With the increasing integration of smartphones into our daily lives, fingerphotos are becoming a potential contactless authentication method. While it offers convenience, it is also more vulnerable to spoofing using various presentation attack instruments (PAI). The contactless fingerprint is an emerging biometric authentication but has not yet been heavily investigated for anti-spoofing. While existing anti-spoofing approaches demonstrated fair results, they have encountered challenges in terms of universality and scalability to detect any unseen/unknown spoofed samples. To address this issue, we propose a universal presentation attack detection method for contactless fingerprints, despite having limited knowledge of presentation attack samples. We generated synthetic contactless fingerprints using StyleGAN from live finger photos and integrating them to train a semi-supervised ResNet-18 model. A novel joint loss function, combining the Arcface and Center loss, is introduced with a regularization to balance between the two loss functions and minimize the variations within the live samples while enhancing the inter-class variations between the deepfake and live samples. We also conducted a comprehensive comparison of different regularizations' impact on the joint loss function for presentation attack detection (PAD) and explored the performance of a modified ResNet-18 architecture with different activation functions (i.e., leaky ReLU and RelU) in conjunction with Arcface and center loss. Finally, we evaluate the performance of the model using unseen types of spoof attacks and live data. Our proposed method achieves a Bona Fide Classification Error Rate (BPCER) of 0.12\%, an Attack Presentation Classification Error Rate (APCER) of 0.63\%, and an Average Classification Error Rate (ACER) of 0.37\%.

翻译：随着智能手机在日常生活中的日益普及，指纹照片正成为一种潜在的非接触式认证方式。尽管它提供了便利性，但也更容易受到各种展示攻击工具（PAI）的欺骗。非接触式指纹是一种新兴的生物识别认证方式，但其反欺骗研究尚未得到深入探索。尽管现有反欺骗方法取得了不错的效果，但在检测任何未知/未见过的伪造样本方面仍面临通用性和可扩展性的挑战。为解决这一问题，我们提出了一种适用于非接触式指纹的通用展示攻击检测方法，即使对展示攻击样本的了解有限。我们利用StyleGAN从真实指纹照片生成合成非接触式指纹，并将其集成到半监督ResNet-18模型的训练中。引入了一种结合Arcface损失和Center损失的新型联合损失函数，并通过正则化平衡两种损失函数，在最小化真实样本内部变异的同时增强深度伪造样本与真实样本之间的类间变异。我们还对不同正则化对展示攻击检测（PAD）联合损失函数的影响进行了全面比较，并探索了改进的ResNet-18架构在不同激活函数（如leaky ReLU和ReLU）下与Arcface和Center损失结合的性能。最后，我们使用未见过的攻击类型和真实数据评估了模型性能。所提方法实现了0.12%的真实样本分类错误率（BPCER）、0.63%的攻击展示分类错误率（APCER）和0.37%的平均分类错误率（ACER）。