Code-in-the-Loop Forensics: Agentic Tool Use for Image Forgery Detection
Fanrui Zhang, Qiang Zhang, Sizhuo Zhou, Jianwen Sun, Chuanhao Li, Jiaxin Ai, Yukang Feng, Yujie Zhang, Wenjie Li, Zizhen Li, Yifan Chang, Jiawei Liu, Kaipeng Zhang

TL;DR
The paper introduces ForenAgent, an interactive framework enabling large language models to autonomously generate and refine tools for more flexible and interpretable image forgery detection, bridging high-level semantics and low-level artifacts.
Contribution
It presents a novel multi-round interactive IFD framework with a training pipeline and a new dataset, enhancing tool interaction and reasoning in forgery detection.
Findings
ForenAgent demonstrates emergent tool-use competence in challenging IFD tasks.
The framework achieves more flexible and interpretable forgery analysis.
Experiments validate the effectiveness of the dynamic reasoning loop.
Abstract
Existing image forgery detection (IFD) methods either exploit low-level, semantics-agnostic artifacts or rely on multimodal large language models (MLLMs) with high-level semantic knowledge. Although naturally complementary, these two information streams are highly heterogeneous in both paradigm and reasoning, making it difficult for existing methods to unify them or effectively model their cross-level interactions. To address this gap, we propose ForenAgent, a multi-round interactive IFD framework that enables MLLMs to autonomously generate, execute, and iteratively refine Python-based low-level tools around the detection objective, thereby achieving more flexible and interpretable forgery analysis. ForenAgent follows a two-stage training pipeline combining Cold Start and Reinforcement Fine-Tuning to enhance its tool interaction capability and reasoning adaptability progressively.…
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