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Image encryption using memristive hyperchaos

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Abstract

In this paper, a novel image encryption algorithm (DMHM-IEA) based on a two-dimensional discrete memristive hyperchaotic map (2D-DMHM) derived from discrete memristor and improved Logistic map is proposed. The thorough performance investigation demonstrates that adding the memristor enhances the complicated dynamic characteristics and sequence unpredictability of the proposed map, making it more appropriate for usage in image encryption applications. Diffusion-permutation-diffusion is an inventive structure that DMHM-IEA uses to provide great encryption effect with just one round of encryption. In the random position permutation, pixel positions are quickly and arbitrarily changed to new positions, while the diffusion process uses main-diagonal diffusion and counter-diagonal diffusion to spread out the minor changes in the original image throughout the entire plane, resulting in a highly distinct encrypted image. The combined performance and security evaluation demonstrates that DMHM-IEA has strong tolerance to noise and data loss as well as good resistance to potential attacks. When compared to other cutting-edge algorithms, the proposed algorithm performs well in terms of security.

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All data generated or analysed during this study are included in this published article.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61961019, the Key Research and Development Program of Jiangxi Province of China under Grant 20181BBE50017, and the Youth Key Project of Natural Science Foundation of Jiangxi Province of China under Grant 20202ACBL212003.

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All authors contributed to the study and writing of the manuscript. Conceptualization, methodology, investigation, data collection and analysis were performed by Qiang Lai and Yuan Liu. Visualization and Formal analysis were perform by Yuan Liu and Liang Yang.

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Correspondence to Qiang Lai.

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Lai, Q., Liu, Y. & Yang, L. Image encryption using memristive hyperchaos. Appl Intell 53, 22863–22881 (2023). https://doi.org/10.1007/s10489-023-04727-w

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