Optimizing Image Encryption Efficiency Through Advanced Deep Learning Architectures and Techniques

Deep learning has significantly advanced image encryption, moving beyond traditional diffusion and confusion methods. This work explores three key methodologies: Encryption with Style Transfer uses neural networks to alter an image's visual style, enhancing security through aesthetic changes but potentially compromising cryptographic strength and efficiency. Style Transfer with Enhanced Diffusion builds on this by incorporating advanced diffusion techniques, improving attack resistance at the cost of increased computational complexity. Combining DNN with Chaotic Systems merges chaos theory with neural networks to produce highly unpredictable encryption patterns, enhancing robustness but adding design complexities and potential vulnerabilities. The work also addresses emerging threats such as Hidden Factors Leakage, involving exposure of internal model parameters, and Network Architecture Leakage.