Face-D(^2)CL: Multi-Domain Synergistic Representation with Dual Continual Learning for Facial DeepFake Detection
Facial forgery techniques are advancing rapidly, posing severe threats to public trust and information security while imposing higher demands on the continual adaptation of DeepFake detection models. Although continual learning enables models to adapt to emerging forgery methods, existing approaches still face two key bottlenecks. On the one hand, they lack sufficient feature representation capacity to handle increasingly diverse and complex forgery traces. On the other hand, continual adaptation to new forgery distributions leads to severe catastrophic forgetting of prior knowledge, which substantially degrades detection performance. To address these issues, we propose Face-D(^2)CL, a fr...
| Authors | Yushuo Zhang, Yu Cheng, Yongkang Hu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 April 2026 |
| Last updated at source | 3 August 2026 |
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