Generative Adversarial Network and Chaotic Map-Based Multi-Layer Medical Image Encryption

One of the major challenges in securing medical image communication systems is the secure and efficient management of cryptographic key material. In this paper, we propose a multi-layer image encryption algorithm that addresses image security while reducing per-image key-storage and transmission overhead under a pre-shared protected-generator model. The proposed algorithm integrates a Generative Adversarial Network, a Piecewise Linear Chaotic Map, DNA complement operations, and bit-level zigzag permutation. A distinguishing feature of the proposed algorithm is that the key image is generated from an image-specific 100-dimensional noise vector, which serves exclusively as the input to the trained generator, while the chaotic parameters and diffusion materials are derived from the generated key image. In this approach, under the assumption of a pre-shared protected generator, transmitting only the image-specific 100-dimensional noise vector that bears no structural relationship to the key image reduces per-image key storage and transmission overhead. Comprehensive numerical evaluations were performed on eleven images, comprising both standard test images and medical images, to assess the security and robustness of the proposed algorithm. The experimental results demonstrate entropy values exceeding 7.996 bits, along with NPCR and UACI values of 99.60% and 33.46%, respectively. Adjacent pixel correlations are reduced to near-zero levels across all tested images. The proposed algorithm exhibits strong robustness against common attacks, including up to 75% cropping and 50% salt-and-pepper noise. The proposed algorithm achieves competitive performance compared with several existing encryption methods. Successful decryption requires the correct image-specific noise vector and the original trained generator.