Recent advancements in AI-based multimedia generation have enabled the creation of hyper-realistic images and videos, raising concerns about their potential use in spreading misinformation. The widespread accessibility of generative techniques, which allow for the production of fake multimedia from prompts or existing media, along with their continuous refinement, underscores the urgent need for highly accurate and generalizable AI-generated media detection methods, underlined also by new regulations like the European Digital AI Act. In this paper, we draw inspiration from Vision Transformer (ViT)-based fake image detection and extend this idea to video. We propose an {original} %innovative framework that effectively integrates ViT embeddings over time to enhance detection performance. Our method shows promising accuracy, generalization, and few-shot learning capabilities across a new, large and diverse dataset of videos generated using five open source generative techniques from the state-of-the-art, as well as a separate dataset containing videos produced by proprietary generative methods.

本研究解决了越来越真实的假视频快速生成可能带来的虚假信息传播问题。我们提出了一种创新框架，利用视觉变换器的嵌入在时间上进行整合，从而提高了检测效果。研究表明，该方法在新、大规模和多样化的视频数据集上在准确性和迁移学习能力方面表现优异，展示了其广泛的应用潜力。

通过视觉变换器推进假视频检测