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.

翻译：大多数技术将图像伪造定位问题视为二元分割任务，通过训练神经网络将原始区域标记为0、伪造区域标记为1进行处理。与此不同，我们从更本质的视角出发，依据图像的来源对图像进行划分。为此，我们提出了分割任意伪造图像区域（SAFIRE）方法，该方法利用点提示解决伪造定位问题。图像上的每个点都被用于分割包含其自身的来源区域。这使得我们能够将图像划分为多个来源区域，这是首次实现的能力。此外，SAFIRE并非记忆特定的伪造痕迹，而是自然地关注每个来源区域内部的统一特征。这种方法带来了更稳定、更有效的学习效果，在新型任务和传统的二元伪造定位任务中均实现了卓越的性能。