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Chaotic image encryption algorithm based on arithmetic sequence scrambling model and DNA encoding operation

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Abstract

In this paper, in order to solve the problem of un-rigorous scrambling method and single diffusion method in image encryption, a new method based on arithmetic sequence scrambling model, DNA coding sequence and one-dimensional Logistic mapping is proposed. First, the Hash array is obtained by combining the information of the original plaintext image with the Hash algorithm SHA-512. The Hash array generates the initial value and control parameters of the chaotic system, and then generates the chaotic key flow. Second, the arithmetic progression of partitioned matrix after scrambling, change the position of the original image pixels, replacement and operation, and then for DNA combines images and the chaotic sequence of chaotic system to produce DNA, DNA encoding, decoding rules for operation, change the original data bits of pixel values, thus further destruction of the original information. Finally, a chaotic diffusion operation is performed to further change the pixel information of the original image and output the cipher-text image. The experimental results and various security analyses show that the algorithm has good encryption effect and can resist the common plaintext attack, clipping attack and noise attack. The algorithm can be used for image encryption.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (No: 61672124), the Password Theory Project of the 13th Five-Year Plan National Cryptography Development Fund (No: MMJJ20170203), Liaoning Province Science and Technology Innovation Leading Talents Program Project (No: XLYC1802013), Key R&D Projects of Liaoning Province (No: 2019020105-JH2/103), Jinan City ‘20 universities’ Funding Projects Introducing Innovation Team Program (No: 2019GXRC031).

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Yan, X., Wang, X. & Xian, Y. Chaotic image encryption algorithm based on arithmetic sequence scrambling model and DNA encoding operation. Multimed Tools Appl 80, 10949–10983 (2021). https://doi.org/10.1007/s11042-020-10218-8

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