Hidden coexisting hyperchaos of new memristive neuron model and its application in image encryption

Qiang Lai, Cong Lai, Hui Zhang and Chunbiao Li

Chaos, Solitons & Fractals, 2022, vol. 158, issue C

Abstract: The neuron models have been widely applied to neuromorphic computing systems and chaotic circuits. However, discrete neuron models and their application in image encryption have not gotten a lot of attention yet. This paper first presents a novel neuron model with significant chaotic characteristics, by coupling a memristor into the proposed neuron, a memristive neuron model is further obtained. Relevant control parameter-relied dynamical evolution is demonstrated using several numerical methods. The explorations manifest that memristor can boost chaos complexity of the discrete neuron, resulting in hyperchaos, infinite coexisting hidden attractors and attractor growing. Particularly, the NIST test verifies the generated hyperchaotic sequences exhibit high complexity, which makes them applicable to many applications based on chaos. Additionally, digital experiments based on developed hardware platform are designed to implement the memristive neuron model and get the hyperchaos. We also propose a new encryption scheme to apply the memristive neuron to the application of image encryption. The evaluation results show that the conceived algorithm appears excellent security characteristics and can effectively protect the information security of images.

Keywords: Hyperchaos; Memristive neuron; Coexisting hidden attractors; Attractor growing; Hardware platform; Image encryption (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (30)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:158:y:2022:i:c:s0960077922002272

DOI: 10.1016/j.chaos.2022.112017

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