# Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection

arXiv: https://arxiv.org/abs/1902.10311 · HTML: https://mlsanity.com/paper/1902.10311/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 6 of 6)

- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2103.00853/) (2021)
- [Deep Learning meets Liveness Detection: Recent Advancements and Challenges](https://mlsanity.com/paper/2112.14796/) (2021)
- [Flow-Attention-based Spatio-Temporal Aggregation Network for 3D Mask Detection](https://mlsanity.com/paper/2310.16569/) (2023)
- [Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing](https://mlsanity.com/paper/2208.04076/) (2022)
- [Enhancing Mobile Face Anti-Spoofing: A Robust Framework for Diverse Attack Types under Screen Flash](https://mlsanity.com/paper/2308.15346/) (2023)

## Abstract (arXiv)

In this paper, we propose a light reflection based face anti-spoofing method named Aurora Guard (AG), which is fast, simple yet effective that has already been deployed in real-world systems serving for millions of users. Specifically, our method first extracts the normal cues via light reflection analysis, and then uses an end-to-end trainable multi-task Convolutional Neural Network (CNN) to not only recover subjects' depth maps to assist liveness classification, but also provide the light CAPTCHA checking mechanism in the regression branch to further improve the system reliability. Moreover, we further collect a large-scale dataset containing 12,000 live and spoofing samples, which covers abundant imaging qualities and Presentation Attack Instruments (PAI). Extensive experiments on both public and our datasets demonstrate the superiority of our proposed method over the state of the arts.