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，通过金融欺诈和隐私泄露等恶意应用日益引起公众关注。因此，保护人脸识别系统免受FPA攻击至关重要。尽管现有的基于学习的人脸防欺骗（FAS）模型能够实现出色的检测性能，但它们缺乏泛化能力，在未预见环境中会出现显著性能下降。许多方法试图通过引入辅助模态数据（如深度图和红外图）来克服这一局限。然而，这些方法存在局限性：（1）它们需要深度相机和红外相机等特定传感器进行数据采集，而商用移动设备几乎不具备此类传感器；（2）在任一模态缺失或质量低劣的实际场景中无法正常工作。本文设计了一种名为M3FAS的准确鲁棒多模态移动人脸防欺骗系统，以解决上述问题。该工作的创新点主要体现在以下方面：（1）为达成鲁棒的呈现攻击检测（PAD），本系统利用三种普及型传感器（摄像头、扬声器和麦克风）融合视觉与听觉模态；（2）我们设计了一种新颖的双分支神经网络，配备三种层级特征聚合模块以实现跨模态特征融合；（3）提出多头训练策略——模型从视觉头、听觉头和融合头输出三类预测结果，从而实现更灵活的PAD。大量实验表明，M3FAS在各种具有挑战性的实验设置下均展现出卓越的准确性、鲁棒性和灵活性。