CHAOTIC IMAGE ENCRYPTION WITH HOPFIELD NEURAL NETWORK

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.

Automated summary

With the development of modern cryptography, it is necessary to protect sensitive information using secure and efficient algorithms. The Hopfield neural network (HNN) has been found to possess stronger memory and can exhibit rich kinetic behavior, especially when incorporating fractional-order operators. Therefore, this study proposes a chaotic image encryption method based on fractional-order HNN (FO-HNN) as a key generator. Spatial permutation strategy and a new diffusion technique using a Three-input logic valve are utilized to eliminate pixel correlation and guide the diffusion process, respectively. Simulation results and security analysis demonstrate the superior security performance of the HNN-based image cryptosystem.