Abstract: 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.‎

Ibrahim A. Zedan, Mona M. Soliman, Khaled M. Elsayed and Hoda M. Onsi, “Copy Move Forgery Detection Techniques: A Comprehensive Survey of Challenges and Future Directions” International Journal of Advanced Computer Science and Applications(IJACSA), 12(7), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120729

@article{Zedan2021,
title = {Copy Move Forgery Detection Techniques: A Comprehensive Survey of Challenges and Future Directions},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120729},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120729},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {7},
author = {Ibrahim A. Zedan and Mona M. Soliman and Khaled M. Elsayed and Hoda M. Onsi}
}