arXiv subjects

Christian Rack

Publications and source records attributed to Christian Rack.

6 recordsLinked to original sources

arXiv2025

Motion-Based User Identification across XR and Metaverse Applications by Deep Classification and Similarity Learning

This paper examines the generalization capacity of two state-of-the-art classification and similarity learning models in reliably identifying users based on their motions in various Extended Reality (XR) applications. We developed a novel dataset containing a wide range of motion data from 49 users in five different XR applications: four XR games with distinct tasks and action patterns, and an additional social XR application with no predefined task sets. The dataset is used to evaluate the performance and, in particular, the generalization capacity of the two models across applications. Our results indicate that while the models can accurately identify individuals within the same application, their ability to identify users across different XR applications remains limited. Overall, our results provide insight into current models generalization capabilities and suitability as biometric methods for user verification and identification. The results also serve as a much-needed risk assessment of hazardous and unwanted user identification in XR and Metaverse applications. Our cross-application XR motion dataset and code are made available to the public to encourage similar research on the generalization of motion-based user identification in typical Metaverse application use cases.

cs.HC↗

arXiv2024

Effect of Duration and Delay on the Identifiability of VR Motion

Social virtual reality is an emerging medium of communication. In this medium, a user's avatar (virtual representation) is controlled by the tracked motion of the user's headset and hand controllers. This tracked motion is a rich data stream that can leak characteristics of the user or can be effectively matched to previously-identified data to identify a user. To better understand the boundaries of motion data identifiability, we investigate how varying training data duration and train-test delay affects the accuracy at which a machine learning model can correctly classify user motion in a supervised learning task simulating re-identification. The dataset we use has a unique combination of a large number of participants, long duration per session, large number of sessions, and a long time span over which sessions were conducted. We find that training data duration and train-test delay affect identifiability; that minimal train-test delay leads to very high accuracy; and that train-test delay should be controlled in future experiments.

cs.CR↗

arXiv2024

Effect of Data Degradation on Motion Re-Identification

The use of virtual and augmented reality devices is increasing, but these sensor-rich devices pose risks to privacy. The ability to track a user's motion and infer the identity or characteristics of the user poses a privacy risk that has received significant attention. Existing deep-network-based defenses against this risk, however, require significant amounts of training data and have not yet been shown to generalize beyond specific applications. In this work, we study the effect of signal degradation on identifiability, specifically through added noise, reduced framerate, reduced precision, and reduced dimensionality of the data. Our experiment shows that state-of-the-art identification attacks still achieve near-perfect accuracy for each of these degradations. This negative result demonstrates the difficulty of anonymizing this motion data and gives some justification to the existing data- and compute-intensive deep-network based methods.

cs.CR↗

arXiv2023

Who Is Alyx? A new Behavioral Biometric Dataset for User Identification in XR

This article presents a new dataset containing motion and physiological data of users playing the game "Half-Life: Alyx". The dataset specifically targets behavioral and biometric identification of XR users. It includes motion and eye-tracking data captured by a HTC Vive Pro of 71 users playing the game on two separate days for 45 minutes. Additionally, we collected physiological data from 31 of these users. We provide benchmark performances for the task of motion-based identification of XR users with two prominent state-of-the-art deep learning architectures (GRU and CNN). After training on the first session of each user, the best model can identify the 71 users in the second session with a mean accuracy of 95% within 2 minutes. The dataset is freely available under https://github.com/cschell/who-is-alyx

cs.HC↗

arXiv2023

Inferring Private Personal Attributes of Virtual Reality Users from Head and Hand Motion Data

Motion tracking "telemetry" data lies at the core of nearly all modern virtual reality (VR) and metaverse experiences. While generally presumed innocuous, recent studies have demonstrated that motion data actually has the potential to uniquely identify VR users. In this study, we go a step further, showing that a variety of private user information can be inferred just by analyzing motion data recorded from VR devices. We conducted a large-scale survey of VR users (N=1,006) with dozens of questions ranging from background and demographics to behavioral patterns and health information. We then obtained VR motion samples of each user playing the game "Beat Saber," and attempted to infer their survey responses using just their head and hand motion patterns. Using simple machine learning models, over 40 personal attributes could be accurately and consistently inferred from VR motion data alone. Despite this significant observed leakage, there remains limited awareness of the privacy implications of VR motion data, highlighting the pressing need for privacy-preserving mechanisms in multi-user VR applications.

cs.HC↗

arXiv2023

Versatile User Identification in Extended Reality using Pretrained Similarity-Learning

Various machine learning approaches have proven to be useful for user verification and identification based on motion data in eXtended Reality (XR). However, their real-world application still faces significant challenges concerning versatility, i.e., in terms of extensibility and generalization capability. This article presents a solution that is both extensible to new users without expensive retraining, and that generalizes well across different sessions, devices, and user tasks. To this end, we developed a similarity-learning model and pretrained it on the "Who Is Alyx?" dataset. This dataset features a wide array of tasks and hence motions from users playing the VR game "Half-Life: Alyx". In contrast to previous works, we used a dedicated set of users for model validation and final evaluation. Furthermore, we extended this evaluation using an independent dataset that features completely different users, tasks, and three different XR devices. In comparison with a traditional classification-learning baseline, our model shows superior performance, especially in scenarios with limited enrollment data. The pretraining process allows immediate deployment in a diverse range of XR applications while maintaining high versatility. Looking ahead, our approach paves the way for easy integration of pretrained motion-based identification models in production XR systems.

cs.HC↗