  As realistic facial manipulation technologies have achieved remarkable
progress, social concerns about potential malicious abuse of these technologies
bring out an emerging research topic of face forgery detection. However, it is
extremely challenging since recent advances are able to forge faces beyond the
perception ability of human eyes, especially in compressed images and videos.
We find that mining forgery patterns with the awareness of frequency could be a
cure, as frequency provides a complementary viewpoint where either subtle
forgery artifacts or compression errors could be well described. To introduce
frequency into the face forgery detection, we propose a novel Frequency in Face
Forgery Network (F3-Net), taking advantages of two different but complementary
frequency-aware clues, 1) frequency-aware decomposed image components, and 2)
local frequency statistics, to deeply mine the forgery patterns via our
two-stream collaborative learning framework. We apply DCT as the applied
frequency-domain transformation. Through comprehensive studies, we show that
the proposed F3-Net significantly outperforms competing state-of-the-art
methods on all compression qualities in the challenging FaceForensics++
dataset, especially wins a big lead upon low-quality media.
