| Issue |
E3S Web Conf.
Volume 391, 2023
4th International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2023)
|
|
|---|---|---|
| Article Number | 01139 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/e3sconf/202339101139 | |
| Published online | 05 June 2023 | |
Medical Image Watermarking for Tamper Detection in ROI Using LWT and Hashing
1 Assistant Professor, Department of Computer Science & Engineering, Dr. Y.S.R. ANU College of Engineering & Technology, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India
2 Assistant Professor, Department of Computer Science & Engineering, Dr. Y.S.R. ANU College of Engineering & Technology, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India
3 Assistant Professor, Department of Artificial Intelligence & Machine Learning, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad
1 Corresponding author: anu.konda.chaitanya@gmail.com
Security is one of the important characteristics while transmitting the data particularly medical images and patient information over cyberspace without any loss of information. In the proposed method LWT, DCT and SHA-256 hashing techniques are used to provide security to the patient data and to detect tampers in ROI of the medical images. In this paper, medical image is segmented into three regions, region of interest (ROI), region of non interest (RONI) and BORDER regions. ROI is an area that has an important impact on diagnosis, whereas RONI has less or no significance in diagnosis. This paper proposed ROI based tamper detection that embeds hash value of ROI into RONI for authentication and also embed the patient information into RONI for providing security. The experimental results of the proposed method on various medical image databases proved to identify the tampers in medical images. Compared with the existing technique, the proposed method offered high Peak Signal to Noise Ratio (PSNR) approximately 59db for watermarked medical images.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.