# Learning Generalized Spoof Cues for Face Anti-spoofing

arXiv: https://arxiv.org/abs/2005.03922 · HTML: https://mlsanity.com/paper/2005.03922/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 14 of 14)

- [PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition](https://mlsanity.com/paper/2203.14325/) (2022)
- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Unified Detection of Digital and Physical Face Attacks](https://mlsanity.com/paper/2104.02156/) (2021)
- [Disentangled Representation with Dual-stage Feature Learning for Face Anti-spoofing](https://mlsanity.com/paper/2110.09157/) (2021)
- [Cyclically Disentangled Feature Translation for Face Anti-spoofing](https://mlsanity.com/paper/2212.03651/) (2022)
- [Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues](https://mlsanity.com/paper/2404.08450/) (2024)
- [CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results](https://mlsanity.com/paper/2102.12642/) (2021)
- [Review of Face Presentation Attack Detection Competitions](https://mlsanity.com/paper/2112.11290/) (2021)
- [Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing](https://mlsanity.com/paper/2212.03943/) (2022)
- [Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels](https://mlsanity.com/paper/2204.10675/) (2022)
- [Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results](https://mlsanity.com/paper/2304.05753/) (2023)
- [A Dataset and Benchmark Towards Multi-Modal Face Anti-Spoofing Under Surveillance Scenarios](https://mlsanity.com/paper/2103.15409/) (2021)
- [Online Adaptive Personalization for Face Anti-spoofing](https://mlsanity.com/paper/2207.12272/) (2022)
- [Generalized Disguise Makeup Presentation Attack Detection Using an Attention-Guided Patch-Based Framework](https://mlsanity.com/paper/2604.26025/) (2026)

## Abstract (arXiv)

Many existing face anti-spoofing (FAS) methods focus on modeling the decision boundaries for some predefined spoof types. However, the diversity of the spoof samples including the unknown ones hinders the effective decision boundary modeling and leads to weak generalization capability. In this paper, we reformulate FAS in an anomaly detection perspective and propose a residual-learning framework to learn the discriminative live-spoof differences which are defined as the spoof cues. The proposed framework consists of a spoof cue generator and an auxiliary classifier. The generator minimizes the spoof cues of live samples while imposes no explicit constraint on those of spoof samples to generalize well to unseen attacks. In this way, anomaly detection is implicitly used to guide spoof cue generation, leading to discriminative feature learning. The auxiliary classifier serves as a spoof cue amplifier and makes the spoof cues more discriminative. We conduct extensive experiments and the experimental results show the proposed method consistently outperforms the state-of-the-art methods. The code will be publicly available at https://github.com/vis-var/lgsc-for-fas.