A SURF and SVD-based robust zero-watermarking for medical image integrity.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2024
Historique:
received: 26 03 2024
accepted: 09 07 2024
medline: 12 9 2024
pubmed: 12 9 2024
entrez: 12 9 2024
Statut: epublish

Résumé

Medical image security is paramount in the digital era but remains a significant challenge. This paper introduces an innovative zero-watermarking methodology tailored for medical imaging, ensuring robust protection without compromising image quality. We utilize Sped-up Robust features for high-precision feature extraction and singular value decomposition (SVD) to embed watermarks into the frequency domain, preserving the original image's integrity. Our methodology uniquely encodes watermarks in a non-intrusive manner, leveraging the robustness of the extracted features and the resilience of the SVD approach. The embedded watermark is imperceptible, maintaining the diagnostic value of medical images. Extensive experiments under various attacks, including Gaussian noise, JPEG compression, and geometric distortions, demonstrate the methodology's superior performance. The results reveal exceptional robustness, with high Normalized Correlation (NC) and Peak Signal-to-noise ratio (PSNR) values, outperforming existing techniques. Specifically, under Gaussian noise and rotation attacks, the watermark retrieved from the encrypted domain maintained an NC value close to 1.00, signifying near-perfect resilience. Even under severe attacks such as 30% cropping, the methodology exhibited a significantly higher NC compared to current state-of-the-art methods.

Identifiants

pubmed: 39264977
doi: 10.1371/journal.pone.0307619
pii: PONE-D-24-12300
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0307619

Informations de copyright

Copyright: © 2024 Taj et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Rizwan Taj (R)

School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu, China.

Feng Tao (F)

School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu, China.

Saima Kanwal (S)

School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu, China.

Ahmad Almogren (A)

Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.

Ateeq Ur Rehman (AU)

School of Computing, Gachon University, Seongnam, Republic of Korea.

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Classifications MeSH