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

Hairong Lin, Chunhua Wang, Li Cui, Yichuang Sun, Cong Xu, Fei Yu

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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.
Original languageEnglish
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Early online date3 Mar 2022
Publication statusE-pub ahead of print - 3 Mar 2022


  • Biological neural networks
  • Boosting
  • Chaos
  • Encryption
  • FPGA implementation
  • Hopfield neural network
  • Hyperchaos
  • Memristors
  • Neurons
  • Synapses
  • medical image encryption
  • memristor


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