Multiple Copy-Move Forgery Detection Based on Density Clustering

Abstract

We propose a new copy-move forgery detection method, which can solve the problems of multiple copy-move forgery, low accuracy and inaccurate tampered region location. First, keypoints and corresponding features of the image are extracted by using AKAZE (accelerated KAZE). Second, features are matched by using the Hamming distance and g2NN which can detect the multiple copy-move forgery. Then, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is used to cluster the keypoints and remove false matching. Finally, PSNR (peak signal-to-noise ratio) and morphological processing is used to locate tampered region accurately. Experimental results show that the proposed method performs well on geometric transformation, post-processing, multiple copy-move forgery, and tampered region localization.