A Novel GAN-based Chaotic Method with DNA Computing for Enhancing Security of Medical Images

Medical images are utilized to diagnose patients' health conditions. Nowadays, medical images are sent over the internet for diagnosis purposes. So, they should be protected from cyber attackers. These medical images are sensitive to any minor changes, and the data volume is rapidly increasing. Thus, security and storage costs must be considered in medical images. Traditional encryption and compression methods are ineffective for encrypting medical images due to their high execution time and algorithm complexity. In this paper, a novel 2D-chaos and Generative Adversarial Network (GAN) with DeoxyriboNucleic Acid (DNA) computing is proposed for generating encryption keys and improving the security of medical images. The proposed scheme uses GAN and 2D-chaos to generate the private key and diffusion process. The pixel values of the original images in the proposed schemes are shuffled using Mersenne Twister (MT) to improve the security of medical images. Moreover, the novel 2D-Chaotic Tent Map (2D-CTM) method is used to construct the key while performing XOR-based encryption. The proposed model has been tested on different medical images, namely the COVIDx-19 X-ray images, the malaria microscopic images, and the brain MRI images. The experiment results have been evaluated using performance metrics, namely key space, histogram analysis, entropy, key sensitivity, robustness analysis, correlation, SSIM, and MS-SSIM. The outcomes demonstrate that the proposed scheme is more effective than the state-of-the-art schemes.