  The increase in face manipulation models has led to a critical issue in
society - the synthesis of realistic visual media. With the emergence of new
forgery approaches at an unprecedented rate, existing forgery detection methods
suffer from significant performance drops when applied to unseen novel forgery
approaches. In this work, we address the few-shot forgery detection problem by
1) designing a comprehensive benchmark based on coverage analysis among various
forgery approaches, and 2) proposing Guided Adversarial Interpolation (GAI).
Our key insight is that there exist transferable distribution characteristics
between majority and minority forgery classes1. Specifically, we enhance the
discriminative ability against novel forgery approaches via adversarially
interpolating the forgery artifacts of the minority samples to the majority
samples under the guidance of a teacher network. Unlike the standard
re-balancing method which usually results in over-fitting to minority classes,
our method simultaneously takes account of the diversity of majority
information as well as the significance of minority information. Extensive
experiments demonstrate that our GAI achieves state-of-the-art performances on
the established few-shot forgery detection benchmark. Notably, our method is
also validated to be robust to choices of majority and minority forgery
approaches. The formal publication version is available in Pattern Recognition.
