CHAOTIC IMAGE ENCRYPTION WITH HOPFIELD NEURAL NETWORK

Yuwen Sha, Jun Mou, Jue Wang, Santo Banerjee and Bo Sun
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Yuwen Sha: School of Management, Dalian Polytechnic University, Dalian 116034, P. R. China†School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, P. R. China
Jun Mou: School of Management, Dalian Polytechnic University, Dalian 116034, P. R. China†School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, P. R. China
Jue Wang: School of Management, Dalian Polytechnic University, Dalian 116034, P. R. China†School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, P. R. China
Santo Banerjee: ��Department of Mathematical Sciences, Giuseppe Luigi Lagrange, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, Italy
Bo Sun: School of Management, Dalian Polytechnic University, Dalian 116034, P. R. China

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-16

Abstract: With modern cryptography evolves, some sensitive information needs to be protected with secure and efficient algorithms. In this context, we found that Hopfield neural network (HNN) has stronger memory and can generate luxuriant kinetic behavior, especially with the introduction of fractional-order operators. Therefore, we propose a chaotic image encryption based on the fractional-order HNN (FO-HNN), where FO-HNN appears as a key generator. To de-correlate the correlation between pixels, a spatial permutation strategy is designed first, and then a new diffusion technique based on a Three-input logic valve is adopted to guide the diffusion process. Simulation results and security analysis show that the HNN-based image cryptosystem has superior security performance.

Keywords: Hopfield Neural Network; Fractional-Order; Spatial Permutation; Image Encryption; Superior Security Performance (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23401072

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