One challenging aspect in face anti-spoofing (or presentation attack detection, PAD) refers to the difficulty of collecting enough and representative attack samples for an application-specific environment. In view of this, we tackle the problem of training a robust PAD model with limited data in an application-specific domain. We propose to leverage data from a richer and related domain to learn meaningful features through the concept of neural network distilling. We first train a deep neural network based on reasonably sufficient labeled data in an attempt to “teach” a neural network for the application-specific domain for which training samples are scarce. Subsequently, we form training sample pairs from both domains and formulate a novel optimization function by considering the cross-entropy loss, as well as maximum mean discrepancy of features and paired sample similarity embedding for network distillation. Thus, we expect to capture spoofing-specific information and train a discriminative deep neural network on the application-specific domain. Extensive experiments validate the effectiveness of the proposed scheme in face anti-spoofing setups.

中文翻译：

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使用深度神经网络蒸馏的人脸反欺骗

面部反欺骗（或演示攻击检测，PAD）的一个具有挑战性的方面是为特定于应用程序的环境收集足够且具有代表性的攻击样本的难度。鉴于此，我们解决了在特定于应用程序的领域中使用有限数据训练鲁棒 PAD 模型的问题。我们建议利用来自更丰富和相关领域的数据，通过神经网络提炼的概念来学习有意义的特征。我们首先基于合理充足的标记数据训练一个深度神经网络，试图为训练样本稀缺的特定应用领域“教”一个神经网络。随后，我们从两个域形成训练样本对，并通过考虑交叉熵损失来制定新的优化函数，以及用于网络蒸馏的特征的最大平均差异和配对样本相似性嵌入。因此，我们希望捕获特定于欺骗的信息并在特定于应用程序的域上训练一个有辨别力的深度神经网络。大量实验验证了所提出方案在人脸反欺骗设置中的有效性。