Asymmetric Anchoring: Opening the Black Box of MLLMs for Forgery Detection

Multimodal large language models (MLLMs) are promisingfor forgery detection, but most methods treat them as fixed black boxes.Preliminary studies have shown that this passive use overlooks instabili-ties in the internal data flow, leading to representation drift. In this work,we introduce the Asymmetric Anchoring Paradigm (AAP), an open-boxapproach that reshapes the MLLM’s internal data flow (rather than ap-pending external components) by re-purposing its pre-trained visual en-coder as an active truth anchor. AAP has two key steps: (1) for realimages, we impose an anchor-alignment constraint that pulls represen-tations toward the truth anchor, yielding a highly stable, low-variancemanifold; (2) for tampered images, we measure their deviation from theanchor and use the resulting error map, which spatially quantifies depar-tures from real-world priors, as a precise cue for the localization decoder.Comprehensive evaluations on SID-Set and OpenSDID demonstrate thatAAP substantially improves detection accuracy and the localization IoUwithin manipulated regions. By opening the black box and anchoring tothe encoder’s real-world priors, AAP turns MLLMs from passive compo-nents into actively regularized detectors for practical forgery detection,offering a new insight for the field. The code is open-sourced and publiclyavailable at https://github.com/rstao-bjtu/AAP.