Brain-Like Initial-Boosted Hyperchaos and Application in Biomedical Image Encryption

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. In this article, we focus on the coupled neural network-based brain-like initial boosting coexisting hype...

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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 18; H. 12; S. 8839 - 8850
Hauptverfasser: Lin, Hairong, Wang, Chunhua, Cui, Li, Sun, Yichuang, Xu, Cong, Yu, Fei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Biological neural networks
Biomedical engineering
Boosting
Brain
Chaos
Chaos theory
Encryption
Entropy (Information theory)
Field programmable gate arrays
Field-programmable gate array (FPGA) implementation
Hopfield neural network (HNN)
hyperchaos
Liapunov exponents
medical image encryption
Medical imaging
memristor
Memristors
Neural networks
Neurons
Performance evaluation
Synapses
ISSN:1551-3203, 1941-0050
Online-Zugang:Volltext
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Zusammenfassung: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. In this article, we focus 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 subneural 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 not only can generate hyperchaotic attractors with high complexity but also can 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 test platform to verify the practicability of the presented MCNN and the designed medical image cryptosystem.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3155599