Nowadays, with the development of public network usage, medical information is transmitted throughout the hospitals. A watermarking system can help for the confidentiality of medical information distributed over the internet. In medical images, regions-of-interest (ROI) contain diagnostic information. The watermark should be embedded only into non-regions-of-interest (NROI) regions to keep diagnostically important details without distortion. Recently, ROI based watermarking has attracted the attention of the medical research community. The ROI map can be used as an embedding key for improving confidentiality protection purposes. However, in most existing works, the ROI map that is used for the embedding process must be sent as side-information along with the watermarked image. This side information is a disadvantage and makes the extraction process non-blind. Also, most existing algorithms do not recover NROI of the original cover image after the extraction of the watermark. In this paper, we propose a framework for blind diagnostically-lossless watermarking, which iteratively embeds only into NROI. The significance of the proposed framework is in satisfying the confidentiality of the patient information through a blind watermarking system, while it preserves diagnostic/medical information of the image throughout the watermarking process. A deep neural network is used to recognize the ROI map in the embedding, extraction, and recovery processes. In the extraction process, the same ROI map of the embedding process is recognized without requiring any additional information. Hence, the watermark is blindly extracted from the NROI. Furthermore, a three-layer fully connected neural network is used for the detection of distorted NROI blocks in the recovery process to recover the distorted NROI blocks to their original form. The proposed framework is compared with one lossless watermarking algorithm. Experimental results demonstrate the superiority of the proposed framework in terms of side information.

如今，随着公共网络使用的发展，医疗信息已在整个医院中传输。水印系统可以帮助保护通过互联网分发的医学信息的机密性。在医学图像中，关注区域（ROI）包含诊断信息。水印应仅嵌入非关注区域（NROI）区域，以保持诊断上重要的细节而不会失真。近来，基于ROI的水印技术引起了医学研究界的关注。ROI映射可以用作嵌入密钥，以提高机密性保护的目的。但是，在大多数现有作品中，用于嵌入过程的ROI映射必须与水印图像一起作为附带信息发送。该辅助信息是一个缺点，并且使提取过程成为非盲目的。而且，大多数现有算法在提取水印后不能恢复原始封面图像的NROI。在本文中，我们提出了一种盲法诊断无损水印的框架，该框架仅迭代地嵌入到NROI中。所提出的框架的意义在于通过盲注水印系统来满足患者信息的机密性，同时在整个水印过程中保留图像的诊断/医学信息。深度神经网络用于在嵌入，提取和恢复过程中识别ROI映射。在提取过程中，无需任何其他信息即可识别嵌入过程的相同ROI图。因此，水印是从NROI中盲目提取的。此外，三层全连接神经网络用于在恢复过程中检测失真的NROI块，以将失真的NROI块恢复到其原始形式。将该框架与一种无损水印算法进行了比较。实验结果证明了所建议框架在辅助信息方面的优越性。