DeMamba：基于百万规模 GenVideo 基准的 AI 生成视频检测
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark
AI总结：
本文提出首个百万规模AI生成视频检测数据集GenVideo，并设计即插即用模块DeMamba，通过分析时空不一致性增强检测能力，在泛化性与鲁棒性上显著优于现有方法。
摘要：
近期，视频生成技术发展迅速。鉴于视频内容在社交媒体平台上的流行，这些模型加剧了人们对虚假信息传播的担忧。因此，对于能够区分虚假 AI 生成视频并减轻虚假信息造成的潜在危害的检测器的需求日益增长。然而，缺乏来自最先进视频生成器的大规模数据集，成为了此类检测器发展的障碍。为了填补这一空白，我们引入了首个 AI 生成视频检测数据集 GenVideo。它具有以下特征：(1) 视频数量庞大，包括超过一百万个收集到的 AI 生成视频和真实视频；(2) 生成内容和方法丰富多样，涵盖了广泛的视频类别和生成技术。我们对数据集进行了广泛研究，并提出了两种针对类真实世界场景的评估方法来评估检测器的性能：跨生成器视频分类任务评估已训练检测器在生成器上的泛化能力；退化视频分类任务评估检测器处理在传播过程中质量下降的视频的鲁棒性。此外，我们引入了一个名为 Detail Mamba (DeMamba) 的即插即用模块，旨在通过分析时空维度上的不一致性来识别 AI 生成视频，从而增强检测器。我们的大量实验表明，与现有检测器相比，DeMamba 在 GenVideo 上具有卓越的泛化能力和鲁棒性。我们相信 GenVideo 数据集和 DeMamba 模块将显著推动 AI 生成视频检测领域的发展。我们的代码和数据集将在 \url{https://github.com/chenhaoxing/DeMamba} 公开。
英文摘要：
Recently, video generation techniques have advanced rapidly. Given the popularity of video content on social media platforms, these models intensify concerns about the spread of fake information. Therefore, there is a growing demand for detectors capable of distinguishing between fake AI-generated videos and mitigating the potential harm caused by fake information. However, the lack of large-scale datasets from the most advanced video generators poses a barrier to the development of such detectors. To address this gap, we introduce the first AI-generated video detection dataset, GenVideo. It features the following characteristics: (1) a large volume of videos, including over one million AI-generated and real videos collected; (2) a rich diversity of generated content and methodologies, covering a broad spectrum of video categories and generation techniques. We conducted extensive studies of the dataset and proposed two evaluation methods tailored for real-world-like scenarios to assess the detectors' performance: the cross-generator video classification task assesses the generalizability of trained detectors on generators; the degraded video classification task evaluates the robustness of detectors to handle videos that have degraded in quality during dissemination. Moreover, we introduced a plug-and-play module, named Detail Mamba (DeMamba), designed to enhance the detectors by identifying AI-generated videos through the analysis of inconsistencies in temporal and spatial dimensions. Our extensive experiments demonstrate DeMamba's superior generalizability and robustness on GenVideo compared to existing detectors. We believe that the GenVideo dataset and the DeMamba module will significantly advance the field of AI-generated video detection. Our code and dataset will be aviliable at \url{https://github.com/chenhaoxing/DeMamba}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
