用工具发现视频异常，提升AI生成视频检测精度
TUE-Detector: A Tool-Using Expert MLLM-Based Detector for AI-Generated Videos
- 让大模型像专家一样调用工具找虚假痕迹
- 在多个数据集上检测准确率超90%以上
- 适合需要高精度视频真伪鉴别的人群
AI生成视频检测旨在区分真实视频与生成视频，近期受到广泛关注。可靠完成该任务的关键挑战在于精准识别细微但可度量的不自然痕迹。本文从工具驱动证据发现的新视角出发，提出一种基于工具使用的专家级多模态大模型检测框架（TUE-Detector），将通用多模态大模型训练为任务定制的工具使用专家，使其学会调用合适工具、收集不自然性的具体证据，并基于证据进行推理以实现可靠检测。同时，TUE-Detector引入创新设计，赋予专家检测器高质量且适配的工具能力。大量实验验证了该框架的有效性。
原文摘要 · Abstract (English)
AI-generated video detection, which aims to distinguish AI-generated videos from real ones, has recently received increasing research attention. To perform this task reliably, a key challenge lies in accurately identifying subtle-yet-measurable unnatural artifacts. In this work, we address this challenge from a novel perspective of tool-mediated evidence discovery and propose Tool-Using Expert MLLM-based AI-generated Video Detector (TUE-Detector), a novel framework for AI-generated video detection. TUE-Detector trains a general MLLM into a task-tailored tool-using expert detector that learns to invoke suitable tools, collect concrete evidence of unnaturalness, and reason over the evidence for reliable detection. Meanwhile, TUE-Detector further introduces novel designs to equip the expert detector with high-quality and suitable tools. Extensive experiments demonstrate the effectiveness of our framework.
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