Learning Universal Feature for Generalizable Image Forgery Localization

The rapid advancement of image generation and editing techniques has rendered the detection and precise localization of forged content an increasingly demanding task. Existing approaches predominantly rely on learning forgery-specific artifacts, which limits their ability to generalize to unseen manipulation types. In this work, we introduce a Generalizable Image Forgery Localization (GIFL) framework that reframes the problem: rather than seeking manipulation traces, we propose to model the intrinsic distribution of authentic image content. GIFL learns a universal, content-consistent representation from pristine regions, organizes the feature space to naturally separate manipulated areas, and constructs a cohesive representation of authenticity that generalizes across diverse forgery types. To further improve robustness, we design a dual-domain interaction module that integrates complementary spectral and spatial cues for reliable localization. Additionally, to advance research on forgeries produced by modern deep generative models, we present Forgery ADE, a new comprehensive dataset containing images edited with a variety of popular deep image editing methods. Extensive experiments demonstrate that our method outperforms existing methods in localizing unseen forgeries also demonstrates competitive results on trained manipulation types, offering a more practical and robust solution for image authenticity verification in the era of generative AI.