  In this paper, we present novel synthetic training data called self-blended
images (SBIs) to detect deepfakes. SBIs are generated by blending pseudo source
and target images from single pristine images, reproducing common forgery
artifacts (e.g., blending boundaries and statistical inconsistencies between
source and target images). The key idea behind SBIs is that more general and
hardly recognizable fake samples encourage classifiers to learn generic and
robust representations without overfitting to manipulation-specific artifacts.
We compare our approach with state-of-the-art methods on FF++, CDF, DFD, DFDC,
DFDCP, and FFIW datasets by following the standard cross-dataset and
cross-manipulation protocols. Extensive experiments show that our method
improves the model generalization to unknown manipulations and scenes. In
particular, on DFDC and DFDCP where existing methods suffer from the domain gap
between the training and test sets, our approach outperforms the baseline by
4.90% and 11.78% points in the cross-dataset evaluation, respectively.
