VidForensics-M1: AI-Generated Video Detection and Forensics via Verifiable Meta-Detection Reinforcement Learning

Recent advances in video generation models have dramatically improved the realism of synthetic videos, blurring the boundary between generated and authentic content and raising significant concerns about misinformation. Existing MLLM-based detectors predominantly rely on supervised fine-tuning or label-level reinforcement learning, where predefined or coarse-grained supervision signals limit their generalization to out-of-domain scenarios and emerging video generators. To overcome these limitations, we are the first to introduce **meta-detection** into AI-generated video detection, jointly supervising label correctness and evidence correctness during reinforcement learning to enable more reliable forgery detection. This paradigm presents two core challenges: (1) identifying evidence forms that provide reliable and scalable supervision for meta-detection, and (2) developing effective mechanisms to integrate such evidence into label-level reinforcement learning for trustworthy and generalizable synthetic video detection. Textual rationales offer semantically rich descriptions of forgery artifacts, yet their generation and verification rely heavily on external reference models, making the resulting supervision vulnerable to hallucinations and semantic biases. This motivates us to seek an evidence representation with verifiable ground truth to support rule-based meta-detection supervision. Temporal grounding meets this requirement because manipulation intervals can be precisely controlled and recorded during data construction. Building on this insight, we adopt temporal grounding as a more reliable meta-detection signal and propose an automated data construction pipeline that generates paired real and fake videos by reconstructing and replacing temporal segments using boundary-frame-conditioned video generation models. Furthermore, we propose **Evidence-Guided Reward Redistribution**, which performs evidence-aware credit assignment by redistributing rewards among label-correct responses according to their evidence quality, thereby preserving reliable label supervision while progressively encouraging the detector to acquire fine-grained and verifiable forgery localization capabilities. Extensive experiments demonstrate that **VidForensics-M1** effectively leverages verifiable temporal evidence to achieve more robust and generalizable AI-generated video detection.