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An efficient chaos-based image compression and encryption scheme using block compressive sensing and elementary cellular automata

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Abstract

In this paper, an efficient image compression and encryption scheme combining the parameter-varying chaotic system, elementary cellular automata (ECA) and block compressive sensing (BCS) is presented. The architecture of permutation, compression and re-permutation is adopted. Firstly, the plain image is transformed by DWT, and four block matrices are gotten, and they are a low-frequency block with important information and three high-frequency blocks with less important information. Secondly, ECA is used to scramble the four sparse block matrices, which can effectively change the position of the elements in the matrices and upgrade the confusion effect of the algorithm. Thirdly, according to the importance of each block, BCS is adopted to compress and encrypt four scrambled matrices with different compression ratios. In the BCS, the measurement matrices are constructed by a parameter-varying chaotic system, and thus few parameters may produce the large measurement matrices, which may effectively reduce memory space and transmission bandwidth. Finally, the four compressed matrices are recombined into a large matrix, and the cipher image is obtained by re-scrambling it. Moreover, the initial values of the chaotic system are produced by the SHA 256 hash value of the plain image, which makes the proposed encryption algorithm highly sensitive to the original image. Experimental results and performance analyses demonstrate its good security and robustness.

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Acknowledgements

All the authors are deeply grateful to the editors for smooth and fast handling of the manuscript. The authors would also like to thank the anonymous referees for their valuable suggestions to improve the quality of this paper. This work is supported by the National Natural Science Foundation of China (Grant Nos. 41571417, U1604145, 61802111, 61872125, 61871175), National Science Foundation of the United States (Grant Nos. CNS-1253424 and ECCS-1202225), Science and Technology Foundation of Henan Province of China (Grant Nos. 182102210027, 182102410051), China Postdoctoral Science Foundation (Grant Nos. 2018T110723, 2016M602235), Key Scientific Research Projects for Colleges and Universities of Henan Province (Grant No. 19A413001), CERNET NGI Technology Innovation Project (Grant No. NGII20170902) and the Research Foundation of Henan University (Grant No. xxjc20140006).

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Authors and Affiliations

  1. Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, 475004, China

    Xiuli Chai, Xianglong Fu & Yang Lu

  2. School of Software, Henan University, Kaifeng, 475004, China

    Zhihua Gan

  3. School of Information Technology, Deakin University, Burwood, VIC, 3125, Australia

    Yushu Zhang

  4. Academy of Science and Technology, Henan University, Kaifeng, 475004, China

    Yang Lu

  5. Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27708, USA

    Yiran Chen

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  1. Xiuli Chai
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Correspondence to Yang Lu.

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Chai, X., Fu, X., Gan, Z. et al. An efficient chaos-based image compression and encryption scheme using block compressive sensing and elementary cellular automata. Neural Comput & Applic 32, 4961–4988 (2020). https://doi.org/10.1007/s00521-018-3913-3

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