User Authentication Using Typing Habits in Virtual Reality

Text input has a wide range of application scenarios in virtual reality. The integration of physical keyboards into virtual environments facilitates more straightforward and efficient text input. However, while the user is immersed in a virtual environment utilizing a physical keyboard for textual interaction, the user's information (e.g., passwords) is vulnerable to potential attackers. This paper explores the feasibility of utilizing hand tracking data for implicit authentication in virtual reality as users type fixed text. We create an undirected graph from hand key-points to model the user's typing habits. We collected hand tracking data from 51 participants twice, two weeks apart. They used a QWERTY keyboard to input text in a virtual space. A spatial-temporal graph convolutional network was employed to extract the user's typing habits, resulting in an equal error rate of 0.048 in the case of internal spoofing attacks and 0.055 in the case of external spoofing attacks.