  Since the invention of cinema, the manipulated videos have existed. But
generating manipulated videos that can fool the viewer has been a
time-consuming endeavor. With the dramatic improvements in the deep generative
modeling, generating believable looking fake videos has become a reality. In
the present work, we concentrate on the so-called deepfake videos, where the
source face is swapped with the targets. We argue that deepfake detection task
should be viewed as a screening task, where the user, such as the video
streaming platform, will screen a large number of videos daily. It is clear
then that only a small fraction of the uploaded videos are deepfakes, so the
detection performance needs to be measured in a cost-sensitive way. Preferably,
the model parameters also need to be estimated in the same way. This is
precisely what we propose here.
