Keypoint-based copy-move forgery detection in digital images: a survey

Abstract

Copy-move (called copy-paste) is one of the most common image forgery, where one or more areas of an image are copied and pasted into another location of the same image. The objective of such a forgery is to hide useful elements and perform area duplication in some sections of an image. Thereby, copy-move forgery (CMF) poses a serious threat to society and forensic experts. Many methods have been proposed for copy-move forgery detection (CMF), which can be categorized into keypoint-based and block-based methods. Generally, the performance of keypoint-based methods is relatively higher in terms of computational efficiency, complexity, and robustness against many transformations. In this paper, a comprehensive survey of the recent keypoint-based methods based on Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Oriented FAST and Rotated Brief (ORB), KAZE, and Binary Robust Invariant Scalable Keypoints (BRISK) is presented.