Salient keypoint-based copy–move image forgery detection

Abstract

Copy–move forgery is one of the most common image forgeries in digital images. In copy–move forgery, some object or region of an object is copied and duplicated in other parts of the same image. Hence, there is a need to develop an accurate and robust forgery detection approach for various image forensics applications. In this article, an improved salient keypoint selection approach for copy–move forgery detection has been proposed. Scale-Invariant Feature Transform (SIFT) and KAZE keypoint features have been extracted from the input image and salient keypoints have been selected for improving the robustness of the proposed algorithm. Salient keypoint selection reduces the feature descriptor matching time for finding region duplication in the given image. To improve the detection accuracy of the proposed approach, selective search-based region proposals have been introduced to create a bounding box on the input image. Feature descriptor matching is performed between keypoints which are located inside two different bounding boxes. The proposed approach has been evaluated on two benchmark datasets, CoMoFoD and MICC-F220 and detection results outperformed state-of-the-art techniques under different geometric transformations and post-processing operations.