A fast technique to detect copy-move image forgery with reflection and non-affine transformation attacks

Abstract

Copy-move forgery is one of the most frequently utilized image tampering technique which uses the segment of the same image to produce manipulated image by duplicating or concealing image regions. To remove suspicious traces of forgery, various attacks are applied over the tampered image which make forgery detection process too complicated. We propose a forgery detection technique in which Center Surround Extrema (CenSurE) detector is applied for keypoint detection from images. To compute keypoint descriptors, Local Image Permutation Interval Descriptor (LIPID) is used. Keypoint matching is performed using k-Nearest Neighbor (k-NN) technique with utilization of k-d tree and Best-Bin-First (BBF) search method. Grouping over keypoints is performed using Fuzzy C-Means (FCM) clustering. We apply Random Sample Consensus (RANSAC) algorithm to remove outliers obtained during forgery detection process. Experimental results show that proposed technique can effectively detect forged images containing reflection and non-affine transformation with geometrical attacks. In addition, proposed approach also shows robustness against erosion, dilation, RGB color addition, zoom motion blur, JPEG compression, spread noise addition, and multiple copy-move attacks. Proposed scheme consumes least time in forgery detection as compared to state-of-the-art methods.