Daen: a Dual-Adversarial Medical Image Encryption Network for Secure Healthcare

Telemedicine significantly reduces patients' medical treatment time and costs. Medical images play an important role in telemedicine services, which contain patients' private information and face the risk of illegal access when transmitted over the network, leading to patient privacy leakage. This paper proposes a dual-adversarial medical image encryption network (DAEN) for protecting patient privacy. We formulate the image encryption problem as an image conversion task and design two paired adversarial networks for image transformation. Furthermore, we analyze the characteristics of an ideal cipher image and construct pseudo-ciphertext to guide network training. The networks generate keys with strong randomness and sensitivity through adversarial training and dynamically encrypt the medical images. Experiments are conducted on the Chest X-ray dataset, and the results show that the DAEN can encrypt medical images into meaningless images, achieving high information entropy, NPCR, and UCAI compared to advanced encryption algorithms, which can effectively secure medical image transmission.