| Paper ID | B-3-3.2 |
| Paper Title |
COST SENSITIVE OPTIMIZATION OF DEEPFAKE DETECTOR |
| Authors |
Ivan Kukanov, A*STAR, Singapore; Janne Karttunen, Hannu Sillanpää, Ville Hautamäki, University of Eastern Finland, Finland |
| Session |
B-3-3: Recent Advances in Multimedia Security and Forensics |
| Time | Thursday, 10 December, 17:30 - 19:30 |
| Presentation Time: | Thursday, 10 December, 17:45 - 18:00 Check your Time Zone |
|
All times are in New Zealand Time (UTC +13) |
| Topic |
Multimedia Security and Forensics (MSF): |
| 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. |