| Title | A Study on Effective Use of BPM Information in Deepfake Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Lee, Soo-Hyun, Yun, Gyung-Eun, Lim, Min Young, Lee, Youn Kyu |
| Conference Name | 2021 International Conference on Information and Communication Technology Convergence (ICTC) |
| Keywords | Biology, composability, DeepFake, deepfake detection, faces, feature extraction, information and communication technology, Information integrity, Metrics, privacy, pubcrawl, R-PPG, resilience, Resiliency, security, signal processing security, Videos |
| Abstract | Recent developments in deepfake technology are increasing new security threats. To solve these issues, various detection methods have been proposed including the methods utilizing biological signals captured by R-PPG. However, existing methods have limitations in terms of detection accuracy and generalized performance. In this paper, we present our approach for R-PPG-based BPM (Beats Per Minute) analysis for effective deepfake detection. With the selected deepfake datasets, we performed (a) comparison and analysis of conditions for BPM processing, and (b) BPM extraction by dividing the face into 16 regions and comparison of BPM in each region. The results showed that our proposed BPM-related properties are effective in deepfake detection. |
| DOI | 10.1109/ICTC52510.2021.9621186 |
| Citation Key | lee_study_2021 |
A Study on Effective Use of BPM Information in Deepfake Detection