DNGG: Medical Image Lossless Encryption via Deep Network Guided Generative
Lin Fan, Meng Li, Zhanxuan Hu, Yuan Hong, Dexing Kong · IEEE Signal Processing Letters · 2025
Ensuring the security and integrity of medical images is crucial for telemedicine. Recently, deep learning-based image encryption techniques have significantly improved data transmission security. However, the unpredictability of complex models may lead to damage during image reconstruction, thereby negatively impacting medical diagnostics. To address this issue, we propose a lossless encryption algorithm for medical images, which is based on a guided image generative neural network. Initially, we designed a guided image generation network. Subsequently, we train a generator using random keys to produce a key map. This key map then guides the encryption of the secret image through a bitwise XOR (bit-XOR) algorithm, effectively merging the secret image with the key map. During the decryption process, the original image can be restored losslessly by using a key map generated from a random key. The experimental results show that the encryption algorithm greatly ensures the security of data and shows strong anti-attack ability.