A Novel Image Copy–Move Forgery Detection Algorithm Using the Characteristics of Local Descriptors

Abstract

The image copy–move forgery can achieve the realistic effects without leaving any obvious manipulation traces because the copied source and pasted target belong to the homogenous sources. Image copy–move forgery detection (CMFD) is still a serious challenge issue for researchers, e.g. computational efficiency, occlusive (smoothing) object detection, and partial object detection, under various post-processing and geometrical transformations. This paper proposes a novel CMFD scheme using the characteristics of local descriptors. First, the proposed method deeply analyzes the structure and mines the inherent characteristics of the local descriptor (SURF) for feature extraction in the coarse and smooth regions. Subsequently, the proposed method takes the kernel features as coarse feature matching to decrease matching costs. Then, the found candidate keypoints with a relatively small quantity are used with complete features to perform fine keypoint matching for getting suspicious candidate keypoint pairs. Furthermore, based on the deep mining of the inherent local descriptor characteristics, the proposed method can indicate the forgery region localizations through keypoint pairs, even the forgeries suffering from various geometrical transformations. Then, the proposed method proposes adaptive dual-filtering algorithms of [Formula: see text]-means relying on keypoint characteristics, and spatial distance for hierarchical progress of feature filtering. Finally, Delaunay Triangulation relies on the true-positive keypoint pairs to perform the forgery region matting effectively and efficiently. The experimental results demonstrate that the proposed method can achieve the best F1 scores compared to the state-of-the-art methods in most cases, especially in anti-scaling transformations.