Unified Face Attack Detection via Fine-Grained Semantic Guidance
The growing applications of facial recognition systems are accompanied by increasingly diverse security threats. Existing datasets lack detailed textual descriptions of forgery cues, leading most prior methods to treat face attack detection primarily as a visual recognition task. In this paper, building upon the large-scale MS-UFAD dataset which contains over 8 million attack images, we enrich each image with a fine-grained textual description of forgery cues. Furthermore, we propose a Dual Alignment Forgery Network(DAF-Net) to better leverage these textual information. Extensive experiments demonstrate that our approach extracts more generalizable and semantically meaningful forgery repr...
| Authors | Ning Jiang, Shijie Yu, Dingheng Zeng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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