Cross-Domain Cross-Task Transfer Mobile Touch-Stroke Authentication

Abstract

Touch-stroke dynamics have garnered significant attention as a means of mobile user authentication. However, current research often assumes the availability of large datasets tailored to specific smartphone applications. In response to the scarcity of real-world data and the computational limitations of smartphones, we introduce an innovative cross-domain cross-task transfer framework for mobile user authentication. Our approach harnesses auxiliary data from diverse applications and user sets to complement the data scarcity. A two-stream Transformer network is devised to enable knowledge transfer between disparate domains and tasks. This network adeptly amalgamates user-discriminative insights from various applications while refining precise embeddings tailored to a target application. Meta-learning optimization is employed to counteract negative knowledge sharing. Furthermore, we introduce a novel data augmentation technique based on feature covariance to bolster the training of robust one-class classifiers. Experimental findings showcased the efficacy of our method in harnessing heterogeneous data sources that exhibit domain and task disparities. Our approach achieved commendable performance to state-of-the-art methods with a reasonable computational burden on smartphones.