A semi-fragile reversible watermarking method based on qdft and tamper ranking

被引:0
|
作者
Ouyang, Junlin [1 ,2 ,3 ]
Huang, Jingtao [1 ,2 ]
Wen, Xingzi [2 ]
机构
[1] School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan,411201, China
[2] Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan, 411201, China
[3] Hunan Software Vocational and Technical University, Xiangtan, 411100, China
关键词
Image forensics - Quaternion discrete fourier transform - Reversible watermarking - Semi-fragile - Semi-fragile reversible watermarking - Semi-fragile watermarking - Tamper localization - Tampering localizations - Watermark embedding - Watermarking methods;
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摘要
The traditional semi-fragile watermarking can effectively protect the integrity of the host image, but the permanent distortion caused by watermark embedding makes the application in high-fidelity images limited. Hence, we propose a novel semi-fragile reversible watermarking scheme for tampering localization. The motivation of this research is to solve the problem that the original carrier cannot be recovered in semi-fragile watermarking method and to improve the accuracy of tampered image detection and location. The proposed scheme embeds the semi-fragile watermark into the mid-frequency quaternion discrete Fourier transform (QDFT) coefficients to provide semi-fragility and reversibly embeds the compensation information into the watermarked image to achieve cover-lossless. First, to reduce the amount of compensation information, the watermarked image is pre-recovered using the neighborhood coefficients of the semi-fragile watermark embedding position. Then, the difference between the pre-recovered image and the original image is represented by the frequency domain error and the spatial domain error. Secondly, the performance of tampering localization is improved by optimizing the graph structure and weight matrix in the manifold ranking (MR) model. Specifically, a new approximate k-regular graph is constituted by introducing spatial relations of neighbors, that must be linked and must not be linked, which are integrated into the original graph structure. Then, a new weight matrix is constructed by combining multi-view features, to enhance the shape performance of the localized regions. The experimental results show that the proposed method can effectively resist content retention attacks, and accurately locate tampered regions. In the event of a non-attack, the original image can be recovered losslessly. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
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页码:41555 / 41578
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