Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection
This addresses security vulnerabilities in authentication systems for millions of users, though it appears incremental as it builds on existing light-based and CNN approaches.
The paper tackles face anti-spoofing by proposing Aurora Guard, a method using light reflection analysis and a multi-task CNN to detect spoofing attacks, achieving state-of-the-art results on public and a new dataset of 12,000 samples.
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.