Learning Expressive And Generalizable Motion Features For Face Forgery
  Detection
Jingyi Zhang Peng Zhang Jingjing Wang Di Xie Shiliang Pu
Previous face forgery detection methods mainly focus on appearance features,
which may be easily attacked by sophisticated manipulation. Considering the
majority of current face manipulation methods generate fake faces based on a
single frame, which do not take frame consistency and coordination into
consideration, artifacts on frame sequences are more effective for face forgery
detection. However, current sequence-based face forgery detection methods use
general video classification networks directly, which discard the special and
discriminative motion information for face manipulation detection. To this end,
we propose an effective sequence-based forgery detection framework based on an
existing video classification method. To make the motion features more
expressive for manipulation detection, we propose an alternative motion
consistency block instead of the original motion features module. To make the
learned features more generalizable, we propose an auxiliary anomaly detection
block. With these two specially designed improvements, we make a general video
classification network achieve promising results on three popular face forgery
datasets.