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Fast image encryption algorithm based on multi-parameter fractal matrix and MPMCML system

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  • Zhao, Hongyu
  • Wang, Shengsheng
  • Wang, Xingyuan

Abstract

This paper studies chaotic image encryption technology and fractal matrix theory, and proposes a synchronous permutation-diffusion encryption algorithm based on multi-parameter fractal matrix. First, this paper proposes a new Multiple Parameter Mixed Coupled Map Lattices system (MPMCML). Then, the concept of fractal matrix is introduced, and the construction process of multi-parameter fractal matrix is given. Finally, based on the multi-parameter fractal matrix, a new synchronous permutation-diffusion image encryption algorithm is proposed. Most importantly, the multi-parameter fractal matrix is very sensitive to the change of parameters, any slight change of parameters will produce a completely different fractal matrix, making the encryption result completely different. Compared with the methods of permutation before diffusion and diffusion before permutation, the synchronous permutation-diffusion method greatly improves the privacy of permutation and diffusion, and improves the anti-attack ability of the algorithm. By analyzing the encryption performance and the ability to resist various attacks, it can be determined that the proposed algorithm exhibits good security characteristics.

Suggested Citation

  • Zhao, Hongyu & Wang, Shengsheng & Wang, Xingyuan, 2022. "Fast image encryption algorithm based on multi-parameter fractal matrix and MPMCML system," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    • Handle: RePEc:eee:chsofr:v:164:y:2022:i:c:s0960077922009213
    DOI: 10.1016/j.chaos.2022.112742
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    References listed on IDEAS

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    1. Khellat, Farhad & Ghaderi, Akashe & Vasegh, Nastaran, 2011. "Li–Yorke chaos and synchronous chaos in a globally nonlocal coupled map lattice," Chaos, Solitons & Fractals, Elsevier, vol. 44(11), pages 934-939.
    2. Zhou, Shuang & Wang, Xingyuan & Zhou, Wenjie & Zhang, Chuan, 2022. "Recognition of the scale-free interval for calculating the correlation dimension using machine learning from chaotic time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    3. Yan, Dengwei & Wang, Lidan & Duan, Shukai & Chen, Jiaojiao & Chen, Jiahao, 2021. "Chaotic Attractors Generated by a Memristor-Based Chaotic System and Julia Fractal," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
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    Cited by:

    1. Lu, Yang & Gong, Mengxin & Gan, Zhihua & Chai, Xiuli & Cao, Lvchen & Wang, Binjie, 2023. "Exploiting one-dimensional improved Chebyshev chaotic system and partitioned diffusion based on the divide-and-conquer principle for 3D medical model encryption," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    2. Ding, Dawei & Wang, Wei & Yang, Zongli & Hu, Yongbing & Wang, Jin & Wang, Mouyuan & Niu, Yan & Zhu, Haifei, 2023. "An n-dimensional modulo chaotic system with expected Lyapunov exponents and its application in image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

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