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An image encryption scheme based on multi-objective optimization and block compressed sensing

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Abstract

Visually meaningful image encryption may keep the data security and appearance security of the digital images. However, there are still security and efficiency shortcomings existing in the current algorithms. To solve these problems, we propose an effective visually meaningful color image encryption scheme by combining hybrid multi-objective particle swarm optimization (HMPSO), block compressed sensing (BCS) and Hessenberg decomposition (HD). Firstly, the R, G, B components of color image are segmented averagely and represented sparsely by discrete cosine transform (DCT), respectively. Next, the obtained sparse images are scrambled by the use of zigzag path and measured by BCS to obtain the measurement value matrices. To improve its security, the key associated with the plain image is used as the initial value of the nonlinear chaotic system Henon, and a cross-component dislocation and diffusion strategy are applied to the measurements using the chaotic sequences generated by Henon to obtain the secret image, which enhances the ability of the algorithm to resist chosen-plaintext attack. Subsequently, the secret images are fused into the carrier image by the HD embedding algorithm to generate the final visually meaningful cipher image. In addition, in order to enhance the quality of the reconstructed image and cipher image, HMPSO is implemented to optimize the threshold value of sparse coefficient modification and the embedding rate simultaneously. Simulation results and performance analysis demonstrate the effectiveness, confidentiality and robustness of the proposed scheme.

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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 was supported by the National Natural Science Foundation of China (Grant Nos. 61802111, 61872125), Science and Technology Foundation of Henan Province of China (Grant Nos. 182102210027, 182102410051) and Basic Research Program of Jiangsu Province (Grant No. BK20201290) and the Key Science and Technology Project of Henan Province (Grant Nos. 201300210400, 212102210094).

Funding

National Natural Science Foundation of China (Grant Nos. 61802111, 61872125) and Science and Technology Foundation of Henan Province of China (Grant Nos. 182102210027, 182102410051) and Basic Research Program of Jiangsu Province (Grant No. BK20201290) and the Key Science and Technology Project of Henan Province (Grant Nos. 201300210400, 212102210094).

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Authors and Affiliations

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

    Xiuli Chai, Jiangyu Fu & Yang Lu

  2. School of Software, Intelligent Data Processing Engineering Research Center of Henan Province, Institute of Intelligent Network System, Henan University, Kaifeng, 475004, China

    Zhihua Gan

  3. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics and the Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 211106, China

    Yushu Zhang

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  1. Xiuli Chai
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  4. Yang Lu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xiuli Chai, Jiangyu Fu, Zhihua Gan, Yang Lu and Yushu Zhang. The first draft of the manuscript was written by Xiuli Chai, Jiangyu Fu and Zhihua Gan. All authors commented on this version of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Zhihua Gan.

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Chai, X., Fu, J., Gan, Z. et al. An image encryption scheme based on multi-objective optimization and block compressed sensing. Nonlinear Dyn 108, 2671–2704 (2022). https://doi.org/10.1007/s11071-022-07328-3

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