A fuzzy activation function based zeroing neural network for dynamic Arnold map image cryptography

Jie Jin, Xiaoyang Lei, Chaoyang Chen and Zhijing Li

Mathematics and Computers in Simulation (MATCOM), 2025, vol. 230, issue C, 456-469

Abstract: As an effective method for time-varying problems solving, zeroing neural network (ZNN) has been frequently applied in science and engineering. In order to improve its performances in practical applications, a fuzzy activation function (FAF) is designed by introducing the fuzzy logic technology, and a fuzzy activation function based zeroing neural network (FAF-ZNN) model for fast solving time-varying matrix inversion (TVMI) is proposed. Rigorous mathematical analysis and comparative simulation experiments with other models guarantee its superior convergence and robustness to noises. In addition, based on the proposed FAF-ZNN model, a new dynamic Arnold map image cryptography algorithm is designed. Specifically, in the new dynamic image encryption, a dynamic key matrix is introduced, and the FAF-ZNN model is applied to fast compute the inversion of the dynamic key matrix for the dynamic Arnold map image cryptography decryption process. The effectiveness of the dynamic image encryption algorithm is verified by experiment results, which enhances the security of existing image encryption algorithms.

Keywords: Zeroing neural network; Fuzzy activation function based zeroing neural network (FAF-ZNN); Dynamic image encryption; Arnold map image cryptography; Robustness (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:230:y:2025:i:c:p:456-469

DOI: 10.1016/j.matcom.2024.10.031

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