Abstract
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{this https URL}.
Abstract (translated)
近年来，视频生成技术发展迅速。考虑到社交媒体上视频内容的流行，这些模型加剧了对虚假信息传播的担忧。因此，人们越来越需要能够区分虚假AI生成的视频并减轻虚假信息可能带来的潜在危害的检测器。然而，从最先进的视频生成器那里获取的大规模数据集的缺乏构成了发展这种检测器的障碍。为解决这个空白，我们引入了第一个AI生成的视频检测数据集GenVideo。其特征如下：（1）大量的视频，包括超过100万AI生成和真实视频；（2）丰富多样的生成内容和方法论，涵盖了广泛的视频类别和生成技术。我们对该数据集进行了广泛的研究，并提出了两个针对现实场景的评估方法，以评估检测器的性能：跨生成器视频分类任务评估训练后的检测器的泛化能力；降低视频分类任务评估检测器对质量下降的视频的鲁棒性。此外，我们还引入了一个名为Detail Mamba（DeMamba）的插件模块，通过分析时空维度的不一致性来识别AI生成的视频。我们的大量实验证明，DeMamba在GenVideo数据集上比现有检测器具有更卓越的泛化能力和鲁棒性。我们相信，GenVideo数据集和DeMamba模块将对AI生成视频检测领域产生重大影响。我们的代码和数据集将在此处可用：https://this URL。
URL
https://arxiv.org/abs/2405.19707
