  Most of previous deepfake detection researches bent their efforts to describe
and discriminate artifacts in human perceptible ways, which leave a bias in the
learned networks of ignoring some critical invariance features intra-class and
underperforming the robustness of internet interference. Essentially, the
target of deepfake detection problem is to represent natural faces and fake
faces at the representation space discriminatively, and it reminds us whether
we could optimize the feature extraction procedure at the representation space
through constraining intra-class consistence and inter-class inconsistence to
bring the intra-class representations close and push the inter-class
representations apart? Therefore, inspired by contrastive representation
learning, we tackle the deepfake detection problem through learning the
invariant representations of both classes and propose a novel real-centric
consistency learning method. We constraint the representation from both the
sample level and the feature level. At the sample level, we take the procedure
of deepfake synthesis into consideration and propose a novel forgery
semantical-based pairing strategy to mine latent generation-related features.
At the feature level, based on the centers of natural faces at the
representation space, we design a hard positive mining and synthesizing method
to simulate the potential marginal features. Besides, a hard negative fusion
method is designed to improve the discrimination of negative marginal features
with the help of supervised contrastive margin loss we developed. The
effectiveness and robustness of the proposed method has been demonstrated
through extensive experiments.
