ForgePan-5.9M: a Generalizable Benchmark for Image Forgery Detection and Localization

Rapid advancements in AI-Generated Content (AIGC) are blurring the boundaries of reality and the synthetic. AIGC can be a fully generated image, partially manipulated by either AI or physical editing tools, or a mixture of both, which we term “hybrid edits”. Despite the increasing volume of AIGC detection benchmarks, prior efforts struggle to generalize to the granularity of manipulation types, such as detectors being reliable for fully generated images may perform poorly on partially edited images, or vice versa, nor is there a unified detection model that can generalize well across different real-world manipulations in terms of both detection and localization. Existing methods generalize poorly to hybrid edits which are multiturn edited images with both physical edit and AI edit. We first introduce ForgePan-5.9M, a large-scale detection and localization benchmark featuring data-aligned samples across multiple manipulation types, such as AI editing, physical copy/move, and full-image generation, enabling unified evaluation of both manipulation detection and localization. This aligned dataset aided with proposed ForgeFormer that learns pixel-level intrinsic fingerprints associated with diffusion, GANs, or physically edited forgeries and ForgeFormer simultaneously achieves state-of-the-art in identifying and localizing unseen fully generated AI images and partial manipulations by either AI or physical edits and generalize well on hybrid edits. Notably, ForgeFormer achieves 1st rank with 87% balanced accuracy on the IEDAL2 challenge dataset, further validating its effectiveness in realistic, heterogeneous forgery scenarios.