发表机构
Politecnico di Milano（米兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对特定人物的深度伪造检测，本文剖析3DMM系数并发现形状块与密集表面携带关键身份信息，据此构建仅用真实视频训练的MOTIF检测器，在多个数据集和操纵类型上超越现有最先进方法。
AI 中文摘要
针对特定个体（即特定人物，POI）的视频深度伪造是最具危害性的，并且由于公众人物有大量真实录像，可以基于该个体的真实镜头构建检测器。此类检测器通常通过3D形变模型（3DMM）描述主体，并整体采用其系数，因此从未衡量过该描述中哪部分携带信号。我们对此进行剖析，固定编码器、训练语料库和注册协议，仅改变编码器所观察的内容。系数组被证明在很大程度上是冗余的，因为仅形状块就能恢复完整向量几乎所有的准确性，而其时间演变贡献了真实但有限的效果。我们进一步表明，同一拟合返回的密集表面（这些检测器丢弃的部分）携带了系数所不具备的身份信息，并且它能在系数最薄弱的环节提供精确帮助。我们将最佳配置整合为MOTIF，一种仅基于真实视频训练的纯视觉检测器，不使用任何伪造视频或特定人物数据。在我们基准的每个数据集和每种操纵方式以及两种质量水平上，它都优于两种最先进的特定人物检测器。我们的实验代码将在该https网址发布。
英文摘要
Video deepfakes targeting a specific individual, the Person-of-Interest (POI), are the most harmful ones, and, since a public figure is abundantly recorded, a detector can be built from genuine footage of that individual. Such detectors commonly describe a subject through a 3D Morphable Model (3DMM) and adopt its coefficients as a whole, so which part of that description carries the signal has never been measured. We dissect it, holding the encoder, the training corpus and the enrollment protocol fixed and varying only what the encoder observes. The groups of coefficients prove largely redundant, since the shape block alone recovers almost all the accuracy of the full vector, and their temporal evolution contributes a real but bounded amount. We further show that the dense surface the same fit returns, which these detectors discard, carries identity information that the coefficients do not, and that it helps precisely where they are weakest. We assemble the best configuration into MOTIF, a visual-only detector trained on real videos only, with no manipulated video and no POI-specific data. It improves on both state-of-the-art POI detectors in every dataset and manipulation of our benchmark and at two quality levels. Our experimental code will be released at https://github.com/polimi-ispl/MOTIF.
Comments6 pages. Accepted at the 2026 IEEE International Workshop on Information Forensics and Security (WIFS)
