Robust copy-move forgery detection by false alarms control
This addresses the challenge of automated image tampering detection for forensic applications, representing an incremental improvement in robustness against transformations like rotation and compression.
The paper tackled the problem of reliably detecting copy-move forgeries in images by distinguishing natural self-similarities from artificial ones, achieving theoretical guarantees on false alarms through an a contrario method validated on multiple databases.
Detecting reliably copy-move forgeries is difficult because images do contain similar objects. The question is: how to discard natural image self-similarities while still detecting copy-moved parts as being "unnaturally similar"? Copy-move may have been performed after a rotation, a change of scale and followed by JPEG compression or the addition of noise. For this reason, we base our method on SIFT, which provides sparse keypoints with scale, rotation and illumination invariant descriptors. To discriminate natural descriptor matches from artificial ones, we introduce an a contrario method which gives theoretical guarantees on the number of false alarms. We validate our method on several databases. Being fully unsupervised it can be integrated into any generic automated image tampering detection pipeline.