DFrRT and the reconstruction process with the Newton Smoothed l0 Norm (NSL0) algorithm in sequence. Simulation results verify the security and the effectiveness of this scheme by taking advantages of both compressive sensing and DFrRT.

Image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing

Most image encryption algorithms based on low-dimensional chaos systems bear security risks and suffer encryption data expansion when adopting nonlinear transformation directly. To overcome these weaknesses and reduce the possible transmission burden, an efficient image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing is proposed. The original image is measured by the measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the cycle shift operation controlled by a hyper-chaotic system. Cycle shift operation can change the values of the pixels efficiently. The proposed cryptosystem decreases the volume of data to be transmitted and simplifies the keys distribution simultaneously as a nonlinear encryption system. Simulation results verify the validity and the reliability of the proposed algorithm with acceptable compression and security performance.

Image compression and encryption scheme based on 2D compressive sensing and fractional Mellin transform

Most of the existing image encryption techniques bear security risks for taking linear transform or suffer encryption data expansion for adopting nonlinear transformation directly. To overcome these difficulties, a novel image compression–encryption scheme is proposed by combining 2D compressive sensing with nonlinear fractional Mellin transform. In this scheme, the original image is measured by measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the nonlinear fractional Mellin transform. The measurement matrices are controlled by chaos map. The Newton Smoothed l0 Norm (NSL0) algorithm is adopted to obtain the decryption image. Simulation results verify the validity and the reliability of this scheme.

Improving the compression and encryption of images using FPGA-based cryptosystems

Compression and encryption technologies are important to the efficient solving of network bandwidth and security issues. A novel scheme, called the Image Compression Encryption Scheme (ICES), is presented. It combines the Haar Discrete Wavelet Transform (DWT), Significance-Linked Connected Component Analysis (SLCCA), and the Advance Encryption Standard (AES). Because of above reason the ICES efficiently reduce the overall processing time. This study develops a novel hardware system to compress and encrypt an image in real-time using an image compression encryption scheme. The proposed system exploits parallel processing to increase the throughout of the cryptosystem for Internet multimedia applications to implement the ICES. Using hardware acceleration for encryption and decryption, the FPGA implementation of DWT, SLCCA and the AES algorithm can be used. Using a pipeline structure, a very high data throughput of 330 Mbit/s at a clock frequency of 40 MHz was obtained. Therefore, the ICES is secure, fast and suited to high speed network protocols such as ATM (Asynchronous Transfer Mode), FDDI (Fiber Distributed Data Interface) or Internet multimedia applications.