Region duplication detection based on image segmentation and keypoint contexts
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In this paper, a novel region duplication detection method is proposed based on image segmentation and keypoint contexts. The proposed method includes the primary region duplication detection based on keypoints and the supplementary region duplication detection based on blocks. In the primary region duplication detection, an image is divided into non-overlapped patches by using SLIC. Furthermore, the keypoints are matched and clustered within the same patch as patch feature. Then the patches are matched and an affine transformation is tried to be estimated from a pair of patches. When the estimation fails, in the supplementary region duplication detection, a transformation matrix is tried to be estimated from a pair of keypoints by the proposed Keypoint Contexts (KC) approach. The experimental results indicate that the proposed method can achieve much better comprehensive performances than the state-of-the-art methods on the public databases, even under various challenging conditions.
KeywordsDigital image forensics Region duplication detection Copy-move forgery Image segmentation Keypoint contexts
This work is supported by the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), the Fundamental Research Funds for the Central Universities (No. 16lgjc83), Scientific and Technological Achievements Transformation Plan of Sun Yat-sen University, the Research Project of Guangdong University of Finance and Economics (No. 10GL52001), Shanghai Sailing Program (No. 17YF1420000), the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University (Contract No. RCS2016K007), and the Science and Technology Development Fund of Macao SAR (No. 097/2013/A3).
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