This paper presents a new approach for the detection of fake videos, based on the analysis of style latent vectors and their abnormal behavior in temporal changes in the generated videos. We discovered that the generated facial videos suffer from the temporal distinctiveness in the temporal changes of style latent vectors, which are inevitable during the generation of temporally stable videos with various facial expressions and geometric transformations. Our framework utilizes the StyleGRU module, trained by contrastive learning, to represent the dynamic properties of style latent vectors. Additionally, we introduce a style attention module that integrates StyleGRU-generated features with content-based features, enabling the detection of visual and temporal artifacts. We demonstrate our approach across various benchmark scenarios in deepfake detection, showing its superiority in cross-dataset and cross-manipulation scenarios. Through further analysis, we also validate the importance of using temporal changes of style latent vectors to improve the generality of deepfake video detection.

翻译：本文提出了一种基于风格潜在向量及其在生成视频时间变化中的异常行为分析的新型伪造视频检测方法。我们发现，生成的面部视频中，风格潜在向量的时间变化存在时序特异性——这种特性在生成具有多样化面部表情与几何变换的时序稳定视频时难以避免。我们的框架采用经对比学习训练的StyleGRU模块，表征风格潜在向量的动态属性。此外，我们引入风格注意力模块，将StyleGRU生成的特征与基于内容的特征进行融合，从而实现对视觉伪影与时序伪影的检测。我们在深度伪造检测的多个基准场景中验证了该方法，展示了其在跨数据集与跨篡改场景中的优越性。通过进一步分析，我们还验证了利用风格潜在向量的时间变化对提升深度伪造视频检测泛化性的重要意义。