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
https://github.com/chenhaoxing/DeMamba.

给予广泛传播的虚假信息的忧虑，我们引入了第一个人工智能生成视频检测数据集 GenVideo，其中包括超过一百万个人工智能生成的视频和真实视频，以及针对现实场景的评估方法和增强检测器的模块 DeMamba，这将显著推进人工智能生成视频检测领域。