# Securing Virtual Reality Experiences: Unveiling and Tackling Cybersickness Attacks with Explainable AI

arXiv: https://arxiv.org/abs/2503.13419 · HTML: https://mlsanity.com/paper/2503.13419/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 81 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 20 of 20)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Adversarial Examples Are Not Bugs, They Are Features](https://mlsanity.com/paper/1905.02175/) (2019)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [Not Just a Black Box: Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1605.01713/) (2016)
- [Adversarial Attacks and Defences: A Survey](https://mlsanity.com/paper/1810.00069/) (2018)
- [Technical Report on the CleverHans v2.1.0 Adversarial Examples Library](https://mlsanity.com/paper/1610.00768/) (2016)
- [A Survey on Metaverse: Fundamentals, Security, and Privacy](https://mlsanity.com/paper/2203.02662/) (2022)
- [When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures](https://mlsanity.com/paper/1909.03418/) (2019)
- [Adversarial Examples in Deep Learning for Multivariate Time Series Regression](https://mlsanity.com/paper/2009.11911/) (2020)
- [The Dark Side of Perceptual Manipulations in Virtual Reality](https://mlsanity.com/paper/2202.13200/) (2022)
- [Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications](https://mlsanity.com/paper/1811.12476/) (2018)
- [Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps](https://mlsanity.com/paper/1901.02144/) (2019)
- [VR-LENS: Super Learning-based Cybersickness Detection and Explainable AI-Guided Deployment in Virtual Reality](https://mlsanity.com/paper/2302.01985/) (2023)
- [Rotation Blurring: Use of Artificial Blurring to Reduce Cybersickness in Virtual Reality First Person Shooters](https://mlsanity.com/paper/1710.02599/) (2017)
- [Real vs Simulated Foveated Rendering to Reduce Visual Discomfort in Virtual Reality](https://mlsanity.com/paper/2107.01669/) (2021)
- [Dynamic Field of View Reduction Related to Subjective Sickness Measures in an HMD-based Data Analysis Task](https://mlsanity.com/paper/2403.07992/) (2024)

## Built on by (most cited first, 2 of 2)

- [Adversarial VR: An Open-Source Testbed for Evaluating Adversarial Robustness of VR Cybersickness Detection and Mitigation](https://mlsanity.com/paper/2512.17029/) (2025)
- [Lightweight Cybersickness Detection based on User-Specific Eye and Head Tracking Data in Virtual Reality](https://mlsanity.com/paper/2604.17158/) (2026)

## Abstract (arXiv)

The synergy between virtual reality (VR) and artificial intelligence (AI), specifically deep learning (DL)-based cybersickness detection models, has ushered in unprecedented advancements in immersive experiences by automatically detecting cybersickness severity and adaptively various mitigation techniques, offering a smooth and comfortable VR experience. While this DL-enabled cybersickness detection method provides promising solutions for enhancing user experiences, it also introduces new risks since these models are vulnerable to adversarial attacks; a small perturbation of the input data that is visually undetectable to human observers can fool the cybersickness detection model and trigger unexpected mitigation, thus disrupting user immersive experiences (UIX) and even posing safety risks. In this paper, we present a new type of VR attack, i.e., a cybersickness attack, which successfully stops the triggering of cybersickness mitigation by fooling DL-based cybersickness detection models and dramatically hinders the UIX. Next, we propose a novel explainable artificial intelligence (XAI)-guided cybersickness attack detection framework to detect such attacks in VR to ensure UIX and a comfortable VR experience. We evaluate the proposed attack and the detection framework using two state-of-the-art open-source VR cybersickness datasets: Simulation 2021 and Gameplay dataset. Finally, to verify the effectiveness of our proposed method, we implement the attack and the XAI-based detection using a testbed with a custom-built VR roller coaster simulation with an HTC Vive Pro Eye headset and perform a user study. Our study shows that such an attack can dramatically hinder the UIX. However, our proposed XAI-guided cybersickness attack detection can successfully detect cybersickness attacks and trigger the proper mitigation, effectively reducing VR cybersickness.