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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Published in:	IEEE transactions on industrial informatics Vol. 18; no. 12; pp. 8839 - 8850
Main Authors:	Lin, Hairong, Wang, Chunhua, Cui, Li, Sun, Yichuang, Xu, Cong, Yu, Fei
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
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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. 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.
AbstractList	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.
Author	Wang, Chunhua Lin, Hairong Cui, Li Sun, Yichuang Xu, Cong Yu, Fei
Author_xml	– sequence: 1 givenname: Hairong orcidid: 0000-0003-3506-9780 surname: Lin fullname: Lin, Hairong email: haironglin@hnu.edu.cn organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, China – sequence: 2 givenname: Chunhua orcidid: 0000-0001-6522-9795 surname: Wang fullname: Wang, Chunhua email: wch1227164@hnu.edu.cn organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, China – sequence: 3 givenname: Li surname: Cui fullname: Cui, Li email: licui@hnust.edu.cn organization: School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China – sequence: 4 givenname: Yichuang orcidid: 0000-0001-8352-2119 surname: Sun fullname: Sun, Yichuang email: y.sun@herts.ac.uk organization: School of Engineering and Computer Science, University of Hertfordshire, Hatfield, U.K – sequence: 5 givenname: Cong surname: Xu fullname: Xu, Cong email: xucong0703@163.com organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, China – sequence: 6 givenname: Fei orcidid: 0000-0002-3091-7640 surname: Yu fullname: Yu, Fei email: yufeiyfyf@csust.edu.cn organization: School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China
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SubjectTerms	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
Title	Brain-Like Initial-Boosted Hyperchaos and Application in Biomedical Image Encryption
URI	https://ieeexplore.ieee.org/document/9726899 https://www.proquest.com/docview/2719555527
Volume	18
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