Reversible Watermarking in Medical Imaging Using Deep Learning for Cross Modality - Tripathi, Pradeep Kumar; Varshney, Manoj; Sharma, Aditi (Journal of Innovative Image Processing, 2025)
With the advancement of digital healthcare, the protection of sensitive medical multimedia data like images, videos, and voice recordings, has become even more critical. The comprehensive deep learning-based reversible watermarking proposed in this work focuses on the protection of cross-modality medical content. The watermarking mechanism is framed on Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) to facilitate robust, invisible, and reversible watermark embedding while maintaining data and content integrity. The system supports real-time, 3D, and layered image and video compatibilities. Apart from watermarking, it is applied to improve the accuracy of images and enable fast viewing and reading of images by users. The approach outlined maintains the quality of images while allowing for compression, cropping, and resizing, as well as incorporating noise. It preserves images as crisp and detailed by adjusting watermark placement based on the relative significance of different areas of the image. This is validated by the application of high PSNR and SSIM values to demonstrate the maintenance of image quality. It is still optimizable and can be applied to a broad range of applications in medicine, with the ability to transition easily into medical routines without compromising data security and audibility.
Keywords: Reversible Watermarking; Convolutional Neural Networks (CNNs); Generative Adversarial Networks (GANs); Peak Signal-to-Noise Ratio (PSNR); Structural Similarity Index Measure (SSIM); Bit Error Rate (BER)