VRSafe: A Secure Virtual Keyboard to Mitigate Keystroke Inference in Virtual Reality

• URL: http://arxiv.org/abs/2604.21001v1
• Date: Wed, 22 Apr 2026 18:49:16 GMT
• Title: VRSafe: A Secure Virtual Keyboard to Mitigate Keystroke Inference in Virtual Reality
• Abstract summary: Password-based authentication is one of the most commonly used methods for verifying user identities.<n> keystroke inference and shoulder surfing are still effective in virtual reality (VR) applications.<n>This paper presents a novel QWERTY keyboard called textitVRSafe that is resilient to keystroke inference attacks.
• Score: 9.100496325427127
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Password-based authentication is one of the most commonly used methods for verifying user identities, and its widespread usage continues in virtual reality (VR) applications. As a result, various forms of attacks on password-based authentication in traditional environments such as keystroke inference and shoulder surfing, are still effective in VR applications. While keystroke inference attacks on virtual keyboards have been studied extensively, few efforts have developed an effective and cost-efficient defense strategy to mitigate keystroke inferences in VR. To address this gap, this paper presents a novel QWERTY keyboard called \textit{VRSafe} that is resilient to keystroke inference attacks. The proposed keyboard carefully introduces false positive keystrokes into the information collected by attackers during the typing process, making the inference of the original password difficult. \textit{VRSafe} also incorporates a novel malicious login detector that can effectively identify unauthorized login attempts using credentials inferred from keystroke inference attacks with high detection rate and minimal time and memory cost. The proposed design is evaluated through both simulation experiments and a real-world user study, and the results show that \textit{VRSafe} can significantly reduce the accuracy of keystroke inference attacks while incurring a modest overhead from a usability standpoint.

Related papers

• QUACK! Making the (Rubber) Ducky Talk: A Systematic Study of Keystroke Dynamics for HID Injection Detection [60.105690843777666]
  We show that robust, privacy-preserving detection is achievable using lightweight models operating solely on timing features.<n>Our analysis reveals that attacker sophistication does not monotonically translate into improved evasion.
  arXiv  Detail & Related papers  (2026-04-17T08:53:24Z)
• An Agent-Based Modeling Approach to Free-Text Keyboard Dynamics for Continuous Authentication [0.0]
  Continuous authentication systems leveraging free-text keyboard dynamics offer a promising additional layer of security in a multifactor authentication setup.<n>This study investigates the efficacy of behavioral biometrics by employing an Agent-Based Model (ABM) to simulate diverse typing profiles across mechanical and membrane keyboards.
  arXiv  Detail & Related papers  (2025-05-08T07:42:05Z)
• Non-intrusive and Unconstrained Keystroke Inference in VR Platforms via Infrared Side Channel [7.493632378968814]
  We present a new side-channel leakage in the constellation tracking system of mainstream VR platforms.<n>The infrared (IR) signals emitted from the VR controllers for controller-headset interactions can be maliciously exploited to reconstruct unconstrained input keystrokes.<n>We propose a novel keystroke inference attack named VRecKey to demonstrate the feasibility and practicality of this novel infrared side channel.
  arXiv  Detail & Related papers  (2024-12-19T13:09:46Z)
• GAZEploit: Remote Keystroke Inference Attack by Gaze Estimation from Avatar Views in VR/MR Devices [8.206832482042682]
  We unveil GAZEploit, a novel eye-tracking based attack specifically designed to exploit these eye-tracking information by leveraging the common use of virtual appearances in VR applications. Our research, involving 30 participants, achieved over 80% accuracy in keystroke inference. Our study also identified over 15 top-rated apps in the Apple Store as vulnerable to the GAZEploit attack, emphasizing the urgent need for bolstered security measures for this state-of-the-art VR/MR text entry method.
  arXiv  Detail & Related papers  (2024-09-12T15:11:35Z)
• Deep Motion Masking for Secure, Usable, and Scalable Real-Time Anonymization of Virtual Reality Motion Data [49.68609500290361]
  Recent studies have demonstrated that the motion tracking "telemetry" data used by nearly all VR applications is as uniquely identifiable as a fingerprint scan. We present in this paper a state-of-the-art VR identification model that can convincingly bypass known defensive countermeasures.
  arXiv  Detail & Related papers  (2023-11-09T01:34:22Z)
• Can Virtual Reality Protect Users from Keystroke Inference Attacks? [23.587497604556823]
  We show that despite assumptions of enhanced privacy, VR is unable to shield its users from side-channel attacks that steal private information. This vulnerability arises from VR's greatest strength, its immersive and interactive nature.
  arXiv  Detail & Related papers  (2023-10-24T21:19:38Z)
• Conditional Generative Adversarial Network for keystroke presentation attack [0.0]
  We propose to study a new approach aiming to deploy a presentation attack towards a keystroke authentication system. Our idea is to use Conditional Generative Adversarial Networks (cGAN) for generating synthetic keystroke data that can be used for impersonating an authorized user. Results indicate that the cGAN can effectively generate keystroke dynamics patterns that can be used for deceiving keystroke authentication systems.
  arXiv  Detail & Related papers  (2022-12-16T12:45:16Z)
• TypeNet: Deep Learning Keystroke Biometrics [77.80092630558305]
  We introduce TypeNet, a Recurrent Neural Network trained with a moderate number of keystrokes per identity. With 5 gallery sequences and test sequences of length 50, TypeNet achieves state-of-the-art keystroke biometric authentication performance. Our experiments demonstrate a moderate increase in error with up to 100,000 subjects, demonstrating the potential of TypeNet to operate at an Internet scale.
  arXiv  Detail & Related papers  (2021-01-14T12:49:09Z)
• Robust Text CAPTCHAs Using Adversarial Examples [129.29523847765952]
  We propose a user-friendly text-based CAPTCHA generation method named Robust Text CAPTCHA (RTC) At the first stage, the foregrounds and backgrounds are constructed with randomly sampled font and background images. At the second stage, we apply a highly transferable adversarial attack for text CAPTCHAs to better obstruct CAPTCHA solvers.
  arXiv  Detail & Related papers  (2021-01-07T11:03:07Z)
• MixNet for Generalized Face Presentation Attack Detection [63.35297510471997]
  We have proposed a deep learning-based network termed as textitMixNet to detect presentation attacks. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category.
  arXiv  Detail & Related papers  (2020-10-25T23:01:13Z)
• TypeNet: Scaling up Keystroke Biometrics [79.19779718346128]
  We first analyze to what extent our method based on a Recurrent Neural Network (RNN) is able to authenticate users when the amount of data per user is scarce. With 1K users for testing the network, a population size comparable to previous works, TypeNet obtains an equal error rate of 4.8%. Using the same amount of data per user, as the number of test users is scaled up to 100K, the performance in comparison to 1K decays relatively by less than 5%.
  arXiv  Detail & Related papers  (2020-04-07T18:05:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.