Memristive Hopfield bi-neurons under external stimuli and data privacy application

Abstract

As medical imaging technology improves, protecting sensitive biomedical images during transmission and storage is becoming more important. Traditional encryption methods often struggle with efficiency and flexibility, especially when dealing with the large datasets typical in this field. This study explores using memristive Hopfield networks for encrypting biomedical images, leveraging their natural abilities for memory and pattern recognition. We examine how electromagnetic radiation and stimulating currents influence the behavior of these networks. Our results show that the model can reach multiple stable and unstable states, producing different patterns, including limit cycles and chaotic structures with various forms (like two-scroll, three-scroll, and four-scroll chaos). Notably, stimulating currents create uneven dynamic behavior. The model also reveals various patterns related to synaptic connections, which could provide insights into neurological diseases. To confirm our findings, we built a digital hardware device that successfully replicated these patterns. We also used a compressive sensing method to compress and encrypt biomedical images with the chaotic sequences generated by the model. Our experiments show that this encryption method is robust against various attacks with high key space, making it a good option for secure communication.