Exploiting Style Latent Flows for Generalizing Deepfake Detection Video
  Detection
Jongwook Choi Taehoon Kim Yonghyun Jeong Seungryul Baek Jongwon Choi
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.