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arXiv · 1911.00382

BlessMark: A Blind Diagnostically-Lossless Watermarking Framework for Medical Applications Based on Deep Neural Networks

Hamidreza Zarrabi·Ali Emami·Pejman Khadivi·Nader Karimi·Shadrokh Samavi
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Abstract

Nowadays, with the development of public network usage, medical information is transmitted throughout the hospitals. The 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) to keep diagnostic information 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.

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Hamidreza Zarrabi, Ali Emami, Pejman Khadivi, Nader Karimi, Shadrokh Samavi. 2019-11-01. BlessMark: A Blind Diagnostically-Lossless Watermarking Framework for Medical Applications Based on Deep Neural Networks. https://arxiv.org/abs/1911.00382

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