Facial biometrics are an essential components of smartphones to ensure
reliable and trustworthy authentication. However, face biometric systems are
vulnerable to Presentation Attacks (PAs), and the availability of more
sophisticated presentation attack instruments such as 3D silicone face masks
will allow attackers to deceive face recognition systems easily. In this work,
we propose a novel Presentation Attack Detection (PAD) algorithm based on 3D
point clouds captured using the frontal camera of a smartphone to detect
presentation attacks. The proposed PAD algorithm, VoxAtnNet, processes 3D point
clouds to obtain voxelization to preserve the spatial structure. Then, the
voxelized 3D samples were trained using the novel convolutional attention
network to detect PAs on the smartphone. Extensive experiments were carried out
on the newly constructed 3D face point cloud dataset comprising bona fide and
two different 3D PAIs (3D silicone face mask and wrap photo mask), resulting in
3480 samples. The performance of the proposed method was compared with existing
methods to benchmark the detection performance using three different evaluation
protocols. The experimental results demonstrate the improved performance of the
proposed method in detecting both known and unknown face presentation attacks.

提出了一种基于 3D 点云的新型演示攻击检测算法 VoxAtnNet，用于侦测手机上的面部演示攻击。通过处理 3D 点云获取体素化的方式，以保留空间结构，然后使用新型卷积注意力网络对体素化的 3D 样本进行训练，实验结果表明该方法在检测已知和未知面部演示攻击的性能方面有所提升。

VoxAtnNet: 面部展示攻击的通用三维点云卷积神经网络

VoxAtnNet: A 3D Point Clouds Convolutional Neural Network for  Generalizable Face Presentation Attack Detection

Facial biometrics are widely deployed in smartphone-based applications
because of their usability and increased verification accuracy in unconstrained
scenarios. The evolving applications of smartphone-based facial recognition
have also increased Presentation Attacks (PAs), where an attacker can present a
Presentation Attack Instrument (PAI) to maliciously gain access to the
application. Because the materials used to generate PAI are not deterministic,
the detection of unknown presentation attacks is challenging. In this paper, we
present an acoustic echo-based face Presentation Attack Detection (PAD) on a
smartphone in which the PAs are detected based on the reflection profiles of
the transmitted signal. We propose a novel transmission signal based on the
wide pulse that allows us to model the background noise before transmitting the
signal and increase the Signal-to-Noise Ratio (SNR). The received signal
reflections were processed to remove background noise and accurately represent
reflection characteristics. The reflection profiles of the bona fide and PAs
are different owing to the different reflection characteristics of the human
skin and artefact materials. Extensive experiments are presented using the
newly collected Acoustic Sound Echo Dataset (ASED) with 4807 samples captured
from bona fide and four different types of PAIs, including print (two types),
display, and silicone face-mask attacks. The obtained results indicate the
robustness of the proposed method for detecting unknown face presentation
attacks.

基于声学回声的手机面部表示攻击检测方法是使用宽脉冲生成新型传输信号，通过处理接收信号的反射特征，检测面部展示攻击，实验表明该方法对于检测未知面部展示攻击具有鲁棒性。