Unsupervised Domain Adaptation-Based Cross-Type Deepfake Image Detection

In practical applications of social media and the Internet, deepfake face images involve a plethora of unlabeled samples. To effectively identify unlabeled deepfake images, the domain adaptation technique has gained significant attention. It applies the knowledge learned from labeled samples (source domain) to unlabeled samples (target domain) in a cross-domain manner. However, the existing domain adaptation-based deepfake detection methods primarily focus on intra-type cross-domain scenarios. In this study, we propose an unsupervised domain adaptation-based deepfake face image detection method for extra-type cross-domain scenarios. The core idea of our approach lies in the development of a domain adaptation model that consists of Domain Tag Adversarial (DTA) and Domain Feature Alignment (DFA) algorithms, called DTA-DFA, which empowers the proposed method with strong cross-domain capability. The DTA is utilized to weaken the specificity within each domain, while DFA aligns the distribution between the source and target domains. Compared with the existing deepfake detection methods, the experimental results demonstrate that the proposed method dramatically enhances the extra-type cross-domain detection performance. Moreover, the DTA-DFA model also exhibits a remarkable ability to perform cross-domain detection from large-shot labeled samples to few-shot labeled samples, further verifying its powerful cross-domain capability. Code is released at https://github.com/QinQin741/DTA-DFA-DA-model.