Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier
复制标题
DOI：
10.1109/fg.2019.8756525
复制
发表时间：
2019-03
期刊：
影响因子：
--
通讯作者：
Poojan Oza;Vishal M. Patel
中科院分区：
文献类型：
--
作者：
Poojan Oza;Vishal M. Patel
主动身份验证是指在用户与移动的设备交互的整个过程中，对用户进行不显眼的监视和身份验证的过程。一般来说，主动身份验证问题被建模为一类分类问题，由于无法获得来自冒名顶替者用户的数据。通常，注册用户被认为是目标类（真正的），未经授权的用户被认为是未知类（冒名顶替者）。我们提出了一种基于卷积神经网络（CNN）的单类分类方法，其中零中心高斯噪声和自动编码器分别用于对伪负类进行建模和正则化网络以学习一类数据的有意义的特征表示。使用交叉熵和重建误差损失的组合来训练整个网络。所提出的方法的一个关键特征是，任何预先训练的CNN都可以用作一个类别分类的基础网络。该框架的有效性证明了使用三个基于人脸的主动认证数据集，它表明，该方法实现了上级性能相比，传统的一类分类方法。源代码可从以下网址获得：github.com/otkupjnoz/oc-acnn。
Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at : github.com/otkupjnoz/oc-acnn.