Code-in-the-Loop Forensics: Agentic Tool Use for Image Forgery Detection

Abstract

Existing image forgery detection (IFD) methods either ex-ploit low-level, semantics-agnostic artifacts or rely on multimodal largelanguage models (MLLMs) with high-level semantic knowledge. Althoughnaturally complementary, these two information streams are highly hetero-geneous in both paradigm and reasoning, making it difficult for existingmethods to unify them or effectively model their cross-level interactions.To address this gap, we propose ForenAgent, a multi-round interactiveIFD framework that enables MLLMs to autonomously invoke, execute,and iteratively refine Python-based low-level tools around the detectionobjective, thereby achieving more flexible and interpretable forgery analy-sis. ForenAgent adopts a two-stage training pipeline with Cold Start andReinforcement Fine-Tuning to progressively improve tool interaction andreasoning adaptability. We design a human-inspired dynamic reasoningloop with global perception, local focusing, iterative probing, and holisticadjudication, and implement it as both a data-sampling strategy anda task-aligned process reward. To support training and evaluation, webuilt FABench, a heterogeneous agent-forensics dataset with 100k imagesand about 200k agent-interaction question-answer pairs. Experimentsshow that ForenAgent exhibits emergent tool-use competence and re-flective reasoning on challenging IFD tasks when assisted by low-leveltools, charting a promising route toward general-purpose IFD. The codeis available at https://github.com/zfr00/ForenAgent.