# Domain-Generalized Face Anti-Spoofing with Unknown Attacks

arXiv: https://arxiv.org/abs/2310.11758 · HTML: https://mlsanity.com/paper/2310.11758/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [Single-Side Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2004.14043/) (2020)
- [CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations](https://mlsanity.com/paper/2007.12342/) (2020)
- [Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network](https://mlsanity.com/paper/1909.08848/) (2019)
- [Deep Tree Learning for Zero-shot Face Anti-Spoofing](https://mlsanity.com/paper/1904.02860/) (2019)
- [PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition](https://mlsanity.com/paper/2203.14325/) (2022)
- [Learning Meta Model for Zero- and Few-shot Face Anti-spoofing](https://mlsanity.com/paper/1904.12490/) (2019)
- [Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks](https://mlsanity.com/paper/2007.11457/) (2020)
- [Detection and Continual Learning of Novel Face Presentation Attacks](https://mlsanity.com/paper/2108.12081/) (2021)
- [Learning Meta Pattern for Face Anti-Spoofing](https://mlsanity.com/paper/2110.06753/) (2021)
- [Difficulty-Aware Simulator for Open Set Recognition](https://mlsanity.com/paper/2207.10024/) (2022)

## Built on by (most cited first, 4 of 4)

- [Test-Time Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2403.19334/) (2024)
- [Group-wise Scaling and Orthogonal Decomposition for Domain-Invariant Feature Extraction in Face Anti-Spoofing](https://mlsanity.com/paper/2507.04006/) (2025)
- [CPG-PAD: Concept-Informed Prompts Guided Presentation Attack Detection](https://mlsanity.com/paper/2607.01303/) (2026)
- [Introducing Nylon Face Mask Attacks: A Dataset for Evaluating Generalised Face Presentation Attack Detection](https://mlsanity.com/paper/2511.08114/) (2025)

## Abstract (arXiv)

Although face anti-spoofing (FAS) methods have achieved remarkable performance on specific domains or attack types, few studies have focused on the simultaneous presence of domain changes and unknown attacks, which is closer to real application scenarios. To handle domain-generalized unknown attacks, we introduce a new method, DGUA-FAS, which consists of a Transformer-based feature extractor and a synthetic unknown attack sample generator (SUASG). The SUASG network simulates unknown attack samples to assist the training of the feature extractor. Experimental results show that our method achieves superior performance on domain generalization FAS with known or unknown attacks.