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        Brain-like Initial-boosted Hyperchaos and Application in Biomedical Image Encryption

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        IEEE_TII_FINAL_VERSION.pdf (PDF, 7Mb)
        Author
        Lin, Hairong
        Wang, Chunhua
        Cui, Li
        Sun, Yichuang
        Xu, Cong
        Yu, Fei
        Attention
        2299/25607
        Abstract
        Neural networks have been widely and deeply studied in the field of computational neurodynamics. However, coupled neural networks and their brain-like chaotic dynamics have not been noticed yet. This paper focuses on the coupled neural network-based brain-like initial boosting coexisting hyperchaos and its application in biomedical image encryption. We first construct a memristive coupled neural network (MCNN) model based on two sub-neural networks and one multistable memristor synapse. Then we investigate its coupling strength-related dynamical behaviors, initial states-related dynamical behaviors, and initial-boosted coexisting hyperchaos using bifurcation diagrams, phase portraits, Lyapunov exponents and attraction basins. The numerical results demonstrate that the proposed MCNN can not only generate hyperchaotic attractors with high complexity but also boost the attractor positions by switching their initial states. This makes the MCNN more suitable for many chaos-based engineering applications. Moreover, we design a biomedical image encryption scheme to explore the application of the MCNN. Performance evaluations show that the designed cryptosystem has several advantages in the keyspace, information entropy, and key sensitivity. Finally, we develop a field-programmable gate array (FPGA) test platform to verify the practicability of the presented MCNN and the designed medical image cryptosystem.
        Publication date
        2022-03-03
        Published in
        IEEE Transactions on Industrial Informatics
        Published version
        https://doi.org/10.1109/TII.2022.3155599
        Other links
        http://hdl.handle.net/2299/25607
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