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arXiv Computer Vision By Enrui Yang, Baoyuan Wu, Yuezun Li

Generalizable Face Forgery Detection via Separable Prompt Learning

Read the original on arXiv Computer Vision →

The paper introduces Separable Prompt Learning (SePL), a method that enhances face forgery detection by leveraging CLIP’s textual encoder through two separate learnable prompts. SePL incorporates a cross-modality alignment strategy and specific objectives to distill forgery knowledge from CLIP. Experiments show that SePL outperforms existing approaches in cross-dataset and cross-method evaluations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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