论文标题
D-NETPAD：可解释且可解释的虹膜演示攻击探测器
D-NetPAD: An Explainable and Interpretable Iris Presentation Attack Detector
论文作者
论文摘要
虹膜识别系统容易受到呈现攻击或PA的攻击，在该攻击中，对手呈现诸如印刷眼睛，塑料眼或化妆品隐形眼镜之类的人工制品，以绕过系统。在这项工作中，我们提出了一个基于Densenet卷积神经网络体系结构的有效且健壮的IRIS PA探测器，称为D-NETPAD。它展示了PA工件，传感器和数据集的普遍性。在专有数据集和公开可用数据集（Livdet-2017）上进行的实验证实了提出的虹膜PA检测方法的有效性。所提出的方法在专有数据集和Livdet-2017数据集上的最先进方法上以虚假检测率为0.2 \％的错误检测率导致98.58 \％的真实检测率。我们分别使用T-SNE图和Grad-CAM可视化中间特征分布和固定热图，以解释D-NetPad的性能。此外，我们进行了频率分析，以解释网络提取的特征的性质。源代码和训练有素的模型可在https://github.com/iprobe-lab/d-netpad上找到。
An iris recognition system is vulnerable to presentation attacks, or PAs, where an adversary presents artifacts such as printed eyes, plastic eyes, or cosmetic contact lenses to circumvent the system. In this work, we propose an effective and robust iris PA detector called D-NetPAD based on the DenseNet convolutional neural network architecture. It demonstrates generalizability across PA artifacts, sensors and datasets. Experiments conducted on a proprietary dataset and a publicly available dataset (LivDet-2017) substantiate the effectiveness of the proposed method for iris PA detection. The proposed method results in a true detection rate of 98.58\% at a false detection rate of 0.2\% on the proprietary dataset and outperfoms state-of-the-art methods on the LivDet-2017 dataset. We visualize intermediate feature distributions and fixation heatmaps using t-SNE plots and Grad-CAM, respectively, in order to explain the performance of D-NetPAD. Further, we conduct a frequency analysis to explain the nature of features being extracted by the network. The source code and trained model are available at https://github.com/iPRoBe-lab/D-NetPAD.
