An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal

Copy-move image forgery detection has become a significant research subject in multimedia forensics and security due to its widespread use and its hard detection. In this type of image forging, a region of the image is copied and pasted elsewhere in the same image. Keypoint-based forgery detection approaches use local visual features to identify the duplicated regions. The performance of keypoint-based methods degrades in those cases when the duplicated regions are near to each other and when handling highly textured area. The clustering algorithm that mostly used in keypoint-based methods suf-fer from high complexity. In this paper, an improved approach for keypoint-based copy-move forgery detection is proposed. The proposed method is based on density-based clustering and Guaranteed Outlier Removal algorithm. Experimental results carried out on various benchmark datasets exhibit that the proposed method surpasses other similar state-of-the-art techniques under different challenging conditions, such as geometric attacks, post-processing attacks, and multiple clonin (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).