REVEAL: Reasoning-Enhanced Forensic Evidence Analysis for Explainable AI-Generated Image Detection

摘要

The rapid progress of visual generative models has madeAI-generated images increasingly difficult to distinguish from authen-tic ones, posing growing risks to social trust and information integrity.This motivates detectors that are not only accurate but also forensicallyexplainable. While recent multimodal approaches improve interpretabil-ity, many rely on post-hoc rationalizations or coarse visual cues, withoutconstructing verifiable chains of evidence, thus often leading to poor gen-eralization. We introduce REVEAL-Bench, a reasoning-enhanced multi-modal benchmark for AI-generated image forensics, structured aroundexplicit chains of forensic evidence derived from lightweight expert mod-els and consolidated into step-by-step chain-of-evidence traces. Basedon this benchmark, we propose REVEAL (Reasoning-enhanced Foren-sic Evidence Analysis), an explainable forensic framework trained withexpert-grounded reinforcement learning. Our reward design jointly pro-motes detection accuracy, evidence-grounded reasoning stability, and ex-planation faithfulness. Extensive experiments demonstrate significantlyimproved cross-domain generalization and more faithful explanations tobaseline detectors. All data and source codes are publicly available at:https://github.com/TrustMedia-zju/REVEAL.