Copy-Move Forgery Detection Based On Key Point Clustering And Similar Neighborhood Search Algorithm

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).Hence, this paper introduces a new robust algorithm to detect copy-move forgery based on Speeded Up Robust Feature (SURF) descriptor, high pass filtering as a feature matching, Nearest Neighbor (NN) used as a clustering algorithm to divide the whole image into superpixel blocks. The doubted regions are determined by replacing the matched feature points with corresponding superpixel blocks then the neighboring blocks have been merged based on similar Local Color Features (LCF). Finally, the morphological close operation was applied to elicit the doubted forged regions. We experimented on image forgery data set including kinds of tampering means to compare and verify the effectiveness and robustness of the 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.