SegAuth: Semantic-aware multi-motion behavioral biometrics based implicit authentication

Multi-motion behavioral biometrics based implicit authentication leverages individual unique behavioral patterns for user authentication. However, traditional methods using fixed-sized time window segmentation often disrupt local temporal structures and overlook behavioral semantics. This paper investigates a semantic-aware segmentation-based implicit authentication approach, yet challenges persist in achieving semantically consistent segmentation, fixed-dimension representations for variable-length data, and robust modeling under large intra-class variance. Towards this end, we propose SegAuth, a novel semantic-aware multi-motion behavioral biometrics based implicit authentication system. Specifically, given the input raw multi-motion data, SegAuth first adopts a data-driven semantic-aware segmentation method to adaptively generate variable-sized segments, capturing fine-grained behavioral patterns for authentication. Next, SegAuth proposes a causal temporal convolutional network, which allows to learn the effective embedding of varied-sized multi-motion data segments. Finally, a multi-center deep one-class classifier–based authentication model is developed to capture behavioral representations from genuine user data characterized by high intra-class variance, allowing it to identify and authenticate behaviors that differ from normal patterns. Extensive experiments are conducted on a large-scale uncontrolled evaluation dataset. The experimental results demonstrate the state-of-the-art authentication performance of SegAuth.