De-Anonymizing Avatars in Virtual Reality: Attacks and Countermeasures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
By providing users with an immersive visual and acoustic experience, virtual reality (VR) serves as a foundational technique for the emerging metaverse. One of the most promising aspects of VR is its ability to protect users’ identities by transforming their physical appearances into avatars with arbitrary appearances in the virtual world. However, the increasing threat of de-anonymization attacks that seek to reveal users’ identities poses significant privacy risks. We propose AvatarHunter, a non-intrusive and user-unaware de-anonymization attack leveraging victims’ inherent movement signatures. AvatarHunter discreetly collects the avatar's gait information by recording videos in the VR scenario without requiring any permissions. Notably, we designed a Unity-based feature extractor that maintains the avatar's movement signature while enabling AvatarHunter to be resistant to changes in the avatar's appearance. We conduct real-world experiments on VRChat to evaluate AvatarHunter's effectiveness. The results demonstrate that in commercial settings, AvatarHunter achieves attack success rates (ASR) of 92.1% and 66.9% in closed-world and open-world avatar scenarios, respectively, significantly surpassing existing benchmarks. Additionally, simulations using an open-source dataset confirm that AvatarHunter can attain over 78% ASR in full-body tracking scenarios. Finally, we discuss several countermeasures and implement an obfuscation mechanism during the avatar rendering phase, significantly reducing the ASR.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it