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收稿：2023-09-12，
修回：2024-01-04，
纸质出版：2024-09-16
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目的
2
指纹识别技术已大规模应用于人们的日常生活中，如身份鉴定、指纹支付与考勤等。然而，最新研究表明这些系统极易遭受伪造指纹的欺骗攻击，因此在使用指纹认证用户身份前，鉴别待测指纹的真伪至关重要。伪造指纹的制作材料具有多样性，现有工作忽视了不同材料伪造指纹之间数据分布的关联性，致使跨材料检测泛化性普遍较低。因此，本文通过分析不同材料伪造指纹数据间的分布关联性，挖掘不同伪造指纹间的材料域不变伪造特征，提出了一种基于共性特征学习的高泛化伪造指纹检测方法。
方法
2
首先，为了表征和学习不同材料伪造指纹间的特征，设计了一种多尺度伪造特征提取器（multi-scale spoofing feature extractor， MSFE），包含一个多尺度空间通道（multi-scale spatial-channel， MSC）注意力模块，以学习真假指纹类间的细粒度差异特征。然后，为了进一步分析不同材料伪造指纹数据间的分布关联性，又构造了一种共性伪造特征提取器（common spoofing feature extractor， CSFE），在MSFE先验知识的引导下进行多任务的材料域不变伪造特征学习。最后，设计一个材料鉴别器对学习到的共性伪造特征进行约束，同时构建一个自适应联合优化损失模块来平衡多个模块在训练过程中的损失权重，以进一步提高面对未知材料伪造指纹检测时的泛化性。
结果
2
在两个公开的指纹数据集（LivDet（liveness detection competition）2017和LivDet2019）上进行了跨材料测试，实验结果表明所提算法相较对比工作，ACE（average classification error）降低了1.34%，TDR（true detection rate）提高了1.43%，表现出较高的泛化性。
结论
2
本文算法在ACE和TDR方面均取得优异性能。此外，当面对未知材料的伪造指纹检测时，同样表现出较强的泛化性。
Objective
2
The realm of our daily lives has witnessed the ubiquitous integration of fingerprint recognition technology in domains， such as authorized identification， fingerprint-based payments， and access control systems. However， recent studies have revealed the vulnerability of these systems to spoofing fingerprint attacks. Attackers can deceive authentication systems by imitating fingerprints using artificial materials. Thus， the authenticity of fingerprint under scrutiny must be ascertained prior to its use to authenticate the user's identity. The development of a spoofing fingerprint detection technology has attracted extensive attention from the academia and industry. The creation of spoofing fingerprints involve the use of diverse materials. The present research disregards the correlation of data distribution among spoofing fingerprints crafted from various materials， which consequently leads to limited generalization in cross-material detection. Hence， a high-generalization spoofing fingerprint detection method based on commonality feature learning is proposed through the analysis of the distribution correlation among counterfeit fingerprint data originating from diverse materials and the exploration of invariant forgery features within the material domain of distinct counterfeit fingerprints.
Method
2
First， to characterize and learn the features of spoofing fingerprints obtained using various materials， a multiscale spoofing feature extractor （MFSE） is designed， and it includes a multiscale spatial-channel attention module to allow the MFSE to pay more attention to fine-grained differences between live and fake fingerprints and improve the capability of the network to learn spoofing features. Then， a common spoofing feature extractor （CSFE） is constructed for further analysis of the distribution correlation between spoofing fingerprint data of different materials and extraction of common spoofing features between spoofing fingerprints made from various materials. Under the guidance of prior knowledge on MFSE， CSFE calculates the distance of the feature distribution extracted by MFSE and CSFE in the regenerated Hilbert space through the feature distance measurement module and minimizes the maximum mean difference （MMD） of data distributions to reduce the distance between them. The multitask material domain invariant spoofing feature learning is implemented， and a material discriminator is designed to constrain the learned common spoofing features and remove specific material information from the spoofing fingerprint. CSFE involves the calculation of multiple loss functions. Manually setting the weight ratio of these loss functions may prevent the improvement of model performance. Therefore， an adaptive joint-optimization loss function is used to balance the loss values of each module and further expand the generalization capability of the network in the presence of unknown material spoofing fingerprints. The training process involves the use of a fingerprint image containing two kinds of labels， which include the authenticity label of the fingerprint and material label of the forged fingerprint. The true fingerprint lacks material properties and is marked as 0. Forged fingerprints are numbered from 1 based on the material category， and the authenticity of fingerprints and type of forged materials are assessed based on the authenticity and material labels， respectively. The random gradient descent method is used for optimization， and the learning rate setting is from 0.001， which is reduced by 0.1 time per 10 epoch.
Result
2
The experimental results on two public datasets revealed that the algorithm proposed in this paper achieved the best comprehensive performance in the cross-material detection of forged fingerprints. On the GreenBit sensor of LivDet2017 dataset， average classification error （ACE） reduced the rate by 0.16% compared with the second-ranked spoofing fingerprint detection model and increased true detection rate （TDR） by 2.4%. On the Digital persona sensor of LivDet2017 dataset， ACE reduced the rate by 0.26% compared with the second-ranked forgery fingerprint detection model and increased TDR by 0.7%. On LivDet2019 dataset， ACE reduces the rate by 1.34% on average compared with the second-ranked spoofing fingerprint detection model and increases TDR by 1.43% on average. These findings indicate a an increase in the corresponding generalization. A comparative experiment was performed to verify the superiority of the multi-scale spatial-channel （MSC） attention module to the convolutional block attention module （CBAM） module in spoofing fingerprint detection. To better evaluate our method， we conducted a series of ablation experiments to verify each module involved in common feature extraction training to aid in the cross-material spoofing fingerprint detection task. To reveal the improved generalization performance of CSFE compared with MFSE in cross-material spoofing fingerprint detection， this paper visualized the distribution of the proposed features using the t-distributed stochastic neighbor embedding algorithm.
Conclusion
2
The method proposed in this paper achieved better detection results than other methods and exhibited a higher generalization performance in the detection of spoofing fingerprints made of unknown materials. Compared with spoofing fingerprint detection using the same material， the extant spoofing fingerprint detection technique harbors substantial scope for the refinement of its generalization capabilities for cross-material detection. Cross-material spoofing fingerprint detection aptly aligns with practical requirements and bears immense importance in the realm of research pursuits.
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