Image copy‐move forgery detection using sparse recovery and keypoint matching

Abstract

Copy-move forgery (CMF) detection is one of the most practical problems in image forensics. The authenticity of the image becomes more crucial when the images are used in the criminal investigations, intelligence services, and medical documentations. In this study, the authors suggest a CMF detection algorithm. At first they they suggest to use a sparse recovery algorithm to identify the suspicious segments. To incorporate the colour information of the image segments, they propose to compare the histograms of the identified segments to detect the similar ones. The keypoints of those parts are obtained and the matched ones are located. In the last step, they suggest a morphology scheme to extract the forged region. They have evaluated the proposed method in the detection of various forged images. The simulation results reveal the forgery detection capabilities of the suggested algorithm compared to the other state-of-the-art schemes. The proposed method has superiority over its counterparts in detecting the scaled forgeries. Moreover, the sparse recovery step enables the proposed algorithm to remove the genuine repeated patterns of the image, while the other CMF detection techniques wrongly consider those parts as a forgery. Furthermore, the proposed scheme is on-average faster than the other schemes.