提出无需依赖特定嵌入的Swin模型，高效检测AI生成视频。
EA-Swin: An Embedding-Agnostic Swin Transformer for AI-Generated Video Detection
- 通过分块窗口注意力直接建模预训练嵌入的时空关系。
- 在13万视频数据集上达到0.97-0.99准确率，领先现有方法5-20%。
- 适用于多种生成器，尤其适合跨分布泛化场景。
近年来，Sora、Veo等基础视频生成模型产生了高度逼真的合成视频，暴露出现有检测方法在依赖浅层嵌入轨迹、基于图像适应或计算量巨大的多模态大模型方面的局限性。我们提出EA-Swin，一种嵌入无关的Swin Transformer，通过分块窗口注意力设计，直接在预训练视频嵌入上建模时空依赖，兼容通用ViT风格的分块编码器。此外，我们构建了EA-Video数据集，包含13万条视频，融合新采集样本与精选公开数据集，覆盖多样商业与开源生成器，并设置未见生成器划分以实现严格的跨分布评估。大量实验表明，EA-Swin在主要生成器上达到0.97-0.99准确率，优于以往最先进方法（通常为0.8-0.9），提升5-20%，同时保持对未见分布的强大泛化能力，为现代AI生成视频检测提供了可扩展且鲁棒的解决方案。
原文摘要 · Abstract (English)
Recent advances in foundation video generators such as Sora2, Veo3, and other commercial systems have produced highly realistic synthetic videos, exposing the limitations of existing detection methods that rely on shallow embedding trajectories, image-based adaptation, or computationally heavy MLLMs. We propose EA-Swin, an Embedding-Agnostic Swin Transformer that models spatiotemporal dependencies directly on pretrained video embeddings via a factorized windowed attention design, making it compatible with generic ViT-style patch-based encoders. Moreover, we construct the EA-Video dataset, a benchmark dataset comprising 130K videos that integrates newly collected samples with curated existing datasets, covering diverse commercial and open-source generators and including unseen-generator splits for rigorous cross-distribution evaluation. Extensive experiments show that EA-Swin achieves 0.97-0.99 accuracy across major generators, outperforming prior SoTA methods (typically 0.8-0.9) by a margin of 5-20\%, while maintaining strong generalization to unseen distributions, establishing a scalable and robust solution for modern AI-generated video detection.
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