SafeGuard: A Multi-Agent Perception-Reasoning Framework for Social-Risk AI-Generated Video Detection

As video generation paradigms evolve from localized manip-ulation to full-scene synthesis, AI-generated video detection becomes in-creasingly challenging, as forgeries exhibit coherent global structure andhigh perceptual realism. However, existing benchmarks are biased towardperceptual fidelity and primarily evaluate detectors based on perceptualartifacts, providing limited coverage of scenarios that require reasoningabout violations of physical laws, structural coherence, or social logic.This dataset bias shapes current approaches and results in a Percep-tion–Reasoning Gap: artifact-centric models capture low-level statisticalirregularities yet lack semantic inference, whereas vision-language mod-els perform semantic reasoning but remain insensitive to fine-grainedforensic cues. To bridge this gap, we propose SafeGuard, a multi-agentframework that enables collaborative specialization between forensic per-ception and semantic reasoning. A hierarchical perceptual solver extractsfine-grained forensic evidence, while a self-reflective verifier enforces con-sistency between semantic inference and physical plausibility, forming aninterpretable evidence chain. To support evaluation, we introduce Safe-Vid, a novel AI-generated video detection benchmark comprising 20Kvideos spanning 10 social risk categories, designed to evaluate physicalplausibility, structural consistency, and the rationality of social behav-iors. Extensive experiments demonstrate the generalization of SafeGuard,improving accuracy on SafeVid by +18.7% and consistently outperform-ing prior methods across four public benchmarks. The code and datasetare publicly available at https://github.com/williamw99/SafeGuard.