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:面部展示攻击(FAS)也被称为面部显露,通过金融欺诈和隐私泄漏等各种恶意应用,使公众日益关切脸部展示攻击(FPA),但通过金融欺诈和隐私泄漏等各种恶意应用,这些方法使公众日益关切。因此,保护面部识别系统对面部识别系统至关重要。尽管现有的基于学习的面部反脸部(FAS)模型可以取得杰出的检测性能,但它们缺乏一般化能力,并在意外环境中出现显著的性能下降。许多方法都试图在演示攻击探测(PAD)期间使用辅助模式数据(如深度和红外线地图)来解决上述限制。但是,这些方法可能有限,因为(1)这些方法需要特定的传感器,如深度和红外摄像机来采集数据,而这种传感器很少在商品移动设备上提供。(2)在缺少模式或质量差的情况下,这些模型无法在实际情况下正常工作。在本文件中,我们设计了一个准确和稳健的多式移动式移动式移动式移动式数据系统以克服上述问题。