Large numbers of synthesized videos from diffusion models pose threats to information security and authenticity, leading to an increasing demand for generated content detection. However, existing video-level detection algorithms primarily focus on detecting facial forgeries and often fail to identify diffusion-generated content with a diverse range of semantics. To advance the field of video forensics, we propose an innovative algorithm named Multi-Modal Detection(MM-Det) for detecting diffusion-generated videos. MM-Det utilizes the profound perceptual and comprehensive abilities of Large Multi-modal Models (LMMs) by generating a Multi-Modal Forgery Representation (MMFR) from LMM's multi-modal space, enhancing its ability to detect unseen forgery content. Besides, MM-Det leverages an In-and-Across Frame Attention (IAFA) mechanism for feature augmentation in the spatio-temporal domain. A dynamic fusion strategy helps refine forgery representations for the fusion. Moreover, we construct a comprehensive diffusion video dataset, called Diffusion Video Forensics (DVF), across a wide range of forgery videos. MM-Det achieves state-of-the-art performance in DVF, demonstrating the effectiveness of our algorithm. Both source code and DVF are available at https://github.com/SparkleXFantasy/MM-Det.

翻译：扩散模型生成的大量合成视频对信息安全和真实性构成威胁，导致对生成内容检测的需求日益增长。然而，现有的视频级检测算法主要集中于检测面部伪造，往往无法识别具有多样化语义的扩散生成内容。为推进视频取证领域发展，我们提出了一种名为多模态检测（MM-Det）的创新算法，用于检测扩散生成的视频。MM-Det通过从大型多模态模型（LMMs）的多模态空间中生成多模态伪造表示（MMFR），利用LMMs强大的感知与综合能力，从而增强其对未见伪造内容的检测能力。此外，MM-Det采用帧内与跨帧注意力（IAFA）机制在时空域进行特征增强。动态融合策略有助于优化伪造表示以进行融合。此外，我们构建了一个涵盖广泛伪造视频类型的综合性扩散视频数据集，称为扩散视频取证（DVF）。MM-Det在DVF上实现了最先进的性能，证明了我们算法的有效性。源代码与DVF数据集均公开于https://github.com/SparkleXFantasy/MM-Det。