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基于 DWT-HD-SVD 算法的多尺寸水印对抗的医疗保健数据安全分析。

Analysis of healthcare data security with DWT-HD-SVD based-algorithm invisible watermarking against multi-size watermarks.

机构信息

University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Sector 16-C, Dwarka, New Delhi, India.

Department of Computer Science and Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad, India.

出版信息

Sci Rep. 2024 May 10;14(1):10769. doi: 10.1038/s41598-024-61479-4.

DOI:10.1038/s41598-024-61479-4
PMID:38730071
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11087543/
Abstract

In the modern day, multimedia and digital resources play a crucial role in demystifying complex topics and improving communication. Additionally, images, videos, and documents speed data administration, fostering both individual and organizational efficiency. Healthcare providers use tools like X-rays, MRIs, and CT scans to improve diagnostic and therapeutic capacities, highlighting the importance of these tools in contemporary communication, data processing, and healthcare. Protecting medical data becomes essential for maintaining patient confidentiality and service dependability in a time when digital assets are crucial to the healthcare industry. In order to overcome this issue, this study analyses the DWT-HD-SVD algorithm-based invisible watermarking in medical data. The main goal is to verify medical data by looking at a DWT-based hybrid technique used on X-ray images with various watermark sizes (256256, 128128, 64*64). The algorithm's imperceptibility and robustness are examined using metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) and are analyzed using Normalized Connection (NC), Bit Error Rate (BER), and Bit Error Rate (BCR) in order to evaluate its resistance to various attacks. The results show that the method works better with smaller watermark sizes than it does with larger ones.

摘要

在现代,多媒体和数字资源在使复杂的主题变得易懂并促进交流方面发挥着关键作用。此外,图像、视频和文档加速了数据管理,提高了个人和组织的效率。医疗保健提供者使用 X 光、MRI 和 CT 扫描等工具来提高诊断和治疗能力,突出了这些工具在当代通信、数据处理和医疗保健中的重要性。在数字资产对医疗行业至关重要的时代,保护医疗数据对于维护患者隐私和服务可靠性至关重要。为了解决这个问题,本研究分析了基于 DWT-HD-SVD 算法的医学数据隐形水印。主要目标是通过查看用于不同大小水印(256256、128128、64*64)的 X 射线图像的基于 DWT 的混合技术,验证医学数据。使用 PSNR 和 SSIM 等指标来检查算法的不可感知性和稳健性,并使用归一化连接 (NC)、误码率 (BER) 和误码率 (BCR) 进行分析,以评估其对各种攻击的抵抗力。结果表明,该方法在较小的水印尺寸下效果更好,而在较大的水印尺寸下效果较差。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/c7a4ea9211b1/41598_2024_61479_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/cbc70aba21e7/41598_2024_61479_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/1d8c487e89c8/41598_2024_61479_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/9f9ee780757d/41598_2024_61479_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/04c8998dc61a/41598_2024_61479_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/fddd869a8008/41598_2024_61479_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/6fa339873314/41598_2024_61479_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/9b4e4d75c852/41598_2024_61479_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/c7a4ea9211b1/41598_2024_61479_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/cbc70aba21e7/41598_2024_61479_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/1d8c487e89c8/41598_2024_61479_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/9f9ee780757d/41598_2024_61479_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/04c8998dc61a/41598_2024_61479_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/fddd869a8008/41598_2024_61479_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/6fa339873314/41598_2024_61479_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/9b4e4d75c852/41598_2024_61479_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/11087543/c7a4ea9211b1/41598_2024_61479_Fig8_HTML.jpg

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