A Keypoint-Based Technique for Detecting the Copy Move Forgery in Digital Images

A decade of research has been conducted on detecting copy-move forgeries (CMFD). Technology has enabled the manipulation of images, once the most authentic source of information. This paper proposes a copy-move forgery detection algorithm based on fused features to address issues such as time complexity and difficulty detecting forgeries in smooth regions. To extract descriptive features, a low-contrast threshold was used in conjunction with three detection methods, including scale-invariant feature transform (SIF), speeded-up robust features (SURF), and accelerated KAZE (AKAZE). SURF and accelerated KAZE (AKAZE) are used in our keypoint-based CMFD technique. To detect manipulated regions efficiently, AKZAE, SURF, and SIFT can be used to extract major keypoints in smooth regions.