Nowadays, the increasingly growing number of mobile and computing devices has
led to a demand for safer user authentication systems. Face anti-spoofing is a
measure towards this direction for bio-metric user authentication, and in
particular face recognition, that tries to prevent spoof attacks. The
state-of-the-art anti-spoofing techniques leverage the ability of deep neural
networks to learn discriminative features, based on cues from the training set
images or video samples, in an effort to detect spoof attacks. However, due to
the particular nature of the problem, i.e. large variability due to factors
like different backgrounds, lighting conditions, camera resolutions, spoof
materials, etc., these techniques typically fail to generalize to new samples.
In this paper, we explicitly tackle this problem and propose a
class-conditional domain discriminator module, that, coupled with a gradient
reversal layer, tries to generate live and spoof features that are
discriminative, but at the same time robust against the aforementioned
variability factors. Extensive experimental analysis shows the effectiveness of
the proposed method over existing image- and video-based anti-spoofing
techniques, both in terms of numerical improvement as well as when visualizing
the learned features.

本文提出了一种基于条件分类器领域鉴别器的活体检测方法，该方法结合了梯度反转层，以产生具有鉴别性而又对环境光照、分辨率等因素具有鲁棒性的真人和虚假特征，该方法在图像和视频方面均优于现有的反欺诈技术。