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.