VideoVeritas: AI-Generated Video Detection via Perception Pretext Reinforcement Learning

Abstract

The growing capability of video generation poses escalating security risks, making reliable detection increasingly essential. In this paper, we introduce **VideoVeritas**, a framework that integrates fine-grained perception and fact-based reasoning. We observe that while current multi-modal large language models (MLLMs) exhibit strong reasoning capacity, their granular perception ability remains limited. To mitigate this, we introduce *Joint Preference Alignment* and *Perception Pretext Reinforcement Learning (PPRL)*. Specifically, rather than directly optimizing for detection task, we adopt general spatiotemporal grounding and self-supervised object counting in the RL stage, enhancing detection performance with simple *perception pretext tasks*. To facilitate robust evaluation, we further introduce **MintVid**, a light yet high-quality dataset containing 3K videos from 9 state-of-the-art generators, along with a real-world collected subset that has factual errors in content. Experimental results demonstrate that existing methods tend to bias towards either *superficial* reasoning or *mechanical* analysis, while **VideoVeritas** achieves more balanced performance across diverse benchmarks.