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    Robust reversible watermarking via clustering and enhanced pixel-wise masking

    An, L. and Gao, X. and Li, Xuelong and Tao, D. and Deng, C. and Li, J. (2012) Robust reversible watermarking via clustering and enhanced pixel-wise masking. IEEE Transactions on Image Processing 21 (8), pp. 3598-3611. ISSN 1057-7149.

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    Robust reversible watermarking (RRW) methods are popular in multimedia for protecting copyright, while preserving intactness of host images and providing robustness against unintentional attacks. However, conventional RRW methods are not readily applicable in practice. That is mainly because: 1) they fail to offer satisfactory reversibility on large-scale image datasets; 2) they have limited robustness in extracting watermarks from the watermarked images destroyed by different unintentional attacks; and 3) some of them suffer from extremely poor invisibility for watermarked images. Therefore, it is necessary to have a framework to address these three problems, and further improve its performance. This paper presents a novel pragmatic framework, wavelet-domain statistical quantity histogram shifting and clustering (WSQH-SC). Compared with conventional methods, WSQH-SC ingeniously constructs new watermark embedding and extraction procedures by histogram shifting and clustering, which are important for improving robustness and reducing run-time complexity. Additionally, WSQH-SC includes the property-inspired pixel adjustment to effectively handle overflow and underflow of pixels. This results in satisfactory reversibility and invisibility. Furthermore, to increase its practical applicability, WSQH-SC designs an enhanced pixel-wise masking to balance robustness and invisibility. We perform extensive experiments over natural, medical, and synthetic aperture radar images to show the effectiveness of WSQH-SC by comparing with the histogram rotation-based and histogram distribution constrained methods.


    Item Type: Article
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Sarah Hall
    Date Deposited: 06 Jun 2013 16:25
    Last Modified: 09 Aug 2023 12:33


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