The vulnerability of automated fingerprint recognition systems to presentation attacks (PA), i.e., spoof or altered fingers, has been a growing concern, warranting the development of accurate and efficient presentation attack detection (PAD) methods. However, one major limitation of the existing PAD solutions is their poor generalization to new PA materials and fingerprint sensors, not used in training. In this study, we propose a robust PAD solution with improved cross-material and cross-sensor generalization. Specifically, we build on top of any CNN-based architecture trained for fingerprint spoof detection combined with cross-material spoof generalization using a style transfer network wrapper. We also incorporate adversarial representation learning (ARL) in deep neural networks (DNN) to learn sensor and material invariant representations for PAD. Experimental results on LivDet 2015 and 2017 public domain datasets exhibit the effectiveness of the proposed approach.

中文翻译：

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指纹呈现攻击检测：一种传感器和材料不可知的方法

自动指纹识别系统对演示攻击 (PA) 的脆弱性，即欺骗或更改的手指，已成为日益受到关注的问题，需要开发准确有效的演示攻击检测 (PAD) 方法。然而，现有 PAD 解决方案的一个主要限制是它们对新 PA 材料和指纹传感器的泛化能力较差，未用于训练。在这项研究中，我们提出了一种强大的 PAD 解决方案，具有改进的跨材料和跨传感器泛化能力。具体来说，我们建立在任何基于 CNN 的架构之上，这些架构针对指纹欺骗检测与使用样式传输网络包装器的跨材料欺骗泛化相结合。我们还在深度神经网络 (DNN) 中加入了对抗性表示学习 (ARL)，以学习 PAD 的传感器和材料不变表示。LivDet 2015 和 2017 公共领域数据集的实验结果证明了所提出方法的有效性。