论文标题
滑动动态作为身份验证的手段：贝叶斯无监督方法的结果
Swipe dynamics as a means of authentication: results from a Bayesian unsupervised approach
论文作者
论文摘要
行为生物识别技术领域是更传统的生物识别系统的一种吸引人的替代方法，因为从用户的角度使用易于使用，并且对演示攻击的潜在稳健性。本文将注意力集中在使用滑动动力学的特定类型的行为生物特征识别上，也称为触摸手势。在触摸手势身份验证中，用户在移动设备的触摸屏上滑动以执行身份验证尝试。通常，触摸手势身份验证和新的行为生物识别技术的关键特征是缺乏可用的数据来训练和验证模型。从机器学习的角度来看，这提出了维度问题的经典诅咒，此处介绍的方法侧重于贝叶斯无监督的模型，因为它们非常适合这种情况。本文介绍了一系列实验，其中包括38个会议，标有受害者以及盲人和肩膀的表现攻击。使用此数据集比较三个模型；两种单模模型：缩减协方差估计和贝叶斯高斯分布，以及贝叶斯非参数无限混合物的高斯，以迪里奇过程为模型。比较这三个模型的同样错误率（EER），并将注意力集中在两个单模模型中如何在不同数量的入学样本中变化。
The field of behavioural biometrics stands as an appealing alternative to more traditional biometric systems due to the ease of use from a user perspective and potential robustness to presentation attacks. This paper focuses its attention to a specific type of behavioural biometric utilising swipe dynamics, also referred to as touch gestures. In touch gesture authentication, a user swipes across the touchscreen of a mobile device to perform an authentication attempt. A key characteristic of touch gesture authentication and new behavioural biometrics in general is the lack of available data to train and validate models. From a machine learning perspective, this presents the classic curse of dimensionality problem and the methodology presented here focuses on Bayesian unsupervised models as they are well suited to such conditions. This paper presents results from a set of experiments consisting of 38 sessions with labelled victim as well as blind and over-the-shoulder presentation attacks. Three models are compared using this dataset; two single-mode models: a shrunk covariance estimate and a Bayesian Gaussian distribution, as well as a Bayesian non-parametric infinite mixture of Gaussians, modelled as a Dirichlet Process. Equal error rates (EER) for the three models are compared and attention is paid to how these vary across the two single-mode models at differing numbers of enrolment samples.
