| Title | Eyebrow Recognition for Identifying Deepfake Videos |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Nguyen, H. M., Derakhshani, R. |
| Conference Name | 2020 International Conference of the Biometrics Special Interest Group (BIOSIG) |
| Keywords | anti-deepfake approaches, Biological system modeling, biometric eyebrow matching, biometrics (access control), Celeb-DF dataset, convolution, DeepFake, deepfake advances, deepfake detection, deepfake imagery, deepfake video identification, eyebrow recognition, Eyebrows, face recognition, faces, feature extraction, highest quality deepfake dataset, Human Behavior, human factors, image anomalies, image matching, manipulated face detection, Metrics, online content, pubcrawl, resilience, Resiliency, Scalability, Training, Videos, visible artifacts, visual artifacts, visualization |
| 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. |
| Citation Key | nguyen_eyebrow_2020 |
Eyebrow Recognition for Identifying Deepfake Videos