DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant
  Forgery Clues
Kun Pan Yin Yifang Yao Wei Feng Lin Zhongjie Ba Zhenguang Liu ZhiBo Wang Lorenzo Cavallaro Kui Ren
The malicious use and widespread dissemination of deepfake pose a significant
crisis of trust. Current deepfake detection models can generally recognize
forgery images by training on a large dataset. However, the accuracy of
detection models degrades significantly on images generated by new deepfake
methods due to the difference in data distribution. To tackle this issue, we
present a novel incremental learning framework that improves the generalization
of deepfake detection models by continual learning from a small number of new
samples. To cope with different data distributions, we propose to learn a
domain-invariant representation based on supervised contrastive learning,
preventing overfit to the insufficient new data. To mitigate catastrophic
forgetting, we regularize our model in both feature-level and label-level based
on a multi-perspective knowledge distillation approach. Finally, we propose to
select both central and hard representative samples to update the replay set,
which is beneficial for both domain-invariant representation learning and
rehearsal-based knowledge preserving. We conduct extensive experiments on four
benchmark datasets, obtaining the new state-of-the-art average forgetting rate
of 7.01 and average accuracy of 85.49 on FF++, DFDC-P, DFD, and CDF2. Our code
is released at https://github.com/DeepFakeIL/DFIL.