  Existing deepfake detection methods have reported promising in-distribution
results, by accessing published large-scale dataset. However, due to the
non-smooth synthesis method, the fake samples in this dataset may expose
obvious artifacts (e.g., stark visual contrast, non-smooth boundary), which
were heavily relied on by most of the frame-level detection methods above. As
these artifacts do not come up in real media forgeries, the above methods can
suffer from a large degradation when applied to fake images that close to
reality. To improve the robustness for high-realism fake data, we propose the
Invariant Texture Learning (InTeLe) framework, which only accesses the
published dataset with low visual quality. Our method is based on the prior
that the microscopic facial texture of the source face is inevitably violated
by the texture transferred from the target person, which can hence be regarded
as the invariant characterization shared among all fake images. To learn such
an invariance for deepfake detection, our InTeLe introduces an auto-encoder
framework with different decoders for pristine and fake images, which are
further appended with a shallow classifier in order to separate out the obvious
artifact-effect. Equipped with such a separation, the extracted embedding by
encoder can capture the texture violation in fake images, followed by the
classifier for the final pristine/fake prediction. As a theoretical guarantee,
we prove the identifiability of such an invariance texture violation, i.e., to
be precisely inferred from observational data. The effectiveness and utility of
our method are demonstrated by promising generalization ability from
low-quality images with obvious artifacts to fake images with high realism.
