CNN-Based Continuous Authentication on Smartphones with Auto Augmentation Search

Abstract

In this paper, we present CAuSe, a CNN-based Continuous Authentication on smartphones using Auto Augmentation Search, where the CNN is specially designed for deep feature extraction and the auto augmentation search is exploited for CNN training data augmentation. Specifically, CAuSe consists of three stages of the offline stage, registration stage and authentication stage. In the offline stage, we utilize auto augmentation search on the collected data to find an optimal strategy for CNN training data augmentation. Then, we specially design a CNN to learn and extract deep features from the augmented data and train the LOF classifier after 95 features are selected by PCA in the registration stage. With the trained CNN and LOF classifier, CAuSe identifies the current user as a legitimate user or an impostor in the authentication stage. Based on our dataset, we evaluate the effectiveness of optimal strategy and the performance of CAuSe. The experimental results demonstrate that the strategy of Time-Warping(0.6)+Time-Warping(0.6) reaches the highest accuracy of 93.19% with data size 400 and CAuSe achieves the best authentication accuracy of 96.93%, respectively, comparing with other strategies and classifiers.