  A variety of effective face-swap and face-reenactment methods have been
publicized in recent years, democratizing the face synthesis technology to a
great extent. Videos generated as such have come to be called deepfakes with a
negative connotation, for various social problems they have caused. Facing the
emerging threat of deepfakes, we have built the Korean DeepFake Detection
Dataset (KoDF), a large-scale collection of synthesized and real videos focused
on Korean subjects. In this paper, we provide a detailed description of methods
used to construct the dataset, experimentally show the discrepancy between the
distributions of KoDF and existing deepfake detection datasets, and underline
the importance of using multiple datasets for real-world generalization. KoDF
is publicly available at https://moneybrain-research.github.io/kodf in its
entirety (i.e. real clips, synthesized clips, clips with adversarial attack,
and metadata).
