Going deeper into copy-move forgery detection: exploring image telltales via multi-scale analysis and voting processes

Given a suspicious image, we extract interest points robust to scale and rotation finding possible correspondences among them. We cluster correspondent points into regions based on geometric constraints.

Thereafter, we construct a multi-scale image representation and for each scale, we examine the generated groups using a descriptor strongly robust to rotation, scaling and partially robust to compression, which decreases search space of duplicated regions and yields a detection map. The final decision is based on a voting process among all detection maps. We validate the method using various datasets comprising original and realistic image clonings.