SAFIRE: Segment Any Forged Image Region
This addresses the problem of detecting forged regions in images for security and forensics applications, introducing a novel approach beyond incremental improvements.
The paper tackles image forgery localization by partitioning images based on their originating sources using point prompting, achieving superior performance in both the new multi-source segmentation task and traditional binary localization.
Most techniques approach the problem of image forgery localization as a binary segmentation task, training neural networks to label original areas as 0 and forged areas as 1. In contrast, we tackle this issue from a more fundamental perspective by partitioning images according to their originating sources. To this end, we propose Segment Any Forged Image Region (SAFIRE), which solves forgery localization using point prompting. Each point on an image is used to segment the source region containing itself. This allows us to partition images into multiple source regions, a capability achieved for the first time. Additionally, rather than memorizing certain forgery traces, SAFIRE naturally focuses on uniform characteristics within each source region. This approach leads to more stable and effective learning, achieving superior performance in both the new task and the traditional binary forgery localization.