DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues

• URL: http://arxiv.org/abs/2309.09526v1
• Date: Mon, 18 Sep 2023 07:02:26 GMT
• Title: DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues
• Authors: Kun Pan, Yin Yifang, Yao Wei, Feng Lin, Zhongjie Ba, Zhenguang Liu, ZhiBo Wang, Lorenzo Cavallaro and Kui Ren
• Abstract summary: Current deepfake detection models can generally recognize forgery images by training on a large dataset. The accuracy of detection models degrades significantly on images generated by new deepfake methods due to the difference in data distribution. We present a novel incremental learning framework that improves the generalization of deepfake detection models.
• Score: 32.045504965382015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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