用点提示分割伪造图像源区域，首次实现多源分区定位
SAFIRE: Segment Any Forged Image Region
- 通过点提示分割图像，按来源划分多个区域
- 在新任务和传统二分类上均表现更优
- 不依赖记忆痕迹，聚焦区域统一特征
现有方法将图像伪造定位视为二值分割任务，训练神经网络将原始区域标记为0，伪造区域标记为1。本文从更根本的角度出发，根据图像来源进行分区。提出SAFIRE（Segment Any Forged Image Region），利用点提示实现伪造区域的分割：每个点用于分割包含该点的来源区域。这首次实现了图像的多源区域划分。此外，SAFIRE不依赖特定伪造痕迹的记忆，而是自然关注各来源区域内的统一特征，从而实现更稳定有效的学习，在新任务和传统二值伪造定位任务中均取得更优性能。
原文摘要 · Abstract (English)
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.
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