GenVidBench：一个600万的AI生成视频检测基准数据集
GenVidBench: A 6-Million Benchmark for AI-Generated Video Detection
AI总结：
GenVidBench是一个包含600万视频的AI生成视频检测基准数据集，通过大规模、跨源和跨生成器的视频收集，提升AI生成视频检测模型的泛化能力。
摘要：
视频生成模型的快速发展使得区分AI生成视频与真实视频变得越来越困难。这一问题凸显了开发有效AI生成视频检测器的紧迫性，以防止通过此类视频传播虚假信息。然而，高性能AI生成视频检测器的发展目前受到缺乏大规模高质量数据集的阻碍。为此，我们介绍了GenVidBench，一个具有几个关键优势的挑战性AI生成视频检测数据集：1）大规模视频收集：数据集包含678万部视频，目前是最大的AI生成视频检测数据集。2）跨源和跨生成器：跨源生成减少了视频内容对检测的干扰。跨生成器确保训练集和测试集之间的视频属性多样性，防止它们过于相似。3）最先进的视频生成器：数据集包含11种最先进的AI视频生成器生成的视频，确保其涵盖视频生成领域的最新进展。这些生成器确保数据集不仅在规模上庞大，而且具有多样性，有助于开发通用且有效的检测模型。此外，我们还展示了使用先进视频分类模型的广泛实验结果。借助GenVidBench，研究人员可以高效地开发和评估AI生成视频检测模型。数据集和代码可在https://genvidbench.github.io获取。
英文摘要：
The rapid advancement of video generation models has made it increasingly challenging to distinguish AI-generated videos from real ones. This issue underscores the urgent need for effective AI-generated video detectors to prevent the dissemination of false information via such videos. However, the development of high-performance AI-generated video detectors is currently impeded by the lack of large-scale, high-quality datasets specifically designed for generative video detection. To this end, we introduce GenVidBench, a challenging AI-generated video detection dataset with several key advantages: 1) Large-scale video collection: The dataset contains 6.78 million videos and is currently the largest dataset for AI-generated video detection. 2) Cross-Source and Cross-Generator: The cross-source generation reduces the interference of video content on the detection. The cross-generator ensures diversity in video attributes between the training and test sets, preventing them from being overly similar. 3) State-of-the-Art Video Generators: The dataset includes videos from 11 state-of-the-art AI video generators, ensuring that it covers the latest advancements in the field of video generation. These generators ensure that the datasets are not only large in scale but also diverse, aiding in the development of generalized and effective detection models. Additionally, we present extensive experimental results with advanced video classification models. With GenVidBench, researchers can efficiently develop and evaluate AI-generated video detection models.. Datasets and code are available at https://genvidbench.github.io.
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。
