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arXiv · 2012.04199

Cost Sensitive Optimization of Deepfake Detector

Ivan Kukanov·Janne Karttunen·Hannu Sillanpää·Ville Hautamäki
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Abstract

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.

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Ivan Kukanov, Janne Karttunen, Hannu Sillanpää, Ville Hautamäki. 2020-12-08. Cost Sensitive Optimization of Deepfake Detector. https://arxiv.org/abs/2012.04199

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