Integrating Handcrafted Features with Deep Representations for Smartphone Authentication

Recent research demonstrates the potential of touch dynamics as a usable and privacy-preserving scheme for smartphone authentication. Most existing approaches rely on handcrafted features since deep models may be vulnerable to behavioral uncertainty due to the lack of consistent semantic information. Toward this end, we propose an approach to integrating handcrafted features into two phases of the deep learning process. On one hand, we present three fine-grained behavior representations by encoding semantic handcrafted features into the raw touch actions. On the other hand, we devise a deep Feature Regularization Net (FRN) architecture to combine the complementary information in both handcrafted and deep features. FRN involves handcrafted features as regularization to guide the learning process of deep features and selectively fuses these two feature types through a feature re-weighting mechanism. Experimental findings demonstrate that FRN outperforms the existing handcrafted or deep features even with smaller training and template sets. The framework also works for SOTA deep models and further boosts the accuracy. Results show that our approach is more reliable to alleviate behavioral variability and is competitively robust to statistical attacks compared with the most effective handcrafted features, suggesting a promising mechanism to improve the effectiveness and usability of behavioral authentication for multi-touch enabled mobile devices.