Abstract
In this paper, we design a color image encryption algorithm based on chaotic system and block compressive sensing. Firstly, the sparse representation of the plain-image is obtained by an adaptive learning dictionary. Secondly, the key streams are produced from two excellent low-dimensional chaotic maps, where updating the initial values and parameters rely on the SHA-384 and the input image. Thirdly, three measurement matrices of R, G, B components are constructed from the chaotic sequences, respectively. Finally, a random rows and columns diffusion method is performed on the encrypted image. Experimental results and safety analysis prove that the proposed scheme has excellent performance.
Highlights
Compressive Sensing (CS) theory is one of the methods of digital images encryption, which can achieve compression and encryption simultaneously
In [1], a CS image encryption algorithm was proposed, where the measurement matrix is constructed via logistic map
Gong et al [2] put forward an image compression and encryption algorithm based on chaotic system, which had a good ability on resistance the known plaintext attacks
Summary
Compressive Sensing (CS) theory is one of the methods of digital images encryption, which can achieve compression and encryption simultaneously. In [1], a CS image encryption algorithm was proposed, where the measurement matrix is constructed via logistic map. The measurement matrix was generated from low-dimensional chaotic systems with the simple structures, which greatly reduce the security and the sensitive of the algorithms To solve this problem, highdimensional chaotic maps were applied in the image encryption methods [3,4,5]. Xu et al [5] applied a hyper-chaotic system to encrypt image, which achieved an acceptable compression effects. These complex chaotic maps increased the computation complexity, and the constructed measurement matrices are single, which would reduce the security and sensibility of cryptosystem. Numerical experiments have verified the feasibility and validity of the proposed algorithm
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