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

  • Xiuli Chai
  • Xianglong Fu
  • Zhihua Gan
  • Yushu Zhang
  • Yang Lu
  • Yiran Chen
Original Article
  • 55 Downloads

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.

Keywords

Image encryption Elementary cellular automata (ECA) Block compressive sensing (BCS) Chaos 

Notes

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).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Big Data Analysis and ProcessingHenan UniversityKaifengChina
  2. 2.School of SoftwareHenan UniversityKaifengChina
  3. 3.School of Information TechnologyDeakin UniversityBurwoodAustralia
  4. 4.Academy of Science and TechnologyHenan UniversityKaifengChina
  5. 5.Department of Electrical and Computer EngineeringDuke UniversityDurhamUSA

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