  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 collectively 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 additive noise, and their corresponding
metadata).
