Resumen de Two-stage Keypoint Detection Scheme for Region Duplication Forgery Detection in Digital Images
Mahmoud Emam, Qi Han, Hongli Zhang
In digital image forensics, copy-move or region duplication forgery detection became a vital research topic recently. Most of the existing keypoint-based forgery detection methods fail to detect the forgery in the smooth regions, rather than its sensitivity to geometric changes. To solve these problems and detect points which cover all the regions, we proposed two steps for keypoint detection. First, we employed the scale-invariant feature operator to detect the spatially distributed keypoints from the textured regions. Second, the keypoints from the missing regions are detected using Harris corner detector with nonmaximal suppression to evenly distribute the detected keypoints. To improve the matching performance, local feature points are described using Multi-support Region Order-based Gradient Histogram descriptor. Based on precision– recall rates and commonly tested dataset, comprehensive performance evaluation is performed. The results demonstrated that the proposed scheme has better detection and robustness against some geometric transformation attacks compared with state-of-the-art methods.
