  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 primary innovation of this work lies in the
following aspects: (1) To achieve robust PAD, our system combines visual and
auditory modalities using three commonly 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, allowing the model to output
predictions from the vision, acoustic, and fusion heads, resulting in a more
flexible PAD. Extensive experiments have demonstrated the accuracy, robustness,
and flexibility of M3FAS under various challenging experimental settings. The
source code and dataset are available at: https://github.com/ChenqiKONG/M3FAS/
