  Video DeepFakes are fake media created with Deep Learning (DL) that
manipulate a person's expression or identity. Most current DeepFake detection
methods analyze each frame independently, ignoring inconsistencies and
unnatural movements between frames. Some newer methods employ optical flow
models to capture this temporal aspect, but they are computationally expensive.
In contrast, we propose using the related but often ignored Motion Vectors
(MVs) and Information Masks (IMs) from the H.264 video codec, to detect
temporal inconsistencies in DeepFakes. Our experiments show that this approach
is effective and has minimal computational costs, compared with per-frame
RGB-only methods. This could lead to new, real-time temporally-aware DeepFake
detection methods for video calls and streaming.
