@inproceedings{b6da656df20a4efe9ff5d0b02a6c18a9,
title = "Deepfake Detection Using Robust Spatial and Temporal Features from Facial Landmarks",
abstract = "Most current deepfake detectors may suffer the decrease of detection accuracy under common video processing like compression. To deal with this issue, we proposed a new way of using biometric features for robust deepfake detection. Our biometric features are derived from a set of selected facial landmarks. We first presented a metric to select robust facial landmarks, and then constructed facial feature vectors with the selected landmarks. The spatial angles and the temporal rotation angles are introduced to facilitate the construction of the SVM feature vector. In essence, we use the spatial angles and temporal rotation angles to characterize the inherent consistency of facial landmarks at both frame level and video level. Experimental results have demonstrated that our detector has the best robustness compared with 6 current methods. It also has good scores in AUC (Area Under the Receiver Operating Characteristic Curve) and detection accuracy.",
keywords = "Deepfake detection, Facial landmarks, Robustness, Spatial features, Temporal features",
author = "Meng Li and Beibei Liu and Yongjian Hu and Liepiao Zhang and Shiqi Wang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 9th International Workshop on Biometrics and Forensics, IWBF 2021 ; Conference date: 06-05-2021 Through 07-05-2021",
year = "2021",
month = may,
day = "6",
doi = "10.1109/IWBF50991.2021.9465076",
language = "英语",
series = "Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021",
address = "美国",
}