  Although current face manipulation techniques achieve impressive performance
regarding quality and controllability, they are struggling to generate temporal
coherent face videos. In this work, we explore to take full advantage of the
temporal coherence for video face forgery detection. To achieve this, we
propose a novel end-to-end framework, which consists of two major stages. The
first stage is a fully temporal convolution network (FTCN). The key insight of
FTCN is to reduce the spatial convolution kernel size to 1, while maintaining
the temporal convolution kernel size unchanged. We surprisingly find this
special design can benefit the model for extracting the temporal features as
well as improve the generalization capability. The second stage is a Temporal
Transformer network, which aims to explore the long-term temporal coherence.
The proposed framework is general and flexible, which can be directly trained
from scratch without any pre-training models or external datasets. Extensive
experiments show that our framework outperforms existing methods and remains
effective when applied to detect new sorts of face forgery videos.
