Training Deepfake Detection Model from Photos with Face Mask

Deepfake is becoming a major security threat nowadays. The development of robust deepfake detection algorithm is booming in recent years. However, the existing works require many face images for model training and the frontal face images collection would have privacy concerns. Therefore, this privacy issue inspires our research work that a deepfake detection model is trained from photos with face mask. We found that training a deepfake detection model from photos with face mask is rarely discussed in the literature. In order to resolve the challenges from face mask, we incorporate sub-regions (e.g., eye, nose, jaw) in a face during the training process. In addition to the extension by including facial sub-regions, we found that the associate training strategy is another important design factor for performance improvement. Our method improved the result significantly and the AUC of FaceForensics++ (FF++) test dataset evaluation is increased from 87.67% to 98.93%. As a result, we can develop a promising deepfake detection model even from the photos with face mask. We expected this work can be a stepping stone to inspire more research works with privacy considerations.