An improved method for SIFT-based copy-move forgery detection using non-maximum value suppression and optimized J-Linkage

In looking to improve the detection performance of the keypoint-based method involving smooth tampered regions, there are three problems to be addressed, namely the nonuniform distribution of the keypoints, the discriminative power of low contrast keypoints, and the high computational cost of clustering. In this study, the classical implementation framework of the keypoint-based method is improved by introducing new techniques and algorithms in order to overcome these problems. First, to acquire uniformly distributed keypoints in the test image, we propose a new solution of selecting the keypoints by region instead of contrast. To this end, we first separate the keypoint detection and selection processes. After obtaining all discernible keypoints, we adapt the non-maximum value suppression algorithm to select keypoints by combining the contrast and density of each keypoint. Second, we apply the opponent scale-invariant feature transform descriptor to enhance the discriminative power of keypoints by adding color information. Finally, to alleviate the computational cost of clustering, we optimize the J-Linkage algorithm by altering the method of computing initial clusters and affine transformation hypotheses. For this purpose, we propose the matched pair grouping algorithm that can obtain a smaller number of initial clusters by utilizing the correspondence between the superpixels in the original and duplicated regions. Experiments performed on three representative datasets confirm that the proposed method can significantly improve the detection performance in smooth tampered regions, and considerably reduce the clustering time in the case of a mass of matched pairs, compared with the state-of-the-art methods.