Due to the diversity of attack materials, fingerprint recognition systems (AFRSs) are vulnerable to malicious attacks. It is thus important to propose effective fingerprint presentation attack detection (PAD) methods for the safety and reliability of AFRSs. However, current PAD methods often exhibit poor robustness under new attack types settings. This paper thus proposes a novel channel-wise feature denoising fingerprint PAD (CFD-PAD) method by handling the redundant noise information ignored in previous studies. The proposed method learns important features of fingerprint images by weighing the importance of each channel and identifying discriminative channels and "noise" channels. Then, the propagation of "noise" channels is suppressed in the feature map to reduce interference. Specifically, a PA-Adaptation loss is designed to constrain the feature distribution to make the feature distribution of live fingerprints more aggregate and that of spoof fingerprints more disperse. Experimental results evaluated on the LivDet 2017 dataset showed that the proposed CFD-PAD can achieve a 2.53% average classification error (ACE) and a 93.83% true detection rate when the false detection rate equals 1.0% (TDR@FDR=1%). Also, the proposed method markedly outperforms the best single-model-based methods in terms of ACE (2.53% vs. 4.56%) and TDR@FDR=1%(93.83% vs. 73.32%), which demonstrates its effectiveness. Although we have achieved a comparable result with the state-of-the-art multiple-model-based methods, there still is an increase in TDR@FDR=1% from 91.19% to 93.83%. In addition, the proposed model is simpler, lighter and more efficient and has achieved a 74.76% reduction in computation time compared with the state-of-the-art multiple-model-based method. The source code is available at https://github.com/kongzhecn/cfd-pad.

翻译：由于攻击材料的多样性，指纹识别系统（AFRS）易受恶意攻击。因此，为保障AFRS的安全性和可靠性，提出有效的指纹呈现攻击检测（PAD）方法至关重要。然而，现有的PAD方法在面对新型攻击类型时往往鲁棒性较差。针对这一问题，本文通过处理先前研究中被忽略的冗余噪声信息，提出了一种新颖的基于通道特征去噪的指纹呈现攻击检测方法（CFD-PAD）。该方法通过评估每个通道的重要性，识别判别性通道与"噪声"通道，从而学习指纹图像的关键特征；随后抑制特征图中"噪声"通道的传播以减少干扰。具体而言，我们设计了PA-Adaptation损失函数来约束特征分布，使真实指纹的特征分布更聚集，而伪造指纹的特征分布更分散。在LivDet 2017数据集上的实验结果表明，所提出的CFD-PAD方法在错误检测率（FDR）为1.0%的条件下，平均分类错误率（ACE）为2.53%，真实检测率（TDR@FDR=1%）达93.83%。此外，该方法在ACE（2.53% vs. 4.56%）和TDR@FDR=1%（93.83% vs. 73.32%）指标上显著优于最佳单模型方法，验证了其有效性。虽然我们已取得与最先进多模型方法相当的结果，但TDR@FDR=1%仍从91.19%提升至93.83%。同时，所提模型更简单、轻量且高效，相比最先进多模型方法计算时间减少了74.76%。源代码已开源至https://github.com/kongzhecn/cfd-pad。