  Existing face forgery detection methods usually treat face forgery detection
as a binary classification problem and adopt deep convolution neural networks
to learn discriminative features. The ideal discriminative features should be
only related to the real/fake labels of facial images. However, we observe that
the features learned by vanilla classification networks are correlated to
unnecessary properties, such as forgery methods and facial identities. Such
phenomenon would limit forgery detection performance especially for the
generalization ability. Motivated by this, we propose a novel method which
utilizes adversarial learning to eliminate the negative effect of different
forgery methods and facial identities, which helps classification network to
learn intrinsic common discriminative features for face forgery detection. To
leverage data lacking ground truth label of facial identities, we design a
special identity discriminator based on similarity information derived from
off-the-shelf face recognition model. With the help of adversarial learning,
our face forgery detection model learns to extract common discriminative
features through eliminating the effect of forgery methods and facial
identities. Extensive experiments demonstrate the effectiveness of the proposed
method under both intra-dataset and cross-dataset evaluation settings.
