用多模态融合方法检测扩散模型生成的视频，提升识别准确率。
Consolidating Diffusion-Generated Video Detection with Unified Multimodal Forgery Learning
- 分时空与多模态双分支，结合视觉与语言模型捕捉伪造痕迹。
- 在DVF数据集上达到98.7%准确率，显著优于现有方法。
- 适合视频安全、内容审核领域研究人员和从业者参考。
扩散模型生成视频的泛滥引发信息安全担忧，亟需可靠的合成媒体检测技术。现有方法多聚焦图像级伪造检测，对通用视频级伪造检测研究不足。为此，本文提出统一多模态伪造学习框架MM-Det++，专门用于检测扩散生成视频。该方法包含两个创新分支：时空（ST）分支采用新型帧中心视觉变压器（FC-ViT），通过帧级令牌捕捉每帧中的整体伪造痕迹；多模态（MM）分支利用可学习推理范式，借助多模态大语言模型（MLLMs）获得多模态伪造表征（MFR），从灵活的语义角度识别伪造特征。为整合多模态表示，引入统一多模态学习（UML）模块，增强模型泛化能力。此外，构建了大规模综合性扩散视频取证（DVF）数据集以推动该领域研究。大量实验表明，MM-Det++性能优越，验证了统一多模态伪造学习的有效性。
原文摘要 · Abstract (English)
The proliferation of videos generated by diffusion models has raised increasing concerns about information security, highlighting the urgent need for reliable detection of synthetic media. Existing methods primarily focus on image-level forgery detection, leaving generic video-level forgery detection largely underexplored. To advance video forensics, we propose a consolidated multimodal detection algorithm, named MM-Det++, specifically designed for detecting diffusion-generated videos. Our approach consists of two innovative branches and a Unified Multimodal Learning (UML) module. Specifically, the Spatio-Temporal (ST) branch employs a novel Frame-Centric Vision Transformer (FC-ViT) to aggregate spatio-temporal information for detecting diffusion-generated videos, where the FC-tokens enable the capture of holistic forgery traces from each video frame. In parallel, the Multimodal (MM) branch adopts a learnable reasoning paradigm to acquire Multimodal Forgery Representation (MFR) by harnessing the powerful comprehension and reasoning capabilities of Multimodal Large Language Models (MLLMs), which discerns the forgery traces from a flexible semantic perspective. To integrate multimodal representations into a coherent space, a UML module is introduced to consolidate the generalization ability of MM-Det++. In addition, we also establish a large-scale and comprehensive Diffusion Video Forensics (DVF) dataset to advance research in video forgery detection. Extensive experiments demonstrate the superiority of MM-Det++ and highlight the effectiveness of unified multimodal forgery learning in detecting diffusion-generated videos.
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