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arXiv · 2303.00917

Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation

Chenqi Kong·Haoliang Li·Shiqi Wang
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Abstract

Nowadays, forgery faces pose pressing security concerns over fake news, fraud, impersonation, etc. Despite the demonstrated success in intra-domain face forgery detection, existing detection methods lack generalization capability and tend to suffer from dramatic performance drops when deployed to unforeseen domains. To mitigate this issue, this paper designs a more general fake face detection model based on the vision transformer(ViT) architecture. In the training phase, the pretrained ViT weights are freezed, and only the Low-Rank Adaptation(LoRA) modules are updated. Additionally, the Single Center Loss(SCL) is applied to supervise the training process, further improving the generalization capability of the model. The proposed method achieves state-of-the-arts detection performances in both cross-manipulation and cross-dataset evaluations.

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Chenqi Kong, Haoliang Li, Shiqi Wang. 2023-03-02. Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation. https://arxiv.org/abs/2303.00917

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