RESAKey GAN: enhancing color image encryption through residual self-attention generative adversarial networks

Abstract Color image encryption techniques have garnered significant attention due to their importance in safeguarding sensitive visual data. This paper introduces RESAKey GAN, a novel generative adversarial network that leverages the Residual Self-Attention mechanism to generate highly secure cryptographic keys for color image encryption. RESAKey GAN employs a redesigned architecture based on Wasserstein GAN with Gradient Penalty, addressing issues such as mode collapse and training instability. The network incorporates three novel loss functions tailored for image encryption tasks, enhancing key randomness, correlation, sensitivity, and visual appearance. Experiments conducted on the SIPI dataset demonstrate that RESAKey GAN generates private keys with excellent security, robust scrambling and diffusion performance, enabling effective encryption of diverse image scenarios using a simple XOR operation. Our approach mitigates concerns associated with traditional key generation algorithms and showcases the potential of deep learning in advancing color image encryption techniques.