Summary of Fsfm: a Generalizable Face Security Foundation Model Via Self-supervised Facial Representation Learning, by Gaojian Wang et al.
FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning
by Gaojian Wang, Feng Lin, Tong Wu, Zhenguang Liu, Zhongjie Ba, Kui Ren
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed FSFM framework leverages masked image modeling (MIM) and instance discrimination (ID) to learn robust and transferable facial representations. The CRFR-P masking strategy encourages models to capture intra-region consistency and inter-region coherency, while the ID network establishes local-to-global correspondence via self-distillation. This 3C learning objective enables encoding of both local features and global semantics in real faces. Pretrained models outperform task-specialized SOTA methods on cross-dataset deepfake detection, cross-domain face anti-spoofing, and unseen diffusion facial forgery detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to learn about faces using lots of pictures without labels. It wants to make a model that can recognize faces in different situations and tasks. The researchers try two things: hiding parts of the picture and comparing it with itself. They also add another step to help the model understand how different parts of the face work together. After learning, they test the model on lots of different tasks like detecting fake videos or spotting fake photos. It turns out that this new way of learning is better than some other methods and even beats the best methods for specific tasks. |
Keywords
» Artificial intelligence » Diffusion » Distillation » Semantics
