  The rapid development of facial manipulation techniques has aroused public
concerns in recent years. Following the success of deep learning, existing
methods always formulate DeepFake video detection as a binary classification
problem and develop frame-based and video-based solutions. However, little
attention has been paid to capturing the spatial-temporal inconsistency in
forged videos. To address this issue, we term this task as a Spatial-Temporal
Inconsistency Learning (STIL) process and instantiate it into a novel STIL
block, which consists of a Spatial Inconsistency Module (SIM), a Temporal
Inconsistency Module (TIM), and an Information Supplement Module (ISM).
Specifically, we present a novel temporal modeling paradigm in TIM by
exploiting the temporal difference over adjacent frames along with both
horizontal and vertical directions. And the ISM simultaneously utilizes the
spatial information from SIM and temporal information from TIM to establish a
more comprehensive spatial-temporal representation. Moreover, our STIL block is
flexible and could be plugged into existing 2D CNNs. Extensive experiments and
visualizations are presented to demonstrate the effectiveness of our method
against the state-of-the-art competitors.
