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

• We have developed a new and effective approach to detect copy-move forgeries.
• Our method is able to deal with rotations, resize and compression simultaneously.
• Our method report a small false positive rate detection.
• We have developed a dataset comprising 216 realistic copy-move forgeries.
• Method tested against 15 state-of-the-art methods and using different datasets.

This work presents a new approach toward copy-move forgery detection based on multi-scale analysis and voting processes of a digital image. 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 the 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. We compare the proposed method to 15 others from the literature and report promising results.