Copy-Move Forgery Detection Based on Keypoint Clustering and Similar Neighborhood Search Algorithm
Haipeng Chen, Xiwen Yang, Yingda Lyu
Abstract
Copy-move is one of the most commonly used methods of tampering with digital images. Keypoint-based detection is recognized as effective in copy-move forgery detection (CMFD). This paper proposes an efficient CMFD method via clustering SIFT keypoints and searching the similar neighborhoods to locate tampered regions. In the proposed method, the keypoints are clustered based on scale and color, grouped into several smaller clusters and matched separately, which reduce the high time complexity caused in matching caused by the high dimensionality of SIFT. In order to locate the tampered regions accurately at pixel level finally, a novel localization algorithm is designed to compare the similar neighborhoods of matching pairs by two similarity measures, and mark the tampered regions in pixels iteratively. We experimented on three different image data sets including kinds of tampering means to compare and verify the effectiveness and robustness of proposed method. The experimental results show that the proposed method is superior to existing state-of-art methods in terms of matching time complexity, detection reliability and forgery location accuracy.