Details

Autor(en) / Beteiligte

• Mao, Yuzhe
• You, Weike
• Zhou, Linna
• Lu, Zhigao

Titel

Fixing Domain Bias for Generalized Deepfake Detection

Ist Teil von

• 2023 IEEE International Conference on Multimedia and Expo (ICME), 2023, p.2225-2230

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Generalizing deepfake detection has posed a great challenge to digital media forensics, as inferior performance is obtained when training sets and testing sets are domain-mismatched. In this paper, we show that a CNN-based detection model can significantly improve performance by fixing domain bias. Specifically, we propose a novel Fixing Domain Bias network (FDBN). FDBN does not rely on manual features, but is based on three core designs. Firstly, a domain-invariant network based on randomly stylized normalization is devised to constrain the domain discrepancy in the feature space. Then, through adversarial learning, a generalizing representation in the stylized distribution is learned to enhance the shared feature bias among manipulation methods in the domain-specific network. Finally, to encourage equality of biases among different domains, we utilize the bias extrapolation penalty strategy by suppressing the expected bias on the extremely-performing domains. Extensive experiments demonstrate that our framework achieves effectiveness and generalization towards unseen face forgeries.