标题： 通过感知拉直进行人工智能生成视频检测 显示英文标题

标题： AI-Generated Video Detection via Perceptual Straightening

摘要： 生成式AI的快速发展使得高度逼真的合成视频成为可能，这对内容认证提出了重大挑战，并引发了对滥用的紧急担忧。 现有的检测方法通常在泛化能力和捕捉细微的时间不一致性方面存在困难。 我们提出了一种名为ReStraV（Representation Straightening Video）的新方法，用于区分自然视频和AI生成的视频。 受“感知直线化”假设的启发——该假设认为现实世界的视频轨迹在神经表示域中会变得更加直线——我们分析了与这种预期几何特性的偏差。 使用预训练的自监督视觉变压器（DINOv2），我们量化了模型表示域中的时间曲率和分步距离。 我们为每段视频聚合这些度量的统计信息并训练分类器。 我们的分析表明，AI生成的视频在曲率和距离模式上与真实视频有显著不同。 一个轻量级分类器实现了最先进的检测性能（例如，在VidProM基准测试中准确率为97.17%，AUROC为98.63%），明显优于现有的基于图像和视频的方法。 ReStraV计算效率高，提供了一种低成本且有效的检测解决方案。 这项工作为利用神经表示几何进行AI生成视频检测提供了新的见解。 显示英文摘要

摘要： The rapid advancement of generative AI enables highly realistic synthetic videos, posing significant challenges for content authentication and raising urgent concerns about misuse. Existing detection methods often struggle with generalization and capturing subtle temporal inconsistencies. We propose ReStraV(Representation Straightening Video), a novel approach to distinguish natural from AI-generated videos. Inspired by the "perceptual straightening" hypothesis -- which suggests real-world video trajectories become more straight in neural representation domain -- we analyze deviations from this expected geometric property. Using a pre-trained self-supervised vision transformer (DINOv2), we quantify the temporal curvature and stepwise distance in the model's representation domain. We aggregate statistics of these measures for each video and train a classifier. Our analysis shows that AI-generated videos exhibit significantly different curvature and distance patterns compared to real videos. A lightweight classifier achieves state-of-the-art detection performance (e.g., 97.17% accuracy and 98.63% AUROC on the VidProM benchmark), substantially outperforming existing image- and video-based methods. ReStraV is computationally efficient, it is offering a low-cost and effective detection solution. This work provides new insights into using neural representation geometry for AI-generated video detection.