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.
@article{arxiv.2012.04199,
title = {Cost Sensitive Optimization of Deepfake Detector},
author = {Ivan Kukanov and Janne Karttunen and Hannu Sillanpää and Ville Hautamäki},
journal= {arXiv preprint arXiv:2012.04199},
year = {2020}
}