Copy Move Forgery

Copy-move forgery detection aims to identify instances where parts of an image have been duplicated and pasted elsewhere, a common form of image manipulation. Current research focuses on improving the accuracy and robustness of detection algorithms, particularly addressing challenges like low-resolution images, seamlessly blended forgeries, and the need to distinguish genuine similar objects from manipulated regions. This is being tackled through various approaches, including keypoint-based methods, deep learning models (e.g., transformer networks and PatchMatch algorithms), and techniques leveraging entropy information or local moment invariants. Advances in this field are crucial for maintaining the integrity of digital images and have significant implications for digital forensics and multimedia security.