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A cross-embedding based medical image tamper detection and self-recovery watermarking scheme

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Abstract

With the rapid growth in communication and computing technologies, the transmission of medical images over the Internet is on the rise. In such a scenario, there is a special need to meet the security and privacy issues and challenges of individual and Intellectual Property (IP) owners. It is highly important for an individual to keep his/her personal medical images against invalid manipulation by impostors. Hence developments of authentication and tamper detection techniques are the need of the hour. For this, a tamper detection and self-recovery watermarking scheme for medical images based on texture degree and cross-embedding is proposed in this paper. Firstly, divide medical images into ROI (Region of Interest) and RONI (Region of Non-Interest); generate a double authentication watermark in ROI to improve the accuracy of tamper detection and reduce the probability of false alarm; calculate texture complexity based on 4-dimensional features in ROI, and divide ROI into texture blocks and smooth blocks; generate different recovery watermarks according to the characteristics of different blocks using compression-aware technology. Then, hide the recovery watermark in RONI based on the reference matrix and cross-embedding technology. Finally, locate the tampered blocks in the ROI based on three level tamper detection strategy including pixel-level, block-level, and multi-direction subband-level; restore the tampered region by the extracted recovery watermark. The experimental results indicate that the tamper detection accuracy of the ROI region is close to 100%. Additionally, at an embedding rate of 1.4074bpp, the PSNR reaches 45.0217 dB and the NC is 0.99. In addition, the scheme provides promising results against copy-paste attacks, collage attacks and steganalysis. Also, the scheme achieves privacy protection. This clearly demonstrates that the proposed scheme has several advantages, including strong tamper detection capability, effective self-recovery, high security, excellent concealment, and robustness.

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Acknowledgments

Supported by National Science Foundation of China (No.61976109, 62006108, 61601214, 61877007); Liaoning Revitalization Talents Program (No.XLYC2006005); Liaoning Provincial Education Department (No. WQ2020014); Scientific Research Project of Liaoning Province(No.LJKZ0963); Key R&D projects of Liaoning Provincial Department of Science and Technology; Liaoning Provincial Key Laboratory Special Fund.

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  1. School of Computer Science and Aritificial Intelligence, Liaoning Normal University, Dalian, 116029, China

    Hui Shi, Kexun Yan, Jianing Geng & Yonggong Ren

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  1. Hui Shi
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Correspondence to Hui Shi or Yonggong Ren.

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Shi, H., Yan, K., Geng, J. et al. A cross-embedding based medical image tamper detection and self-recovery watermarking scheme. Multimed Tools Appl 83, 30319–30360 (2024). https://doi.org/10.1007/s11042-023-16679-x

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