A training strategy (OWG-DS) combines domain distance optimization, similarity-based class separation, and adversarial domain classification to adapt deepfake detectors to new forgery types with limited labeled and abundant unlabeled data.
Improving the efficiency and robustness of deepfakes detection through precise geometric features
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Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation
A training strategy (OWG-DS) combines domain distance optimization, similarity-based class separation, and adversarial domain classification to adapt deepfake detectors to new forgery types with limited labeled and abundant unlabeled data.