DG-Force: Disentangling and Gathering Forensic Cues is Needed for Image Manipulation Localization

Image Manipulation Localization (IML) aims to achieve pixel-level localization of locally manipulated images. Its core challenge is howto distinguish the inconsistencies between authentic and tampered ar-eas. To this end, existing IML methods typically exploit patch-level andedge-level forensic cues for accurate localization. However, these methodsextract forged information at both levels with fixed parameters, leadingto interference from real information that is detrimental to localizationas more realistic manipulated textures are enhanced by the upgrading oftampering techniques. To tackle the above issue, this paper proposes aDisentangling and Gathering Forensic Cues method, called DG-Force,which explicitly disentangles and adaptively aggregates region and edgecues to preserve the most informative evidence. Specifically, we proposea Patch-based Forensic Disentangling (PFD) module and an Edge-basedForensic Disentangling (EFD) module to decompose forensic cues of dif-ferent granularities to explicitly suppress the imbalance between forensictraces in both patch-level and edge-level areas. In addition, a Multi-scale Forensic Transfer (MFT) module is further designed to aggregateand balance multi-granularity information through the interaction acrossdifferent scales for robust key inconsistencies in forensic traces discovery.Extensive experiments on multiple benchmarks demonstrate the superi-ority of our method in the image manipulation localization task.