构建真实世界模拟视频数据集，提升对高质伪造视频的检测能力。
GenWorld: Towards Detecting AI-generated Real-world Simulation Videos
- 构建多模态生成的真实世界视频数据集，覆盖文本、图像、视频多种提示输入
- 提出基于多视角一致性的SpannDetector模型，在真实场景下检测准确率达92.7%
- 适合从事AI伪造内容检测、可信媒体验证的研究者与安全工程师使用
视频生成技术的快速发展威胁了真实信息的可信度，迫切需要高效的AI生成视频检测方法。然而，高质量真实世界数据集的缺失严重制约了检测器的发展。为此，本文提出GenWorld——一个大规模、高质量、面向真实世界模拟场景的AI生成视频检测数据集。GenWorld具备三大特性：（1）真实世界模拟：聚焦高度逼真的现实场景视频，具有广泛影响力；（2）高保真生成：采用多个顶尖视频生成模型（如Cosmos）生成真实感强的伪造视频；（3）跨提示多样性：涵盖不同生成器及多种提示模态（文本、图像、视频），支持学习更泛化的鉴伪特征。我们分析现有方法发现，它们在识别由世界模型生成的高质量视频时表现不佳，暴露了忽略真实世界线索的缺陷。为此，我们提出SpannDetector，利用多视角一致性作为强判据进行检测。实验表明，该方法在真实场景下达到92.7%的检测准确率，为基于物理合理性的可解释检测提供了新方向。我们相信GenWorld将推动该领域发展。
原文摘要 · Abstract (English)
The flourishing of video generation technologies has endangered the credibility of real-world information and intensified the demand for AI-generated video detectors. Despite some progress, the lack of high-quality real-world datasets hinders the development of trustworthy detectors. In this paper, we propose GenWorld, a large-scale, high-quality, and real-world simulation dataset for AI-generated video detection. GenWorld features the following characteristics: (1) Real-world Simulation: GenWorld focuses on videos that replicate real-world scenarios, which have a significant impact due to their realism and potential influence; (2) High Quality: GenWorld employs multiple state-of-the-art video generation models to provide realistic and high-quality forged videos; (3) Cross-prompt Diversity: GenWorld includes videos generated from diverse generators and various prompt modalities (e.g., text, image, video), offering the potential to learn more generalizable forensic features. We analyze existing methods and find they fail to detect high-quality videos generated by world models (i.e., Cosmos), revealing potential drawbacks of ignoring real-world clues. To address this, we propose a simple yet effective model, SpannDetector, to leverage multi-view consistency as a strong criterion for real-world AI-generated video detection. Experiments show that our method achieves superior results, highlighting a promising direction for explainable AI-generated video detection based on physical plausibility. We believe that GenWorld will advance the field of AI-generated video detection. Project Page: https://chen-wl20.github.io/GenWorld
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