Most imageencryption 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...
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Most imageencryption 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. (C) 2016 Elsevier Ltd. All rights reserved.
作者：
Zhu, ShuqinZhu, CongxuLiaocheng Univ
Sch Comp & Sci Liaocheng 252059 Shandong Peoples R China Cent S Univ
Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China Hunan Police Acad
Hunan Prov Key Lab Network Invest Technol Changsha 410138 Hunan Peoples R China
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...
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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 similar to 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.
An image cryptosystem using chaotic compressive sensing is designed to achieve simultaneous compression - encryption. Compressive sensing requires a measurement matrix to compressively sample a sparse signal and to gu...
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An image cryptosystem using chaotic compressive sensing is designed to achieve simultaneous compression - encryption. Compressive sensing requires a measurement matrix to compressively sample a sparse signal and to guarantee its recovery at the receiver. In this paper, a new one-dimensional chaotic map is proposed which is used to construct the chaotic measurement matrix. Performance analysis demonstrates that the proposed chaotic map is highly chaotic, ergodic, highly sensitive to the initial conditions and suitable for chaotic compressive sensing. The parameters of the chaotic system are used as the secret key in the construction of measurement matrix and also the masking matrix. The sparse representation of the image is obtained using discrete wavelet transform. The sparse coefficients are then compressively sampled and encrypted using the chaotic measurement matrix and masking matrix. A parallel compressive sensing framework is employed which greatly improves the efficiency of the proposed chaotic compressive sensing scheme. Simulation results shows that the proposed scheme has good security performance against various attacks and better reconstruction performance, when compared with the commonly used random measurement matrix.
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 wi...
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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 4D 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
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