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

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Brain-like Initial-boosted Hyperchaos and Application in Biomedical Image Encryption. / Lin, Hairong ; Wang, Chunhua; Cui, Li ; Sun, Yichuang; Xu, Cong ; Yu, Fei .

In: IEEE Transactions on Industrial Informatics, 03.03.2022.

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@article{b864e275b6044ed0b288e2f7f07fa61e,
title = "Brain-like Initial-boosted Hyperchaos and Application in Biomedical Image Encryption",
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 moresuitable 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.",
keywords = "Biological neural networks, Boosting, Chaos, Encryption, FPGA implementation, Hopfield neural network, Hyperchaos, Memristors, Neurons, Synapses, medical image encryption, memristor",
author = "Hairong Lin and Chunhua Wang and Li Cui and Yichuang Sun and Cong Xu and Fei Yu",
note = "{\textcopyright} 2022 IEEE - All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TII.2022.3155599",
year = "2022",
month = mar,
day = "3",
doi = "10.1109/TII.2022.3155599",
language = "English",
journal = "IEEE Transactions on Industrial Informatics",
issn = "1551-3203",
publisher = "IEEE Computer Society",

}

RIS

TY - JOUR

T1 - Brain-like Initial-boosted Hyperchaos and Application in Biomedical Image Encryption

AU - Lin, Hairong

AU - Wang, Chunhua

AU - Cui, Li

AU - Sun, Yichuang

AU - Xu, Cong

AU - Yu, Fei

N1 - © 2022 IEEE - All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TII.2022.3155599

PY - 2022/3/3

Y1 - 2022/3/3

N2 - 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 moresuitable 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.

AB - 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 moresuitable 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.

KW - Biological neural networks

KW - Boosting

KW - Chaos

KW - Encryption

KW - FPGA implementation

KW - Hopfield neural network

KW - Hyperchaos

KW - Memristors

KW - Neurons

KW - Synapses

KW - medical image encryption

KW - memristor

UR - http://www.scopus.com/inward/record.url?scp=85125709760&partnerID=8YFLogxK

U2 - 10.1109/TII.2022.3155599

DO - 10.1109/TII.2022.3155599

M3 - Article

JO - IEEE Transactions on Industrial Informatics

JF - IEEE Transactions on Industrial Informatics

SN - 1551-3203

ER -