Deep learning based medical image segmentation for encryption with copyright protection through data hiding

The prevention of medical information leakage has gained significant attention in recent
times. As a result, numerous image encryption schemes are gaining prominence in protecting
the privacy of original images. However, third-party users can easily compromise and access
encrypted data after decryption. Therefore, it is imperative to develop encryption systems
with enhanced confidentiality to address this issue. To tackle these problems, 3D-chaos-based
encryption combined with copyright protection is proposed. This achieves high security at a
low time cost. The method first segments the most significant information, i.e. the region of
interest (ROI) part of the medical image, through the recent deep learning-based
segmentation, i.e., you only look once (YOLO) version 8, for image encryption. The 3D-
chaos-based encryption encodes only the ROI part, making it well-suited for secure
healthcare with a low time cost. Finally, the hash of the ROI and the MAC address of the
sender system is embedded into the non-region of interest (NROI) part of the image, making
it effective against copyright violation, high bandwidth and storage costs. The results of
extensive experiments on COVID-19 and COCO2017 datasets indicate that the method is
highly secure, cost-effective and resistant to brute-force attacks. Given the advantages of
encryption and data hiding, the proposed method could be an apt choice for medical data
transmission and protection against any brute-force, statistical or differential attacks.