Copy Move Forgery Detection Techniques: A Comprehensive Survey of Challenges and Future Directions

Digital Image Forensics is a growing field of image processing that attempts to gain objective proof of the origin and veracity of a visual image. Copy-move forgery detection (CMFD) has currently become an active research topic in the passive/blind image forensics field. There has no doubt that conventional techniques and especially the keypoint based techniques have pushed the CMFD forward in the previous two decades. However, CMFD techniques in general and conventional techniques in particular suffer from several challenges. And thus, increasing approaches are exploiting deep learning for CMFD. In this survey, we cover the conventional and the deep learning based CMFD techniques from a new perspective. We classify the CMFD techniques into several classifications according to the detection methodology, the detection paradigm, and the detection capability . We discuss the challenges facing the CMFD techniques as well as the ways for solving them. In addition, this survey covers the evaluation metrics and datasets commonly utilized for CMFD. Also, we are debating and proposing certain plans for future research. This survey will be helpful for the researchers’ as it master the recent trends of CMFD and outline some future research directions. Keywords—Image forensics; copy-move forgery detection (CMFD); conventional techniques; deep learning techniques