激光散斑衬度成像和深度神经网络检测已知和未知指纹呈现攻击的有效性
On the Effectiveness of Laser Speckle Contrast Imaging and Deep Neural Networks for Detecting Known and Unknown Fingerprint Presentation Attacks
摘要：
由于攻击技术的不断进步，指纹呈现攻击检测（FPAD）正在成为一个越来越具有挑战性的问题，这会产生“逼真”的假指纹呈现。最近，激光散斑衬度成像（LSCI）已被引入作为一种新的传感模式FPAD。LSCI具有捕获皮肤表面下的血流的有趣特性。为了研究LSCI对FPAD的重要性和有效性，我们使用不同的基于补丁的深度神经网络架构进行了全面的研究。我们研究的架构包括2D和3D卷积网络以及使用长短期记忆（LSTM）单元的递归网络。该研究表明，强大的FPAD性能可以实现使用LSCI。我们在一个新的大型数据集上评估不同的模型。该数据集由3743个真实样本组成，这些样本来自335个独特的主题，以及218个演示攻击样本，包括六种不同类型的攻击。为了检查改变训练集和测试集的效果，我们进行了3重交叉验证评估。为了检查存在看不见的攻击的影响，我们采用了留一攻击策略。网络的FPAD分类结果分别针对时间和空间补丁大小进行了优化和调整，表明LSTM实现了最佳性能。
英文摘要：
Fingerprint presentation attack detection (FPAD) is becoming an increasingly challenging problem due to the continuous advancement of attack techniques, which generate `realistic-looking' fake fingerprint presentations. Recently, laser speckle contrast imaging (LSCI) has been introduced as a new sensing modality for FPAD. LSCI has the interesting characteristic of capturing the blood flow under the skin surface. Toward studying the importance and effectiveness of LSCI for FPAD, we conduct a comprehensive study using different patch-based deep neural network architectures. Our studied architectures include 2D and 3D convolutional networks as well as a recurrent network using long short-term memory (LSTM) units. The study demonstrates that strong FPAD performance can be achieved using LSCI. We evaluate the different models over a new large dataset. The dataset consists of 3743 bona fide samples, collected from 335 unique subjects, and 218 presentation attack samples, including six different types of attacks. To examine the effect of changing the training and testing sets, we conduct a 3-fold cross validation evaluation. To examine the effect of the presence of an unseen attack, we apply a leave-one-attack out strategy. The FPAD classification results of the networks, which are separately optimized and tuned for the temporal and spatial patch-sizes, indicate that the best performance is achieved by LSTM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
