论文标题
学习使用多通道卷积神经网络的面部表现攻击检测的一类表示
Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks
论文作者
论文摘要
面部识别已演变为广泛使用的生物识别方式。但是，其针对演示攻击的脆弱性构成了重大的安全威胁。尽管演示攻击检测（PAD）方法试图解决此问题，但它们通常无法概括看不见的攻击。在这项工作中，我们为使用单级分类器提出了一个新的PAD框架，其中使用多渠道卷积神经网络（MCCNN）学习了所使用的表示形式。引入了一种新颖的损失函数，迫使网络学习真正的嵌入真正的损失功能，同时远离攻击的代表。在这些嵌入式的顶部，用于PAD任务。拟议的框架引入了一种新颖的方法，可以从真正的（已知）攻击类中学习强大的垫系统。这一点尤其重要，因为收集善意的数据和更简单的攻击要比收集各种昂贵的攻击要容易得多。在公开可用的WMCA多渠道面垫数据库中评估了所提出的系统，该数据库包含各种各样的2D和3D攻击。此外，我们仅使用RGB通道对MLFP和SIW-M数据集进行了实验。在看不见的攻击方案中的出色表现显示了所提出的方法的有效性。复制结果的软件，数据和协议可公开使用。
Face recognition has evolved as a widely used biometric modality. However, its vulnerability against presentation attacks poses a significant security threat. Though presentation attack detection (PAD) methods try to address this issue, they often fail in generalizing to unseen attacks. In this work, we propose a new framework for PAD using a one-class classifier, where the representation used is learned with a Multi-Channel Convolutional Neural Network (MCCNN). A novel loss function is introduced, which forces the network to learn a compact embedding for bonafide class while being far from the representation of attacks. A one-class Gaussian Mixture Model is used on top of these embeddings for the PAD task. The proposed framework introduces a novel approach to learn a robust PAD system from bonafide and available (known) attack classes. This is particularly important as collecting bonafide data and simpler attacks are much easier than collecting a wide variety of expensive attacks. The proposed system is evaluated on the publicly available WMCA multi-channel face PAD database, which contains a wide variety of 2D and 3D attacks. Further, we have performed experiments with MLFP and SiW-M datasets using RGB channels only. Superior performance in unseen attack protocols shows the effectiveness of the proposed approach. Software, data, and protocols to reproduce the results are made available publicly.
