Cost Sensitive Optimization of Deepfake Detector

Cost Sensitive Optimization of Deepfake Detector
Title:
Cost Sensitive Optimization of Deepfake Detector
Other Titles:
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)
DOI:
Publication Date:
07 December 2020
Citation:
Kukanov, J. Karttunen, H. Sillanpää and V. Hautamäki, "Cost Sensitive Optimization of Deepfake Detector," 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Auckland, New Zealand, 2020, pp. 1300-1303.
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.
License type:
Funding Info:
There is no specific funding for the research work done
Description:
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
978-988-14768-8-3
Files uploaded:

File Size Format Action
deepfakes-apsipa-2020.pdf 369.89 KB PDF Open