A Novel Deep Learning-Assisted SVD-based Method for Medical Image Watermarking

In the present era, the administration of medical images faces various security challenges that necessitate the authentication of image source and origin for accurate patient identification. With the increasing exchange of medical images between hospitals to facilitate informed decision-making, the adoption of digital watermarking techniques has emerged as an efficient solution to address the imperceptibility and robustness requirements in medical imaging watermarking. This research work introduces a technically advanced approach that combines singular value decomposition (SVD) watermarking with deep learning segmentation models to enhance the security of medical image sharing and transfer. The primary objective is to seamlessly integrate the watermark while minimizing distortion to preserve critical medical information within the image. The proposed methodology involves utilizing a ResNet-based U-Net segmentation model to segment X-Ray radiographs into the Region of Interest (ROI) and the Region of Non-Interest (RONI). The watermark data is then encoded into the ROI using singular value decomposition. Subsequently, the ROI and RONI are merged to reconstruct the complete image, preserving its original identity. Additionally, XOR encryption is applied to the watermarked image to enhance data integrity and copyright protection. On the other side of the methodology, the reconstructed image is once again separated into ROI and RONI. The ROI is decoded to recover the original transferred content. To assess the efficacy of the proposed method, a publicly available X-Ray radiograph dataset is employed, and evaluation metrics demonstrate an impressive segmentation accuracy of 98.27%. The proposed approach ensures information integrity, patient confidentiality during data sharing, and robustness against various conventional attacks, demonstrating its effectiveness in the field of medical image watermarking.