RoGRE: Image Manipulation Localization as Controlled Hypothesis Revision

Image manipulation localization (IML) requires combining heterogeneous evidence with different localization capabilities and failure modes. Existing methods typically fuse such representations as equal-status features, obscuring how each source should modify the current prediction. We instead formulate IML as controlled hypothesis revision: localization begins from an initial estimate, and complementary evidence progressively corrects its remaining errors. Based on this view, we introduce RoGRE, which maintains a shared logit-valued localization state and revises it through role-specific stages. DINOv3 first establishes an initial hypothesis, SAM2 features correct its spatial organization, and forensic residual features refine manipulation evidence that remains weakly represented. Each refinement proposes a bounded signed correction whose magnitude is regulated by a gate conditioned on both the incoming state and stage-specific evidence, allowing informative revisions while limiting unreliable updates. The evolved state is finally decoded by SAM2 into the manipulation mask. RoGRE achieves mean pixel-level F1 scores of 0.888 and 0.813 under the CAT-Net and MVSS-Net protocols, respectively. Ablations further validate the correction order and state-dependent gating, supporting controlled hypothesis revision as an effective approach to heterogeneous evidence integration for IML.