论文标题
基于异常检测的未知面部表现攻击检测
Anomaly Detection-Based Unknown Face Presentation Attack Detection
论文作者
论文摘要
基于异常检测的SPOOF攻击检测是面部表现攻击检测（FPAD）的最新发展，在该检测中，仅使用未攻击的用户图像来学习欺骗检测器。这些探测器非常重要，因为它们被证明可以很好地推广到新的攻击类型。在本文中，我们提出了一种深入学习解决方案，用于基于异常检测的SPOOF攻击检测，其中分类器和特征表示端到端一起学习。首先，在没有攻击图像的情况下，我们在培训期间介绍了伪阴性类。伪阴性类是使用高斯分布建模的，该分布的平均值是由加权均值均值计算的。其次，我们使用成对的混乱损失来进一步规范训练过程。所提出的方法从CNN的表示能力中受益，并在我们的消融研究中学到更好的FPAD任务功能。我们在四个公开可用的数据集上进行了广泛的实验：重播攻击，Rose-Youtu，Oulu-npu和狂野的野生攻击，以显示拟议方法对先前方法的有效性。代码可在：\ url {https://github.com/yashasvi97/ijcb2020_anomaly}中获得。
Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection (fPAD), where a spoof detector is learned using only non-attacked images of users. These detectors are of practical importance as they are shown to generalize well to new attack types. In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection where both classifier and feature representations are learned together end-to-end. First, we introduce a pseudo-negative class during training in the absence of attacked images. The pseudo-negative class is modeled using a Gaussian distribution whose mean is calculated by a weighted running mean. Secondly, we use pairwise confusion loss to further regularize the training process. The proposed approach benefits from the representation learning power of the CNNs and learns better features for fPAD task as shown in our ablation study. We perform extensive experiments on four publicly available datasets: Replay-Attack, Rose-Youtu, OULU-NPU and Spoof in Wild to show the effectiveness of the proposed approach over the previous methods. Code is available at: \url{https://github.com/yashasvi97/IJCB2020_anomaly}
