  With the rapid progress of deepfake techniques in recent years, facial video
forgery can generate highly deceptive video contents and bring severe security
threats. And detection of such forgery videos is much more urgent and
challenging. Most existing detection methods treat the problem as a vanilla
binary classification problem. In this paper, the problem is treated as a
special fine-grained classification problem since the differences between fake
and real faces are very subtle. It is observed that most existing face forgery
methods left some common artifacts in the spatial domain and time domain,
including generative defects in the spatial domain and inter-frame
inconsistencies in the time domain. And a spatial-temporal model is proposed
which has two components for capturing spatial and temporal forgery traces in
global perspective respectively. The two components are designed using a novel
long distance attention mechanism. The one component of the spatial domain is
used to capture artifacts in a single frame, and the other component of the
time domain is used to capture artifacts in consecutive frames. They generate
attention maps in the form of patches. The attention method has a broader
vision which contributes to better assembling global information and extracting
local statistic information. Finally, the attention maps are used to guide the
network to focus on pivotal parts of the face, just like other fine-grained
classification methods. The experimental results on different public datasets
demonstrate that the proposed method achieves the state-of-the-art performance,
and the proposed long distance attention method can effectively capture pivotal
parts for face forgery.
