Summary of Using Motion Forecasting For Behavior-based Virtual Reality (vr) Authentication, by Mingjun Li et al.
Using Motion Forecasting for Behavior-Based Virtual Reality (VR) Authentication
by Mingjun Li, Natasha Kholgade Banerjee, Sean Banerjee
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a deep learning-based approach for task-based behavioral biometric authentication in virtual reality environments. The authors leverage the concept that user behavior is predictable, using Transformer-based forecasting to predict future motion trajectories. This allows for more accurate user authentication by leveraging the forecasted trajectory. The approach improves upon existing techniques, which rely on complete or near-complete portions of the user’s motion trajectory, and can reduce the equal error rate (EER) in user authentication by up to 36.14%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In virtual reality, behavioral biometric authentication is important for seamless user experience. This paper shows how we can use deep learning to predict a person’s behavior in VR and confirm their identity. It’s like predicting what someone will do next based on what they’ve done before! The researchers used a special kind of forecasting called Transformer-based forecasting to make these predictions. They tested this approach using a big dataset of people throwing balls, and it worked really well – reducing the error rate by 23.85% on average. |
Keywords
* Artificial intelligence * Deep learning * Transformer
