Image copy–move forgery detection based on sped-up robust features descriptor and adaptive minimal–maximal suppression

Abstract

Region duplication is a simple and effective operation to create digital image forgeries, where a continuous portion of pixels in an image is copied and pasted to a different location in the same image. Many prior copy–move forgery detection methods suffer from their inability to detect the duplicated region, which is subjected to various geometric transformations. A keypoint-based approach is proposed to detect the copy–move forgery in an image. Our method starts by extracting the keypoints through a fast Hessian detector. Then the adaptive minimal–maximal suppression (AMMS) strategy is developed for distributing the keypoints evenly throughout an image. By using AMMS and a sped-up robust feature descriptor, the proposed method is able to deal with the problem of insufficient keypoints in the almost uniform area. Finally, the geometric transformation performed in cloning is recovered by using the maximum likelihood estimation of the homography. Experimental results show the efficacy of this technique in detecting copy–move forgeries and estimating the geometric transformation parameters. Compared with the state of the art, our approach obtains a higher true positive rate and a lower false positive rate.