Harnessing Vital Sign Vibration Harmonics for Effortless and Inbuilt XR User Authentication

  • Tianfang Zhang
  • , Qiufan Ji
  • , Md Mojibur Rahman Redoy Akanda
  • , Zhengkun Ye
  • , Ahmed Tanvir Mahdad
  • , Cong Shi
  • , Yan Wang
  • , Nitesh Saxena
  • , Yingying Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Extended Reality (XR) headsets are increasingly serving as repositories for substantial volumes of sensitive data and gateways to web applications. This transition highlights the need for convenient and secure user authentication solutions. Traditional password/PIN-based schemes are ill-suited to the XR's gesture- and voice-based interfaces and are prone to shoulder-surfing attacks. Some recent XR systems incorporate two-factor authentication, but it requires additional operations on a second device (e.g., a smartphone or wearable). In this work, we introduce the first effortless and inbuilt XR user authentication system by leveraging the harmonics of vibrations excited by users' vital signs. The system is transparent to users (no efforts during enrollment and authentication) and requires no additional hardware. The key idea is that vital signs (i.e., breathing and heart beating) naturally generate low-frequency mechanical vibrations, causing human skull to vibrate and produces harmonic signals. When the harmonics pass the human head, they carry rich biometrics associated with the wearer's skull structure and soft tissues, which can be captured by the XR motion sensors. Instead of directly utilizing the vibrations, we extract more reliable biometrics from the ratios among different harmonic frequencies, which capture wearers' unique head and facial attenuation properties and are non-volatile when the periodicity and amplitude of vital signs fluctuate. We further design an adaptive filter to mitigate the body motion distortions in common XR interactions. By adopting advanced deep learning models with the attention mechanism, our system realizes effective and robust authentication across XR scenarios. Evaluations across 10 months, with 52 users and two popular XR headsets, show that our system can accurately authenticate users with over 95% true positive rates and rejects unauthorized users with over 98% true negative rates under various XR scenarios, with biometrics remaining consistent over long-term periods.

Original languageEnglish (US)
Title of host publicationCCS 2025 - Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery, Inc
Pages3520-3534
Number of pages15
ISBN (Electronic)9798400715259
DOIs
StatePublished - Nov 22 2025
Event32nd ACM SIGSAC Conference on Computer and Communications Security, CCS 2025 - Taipei, Taiwan, Province of China
Duration: Oct 13 2025Oct 17 2025

Publication series

NameCCS 2025 - Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security

Conference

Conference32nd ACM SIGSAC Conference on Computer and Communications Security, CCS 2025
Country/TerritoryTaiwan, Province of China
CityTaipei
Period10/13/2510/17/25

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Computer Science Applications

Keywords

  • Authentication
  • Vital sign harmonics
  • XR headsets

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