GANDACOG: Implicit Mobile User Authentication in Multi Environments With Scarce Data
Tiantian Zhu, Jiayuan Chen, Mingjun Ma, Tieming Chen, Mingqi Lv, Zhengqiu Weng, Suyu Zhang · IEEE Internet of Things Journal · 2025
Mobile device user authentication technologies have been studied for decades in the context of personal information security. To strike a balance between security, privacy, and usability, authentication methods based on motion sensors have gained widespread attention in recent years. However, these methods still face several challenges, such as the limited training samples, the finite scene coverage, and the high-cost models. Therefore, there is an urgent need to develop more efficient and reliable solutions to enhance the user experience. To address these challenges, we introduce, which offers the following features: 1) It uses a novel data augmentation method (AUTHGANS) to expand the dataset. 2) It employs a differential attention mechanism to reduce noise interference, improve model scene coverage, and simultaneously reduce the model size during the model training phase, and improve the model’s accuracy. 3) It uses a model distillation strategy (AuthFusion), ensuring high accuracy while reducing the model’s computational requirements on devices. Experiments on a dataset with 1,513 users and noise show that achieves high accuracy while requiring less computational power than other state-of-the-art authentication methods.