Deep learning-based encryption scheme for medical images using DCGAN and virtual planet domain
摘要
The motivation for this article stems from the fact that medical image security is crucial for maintaining patient confidentiality and protecting against unauthorized access or manipulation. This paper presents a novel encryption technique that integrates the Deep Convolutional Generative Adversarial Networks (DCGAN) and Virtual Planet Domain (VPD) approach to enhance the protection of medical images. The method uses a Deep Learning (DL) framework to generate a decoy image, which forms the basis for generating encryption keys using a timestamp, nonce, and 1-D Exponential Chebyshev map (1-DEC). Experimental results validate the efficacy of the approach in safeguarding medical images from various security threats, including unauthorized access, tampering, and adversarial attacks. The randomness of the keys and encrypted images are demonstrated through the National Institute of Standards and Technology (NIST) SP 800-22 Statistical test suite provided in Tables