A Study on Effective Use of BPM Information in Deepfake Detection

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.