多智能体取证推理用于泛化深度伪造视频检测

摘要

恶意使用生成式人工智能制造高度逼真的深度伪造视频，引发了严重的伦理关切，并对AI安全构成了重大挑战。然而，现有的深度伪造视频基准测试对近期合成方法的覆盖有限，且普遍缺乏可靠的细粒度文本标注。与此同时，传统检测器和多模态大语言模型（MLLMs）——无论是作为单一模型运行还是依赖单一分析视角——往往难以捕捉细微的伪造痕迹，限制了其向新兴AI生成方法的泛化能力。为解决上述局限，我们提出了FaceVid-Forensics-100K，一个大规模深度伪造视频数据集，包含10万条视频，涵盖人脸交换、人脸重建和全脸合成三大类共33种合成方法，包括Seedance 2.0等近期生成器。该数据集提供了视觉观察的细粒度文本标注和判决一致的取证解释，这些标注通过由先进MLLMs驱动的多模型聚合与冲突消解流程自动合成。基于该基准，我们提出了一个多智能体取证推理框架，采用四个专门的领域专家智能体，分别从纹理、光照、运动和物理四个视角独立分析伪造线索。随后，一个裁判智能体对它们的报告进行协调，以生成最终预测及解释。在跨域测试集上的广泛评估表明，尽管我们的框架完全由小型开源MLLMs组成，其性能仍优于包括闭源GPT和Gemini模型在内的所有方法，并在该基准的所有报告指标上排名第一。项目页面可在https://xavierjiezou.github.io/ARGUS/访问。

English

The malicious use of generative artificial intelligence to create highly realistic deepfake videos raises serious ethical concerns and poses substantial challenges to AI safety. However, existing deepfake video benchmarks provide limited coverage of recent synthesis methods and generally lack reliable fine-grained textual annotations. Meanwhile, conventional detectors and multimodal large language models (MLLMs), whether operating as a single model or relying on a single analytical perspective, often fail to capture subtle forgery artifacts, limiting their generalization to emerging AI-generated methods. To address these limitations, we introduce FaceVid-Forensics-100K, a large-scale deepfake video dataset comprising 100,000 videos and spanning 33 synthesis methods across face swapping, face reenactment, and entire-face synthesis, including recent generators such as Seedance 2.0. The dataset provides fine-grained textual annotations of visual observations and verdict-consistent forensic explanations, automatically synthesized through a multi-model aggregation and conflict-resolution pipeline powered by advanced MLLMs. Building on this benchmark, we propose a multi-agent forensic reasoning framework that employs four specialized domain-expert agents to independently analyze forgery cues from four perspectives: texture, lighting, motion, and physics. A judge agent then reconciles their reports to produce a final prediction together with an explanation. Extensive evaluations on out-of-domain test sets show that, despite being composed entirely of small open-source MLLMs, our framework outperforms all methods including closed-source GPT and Gemini models and ranks first across all reported metrics on this benchmark. The project page is available at https://xavierjiezou.github.io/ARGUS/.