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A Chaotic Image Encryption Method Based on the Artificial Fish Swarms Algorithm and the DNA Coding

Author

Listed:
  • Yue Zhu

    (College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China)

  • Chunhua Wang

    (College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China)

  • Jingru Sun

    (College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China)

  • Fei Yu

    (School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China)

Abstract

Aiming at the problems of small key space and weak resistance to differential attacks in existing encryption algorithms, we proposed a chaotic digital image encryption scheme based on an optimized artificial fish swarm algorithm and DNA coding. First, the key is associated with the ordinary image pixel through the MD5 hash operation, and the hash value generated by the ordinary image is used as the initial value of the hyper-chaotic system to increase the sensitivity of the key. Next, the artificial fish school algorithm is used to scramble the positions of pixels in the block. In addition, scrambling operation between blocks is proposed to increase the scrambling effect. In the diffusion stage, operations are performed based on DNA encoding, obfuscation, and decoding technologies to obtain encrypted images. The research results show that the optimized artificial fish swarm algorithm has good convergence and can obtain the global optimal solution to the greatest extent. In addition, simulation experiments and security analysis show that compared with other encryption schemes, the scheme proposed in this paper has a larger key space and better resistance to differential attacks, indicating that the proposed algorithm has better encryption performance and higher security.

Suggested Citation

  • Yue Zhu & Chunhua Wang & Jingru Sun & Fei Yu, 2023. "A Chaotic Image Encryption Method Based on the Artificial Fish Swarms Algorithm and the DNA Coding," Mathematics, MDPI, vol. 11(3), pages 1-18, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:767-:d:1056493
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    References listed on IDEAS

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    1. Minglin Ma & Kangling Xiong & Zhijun Li & Yichuang Sun, 2023. "Dynamic Behavior Analysis and Synchronization of Memristor-Coupled Heterogeneous Discrete Neural Networks," Mathematics, MDPI, vol. 11(2), pages 1-13, January.
    2. Gao, Tiegang & Chen, Zengqiang, 2008. "Image encryption based on a new total shuffling algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 38(1), pages 213-220.
    3. Zhou, Chao & Wang, Chunhua & Yao, Wei & Lin, Hairong, 2022. "Observer-based synchronization of memristive neural networks under DoS attacks and actuator saturation and its application to image encryption," Applied Mathematics and Computation, Elsevier, vol. 425(C).
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    Cited by:

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    2. Bayani, Atiyeh & Alexander, Prasina & Azarnoush, Hamed & Rajagopal, Karthikeyan & Jafari, Sajad & Nazarimehr, Fahimeh, 2023. "Designing networks with specific synchronization transitions independent of the system’s dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    3. Lin, Hairong & Wang, Chunhua & Du, Sichun & Yao, Wei & Sun, Yichuang, 2023. "A family of memristive multibutterfly chaotic systems with multidirectional initial-based offset boosting," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    4. Hairong Lin & Chunhua Wang & Fei Yu & Jingru Sun & Sichun Du & Zekun Deng & Quanli Deng, 2023. "A Review of Chaotic Systems Based on Memristive Hopfield Neural Networks," Mathematics, MDPI, vol. 11(6), pages 1-18, March.

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