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• W4386952783 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." @default.
• W4386952783 created "2023-09-23" @default.
• W4386952783 creator A5040091210 @default.
• W4386952783 creator A5042401810 @default.
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• W4386952783 date "2023-08-01" @default.
• W4386952783 modified "2023-09-27" @default.
• W4386952783 title "Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation" @default.
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• W4386952783 doi "https://doi.org/10.1109/mipr59079.2023.00033" @default.
• W4386952783 hasPublicationYear "2023" @default.
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