Perceptual Encryption

Perceptual encryption aims to transform images into unrecognizable forms while preserving information crucial for machine learning tasks, thereby safeguarding data privacy during model training. Current research focuses on developing robust encryption methods, often employing pixel-based techniques and generative adversarial networks (GANs), and simultaneously evaluating their security against sophisticated attacks. A key challenge lies in balancing strong encryption with minimal impact on model accuracy, with recent work exploring chaotic maps and improved key management to enhance security. This field is vital for enabling privacy-preserving deep learning applications across various domains.