Image copy-move forgery detection using hierarchical feature point matching

Copy-move forgery is one of the most commonly used manipulations for tempering digital images. Keypoint-based detection methods have been reported to be very effective in revealing copy-move evidences, due to their robustness against geometric transforms. However, these methods fail to handle the cases when copy-move forgery only involves small or smooth regions, where the number of keypoints is very limited. To tackle this challenge, we propose a simple yet effective copy-move forgery detection approach. By lowering the contrast threshold and rescaling the input image, we first generate a sufficient number of keypoints that exist even in the small or smooth regions. Then, a novel hierarchical matching strategy is developed for solving the keypoint matching problems. Finally, a novel iterative homography estimation technique is suggested through exploiting the dominant orientation information of each keypoint. Extensive experimental results are provided to demonstrate the superior performance of the proposed scheme.