Face presentation attacks (FPA), also known as face spoofing, have brought increasing concerns to the public through various malicious applications, such as financial fraud and privacy leakage. Therefore, safeguarding face recognition systems against FPA is of utmost importance. Although existing learning-based face anti-spoofing (FAS) models can achieve outstanding detection performance, they lack generalization capability and suffer significant performance drops in unforeseen environments. Many methodologies seek to use auxiliary modality data (e.g., depth and infrared maps) during the presentation attack detection (PAD) to address this limitation. However, these methods can be limited since (1) they require specific sensors such as depth and infrared cameras for data capture, which are rarely available on commodity mobile devices, and (2) they cannot work properly in practical scenarios when either modality is missing or of poor quality. In this paper, we devise an accurate and robust MultiModal Mobile Face Anti-Spoofing system named M3FAS to overcome the issues above. The innovation of this work mainly lies in the following aspects: (1) To achieve robust PAD, our system combines visual and auditory modalities using three pervasively available sensors: camera, speaker, and microphone; (2) We design a novel two-branch neural network with three hierarchical feature aggregation modules to perform cross-modal feature fusion; (3). We propose a multi-head training strategy. The model outputs three predictions from the vision, acoustic, and fusion heads, enabling a more flexible PAD. Extensive experiments have demonstrated the accuracy, robustness, and flexibility of M3FAS under various challenging experimental settings.

翻译：人脸呈现攻击（FPA），也称为人脸欺骗，已通过金融欺诈和隐私泄露等多种恶意应用引发公众日益关注。因此，保护人脸识别系统免受FPA攻击至关重要。尽管现有基于学习的人脸防欺骗（FAS）模型可实现出色的检测性能，但它们缺乏泛化能力，在未知环境下性能显著下降。许多方法试图利用辅助模态数据（如深度图和红外图）进行呈现攻击检测（PAD）以解决这一局限。然而，这些方法存在以下局限性：（1）需要深度摄像头和红外摄像头等特定传感器进行数据采集，而此类传感器在商用移动设备上罕见；（2）当任一模态缺失或质量较差时，这些方法无法在实际场景中正常工作。本文设计了一种名为M3FAS的高精度鲁棒多模态移动人脸防欺骗系统，以克服上述问题。本工作的创新主要在于以下方面：（1）为实现鲁棒的PAD，本系统利用摄像头、扬声器和麦克风三种广泛可用的传感器结合视觉和听觉模态；（2）设计了一种新颖的双分支神经网络，包含三个层次化特征聚合模块以执行跨模态特征融合；（3）提出了一种多头训练策略。模型从视觉头、声学头和融合头输出三个预测，实现更灵活的PAD。大量实验证明了M3FAS在各种挑战性实验设置下的准确性、鲁棒性和灵活性。