https://doi.org/10.1140/epjp/s13360-024-05918-1
Regular Article
Cross-plane color image encryption and FPGA implementation of arctangent memristor Hopfield network
College of Information Engineering, Shenyang University of Chemical Technology, 110142, Shenyang, China
Received:
30
September
2024
Accepted:
9
December
2024
Published online:
27
December
2024
In order to explore chaotic systems with complex topological structure, this study focuses on the fusion of memristor and neural network in real physical models. This article innovatively combines local active memristors with Hopfield neural networks, constructing a novel coexisting dual-vortex memristive autapse Hopfield neural network model. Firstly, by introducing the arctangent function, a locally active dual-state memristor was designed, and its hysteresis loops, POP characteristics, state switching capability, and local activity were comprehensively verified using numerical analysis methods. Based on the characteristic of memristors to simulate the induced current caused by the potential difference between neurons, they are introduced as synaptic weights into the Hopfield network. Dynamic analysis shows that the network exhibits rich dynamic behaviors under different synaptic weights, including double scroll, single scroll, and periodic attractors, and the coexistence of symmetric attractors has been discovered. Furthermore, a cross-plane color image scrambling encryption algorithm based on memristor chaotic systems has been developed, which effectively scrambles and diffuses images using the chaotic sequences generated by the system. Comparative verification has demonstrated the high confidentiality and feasibility of this algorithm. Finally, the digital memristor chaotic circuit was implemented on an FPGA platform, successfully validating the potential of the proposed model and algorithm in practical applications.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
