Name
Abstract | ||
|---|---|---|
Deepfake imagery that contains altered faces has become a threat to online content. Current anti-deepfake approaches usually do so by detecting image anomalies, such as visible artifacts or inconsistencies. However, with deepfake advances, these visual artifacts are becoming harder to detect. In this paper, we show that one can use biometric eyebrow matching as a tool to detect manipulated faces. Our method could provide an 0.88 AUC and 20.7% EER for deepfake detection when applied to the highest quality deepfake dataset, Celeb-DF. |
Year | Venue | DocType |
|---|---|---|
2020 | 2020 International Conference of the Biometrics Special Interest Group (BIOSIG) | Conference |
ISBN | Citations | PageRank |
978-3-88579-700-5 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hoang (Mark) Nguyen | 1 | 0 | 0.68 |
| Reza Derakhshani | 2 | 166 | 21.08 |