GenVidBench

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 through such videos. However, the development of high-performance generative 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) Cross Source and Cross Generator: The cross-generation source mitigates 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. 2) State-of-the-Art Video Generators: The dataset includes videos from 8 state-of-the-art AI video generators, ensuring that it covers the latest advancements in the field of video generation. 3) Rich Semantics: The videos in GenVidBench are analyzed from multiple dimensions and classified into various semantic categories based on their content. This classification ensures that the dataset is not only large but also diverse, aiding in the development of more generalized and effective detection models. We conduct a comprehensive evaluation of different advanced video generators and present a challenging settings.

视频生成模型的快速迭代发展，使得AI生成视频与真实视频的鉴别难度与日俱增。这一问题凸显了研发高效AI生成视频检测器的迫切性，以遏制此类视频所携带的虚假信息的传播。然而，当前高性能生成式视频检测器的研发，仍受限于缺乏专为生成视频检测打造的大规模高质量数据集。为此，我们推出GenVidBench——一款兼具挑战性的AI生成视频检测基准数据集，其具备三大核心优势：1）跨源与跨生成器：跨源设计可削弱视频内容对检测任务的干扰；跨生成器设置则确保训练集与测试集的视频属性存在多样性，避免二者特征过于趋同。2）前沿视频生成模型覆盖：本数据集纳入了8款当前最先进的AI视频生成模型产出的视频，确保覆盖视频生成领域的最新技术进展。3）丰富语义维度：GenVidBench中的视频从多维度进行分析，并基于内容划分为多类语义类别。这种分类方式不仅保障了数据集的规模体量，更确保了其多样性，有助于研发泛化性更强、检测效果更优的检测器模型。我们针对多款先进视频生成模型开展了全面评估，并构建了极具挑战性的测试场景。