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用于医学视频认证的盲视频水印方案。

Blind video watermarking scheme for medical video authentication.

作者信息

Khafaga Doaa Sami, Alohaly Manar, Abdel-Aziz Mostafa M, Hosny Khalid M

机构信息

Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Department of Information Systems, College of Computer and Information Sciences Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

出版信息

Heliyon. 2023 Sep 7;9(9):e19809. doi: 10.1016/j.heliyon.2023.e19809. eCollection 2023 Sep.

Abstract

Medical video watermarking is one of the beneficial and efficient tools to prohibit important patients' data from illicit enrollment and redistribution. In this paper, a new blind watermarking scheme has been proposed to improve the confidentiality, integrity, authenticity, and perceptual quality of a medical video with minimum distortion. The proposed scheme is based on 2D-DWT and dual Hessenberg-QR decomposition, where the input medical video is initially processed into frames. Then, the processed frames are transformed into sub-bands using 2D-DWT, followed by applying Hessenberg-QR decomposition on the selected wavelet HL2 sub-band. The watermark is scrambled via Arnold cat map to raise confidentiality and then concealed in the modified selected features. The watermark is extracted in a fully blind mode without referencing the original video, which reduces the extraction time. The proposed scheme maintained a fundamental tradeoff between robustness and visual imperceptibility compared to existing methods against many commonly encountered attacks. The visual imperceptibility has been evaluated using well-known metrics PSNR, SSIM, Q-index, and histogram analysis. The proposed scheme achieves a high PSNR value of (70.6899 dB) with minimal distortion and a high robustness level with an average NC value of (0.9998) and BER value of (0.0023) while conserving a large payload capacity. The obtained results show superior performance over similar video watermarking methods. The limitation of this scheme is the elapsed time during the embedding process since we utilized dual Hessenberg-QR decomposition. One possible solution to reduce time consumption is simple decompositions like bound-constrained SVM or similar decompositions.

摘要

医学视频水印是防止重要患者数据被非法获取和重新分发的有益且高效的工具之一。本文提出了一种新的盲水印方案,以在最小失真的情况下提高医学视频的保密性、完整性、真实性和感知质量。该方案基于二维离散小波变换(2D-DWT)和双 Hessenberg-QR 分解,其中输入的医学视频首先被处理成帧。然后,使用 2D-DWT 将处理后的帧变换为子带,接着对所选的小波 HL2 子带应用 Hessenberg-QR 分解。水印通过 Arnold 猫映射进行置乱以提高保密性,然后隐藏在修改后的所选特征中。水印在完全盲的模式下提取,无需参考原始视频,这减少了提取时间。与现有方法相比,该方案在抵抗许多常见攻击时,在鲁棒性和视觉不可感知性之间保持了基本的权衡。使用著名的指标峰值信噪比(PSNR)、结构相似性指数(SSIM)、Q 指数和直方图分析对视觉不可感知性进行了评估。该方案在最小失真的情况下实现了高达 70.6899 dB 的 PSNR 值,具有高鲁棒性水平,平均归一化相关性(NC)值为 0.9998,误码率(BER)值为 0.0023,同时保留了较大的有效载荷容量。所得结果表明,该方案的性能优于类似的视频水印方法。该方案的局限性在于嵌入过程中的耗时,因为我们使用了双 Hessenberg-QR 分解。减少时间消耗的一种可能解决方案是采用诸如边界约束支持向量机(SVM)之类的简单分解方法或类似的分解方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a46/10559170/c9920809af98/gr1.jpg

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