Novel chaotic colour image cryptosystem with deep learning

Shuang Zhou, Zhipeng Zhao and Xingyuan Wang

Chaos, Solitons & Fractals, 2022, vol. 161, issue C

Abstract: A chaotic colour image cryptosystem frame with deep learning is proposed in this paper. First, we choose a chaotic system, after which a long short-term memory network is used to train 4D hyper-chaotic Lorenz signals and forecast four new signals, which passed the randomness test. Finally, we applied them to the colour cryptosystem. Given that new chaotic signals generated by deep learning are different from the original chaotic signals and the complex structure of deep learning, it is difficult to be attacked. Simulation results indicate that the proposed method has a high level of security compared to the representative image encryption algorithms.

Keywords: Chaotic system; Image encryption; Deep learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:161:y:2022:i:c:s0960077922005902

DOI: 10.1016/j.chaos.2022.112380

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