HarassGuard: Detecting Harassment Behaviors in Social Virtual Reality with Vision-Language Models

TL;DR

HarassGuard is a vision-language model system designed to detect physical harassment in social VR environments using only visual data, achieving high accuracy with fewer training samples.

Contribution

The paper introduces HarassGuard, a novel vision-language model that effectively detects harassment in social VR with enhanced privacy and less training data compared to existing methods.

Findings

HarassGuard achieves up to 88.09% accuracy in binary harassment detection.

It reaches 68.85% accuracy in multi-class harassment classification.

HarassGuard requires significantly fewer fine-tuning samples than baseline models.

Abstract

Social Virtual Reality (VR) platforms provide immersive social experiences but also expose users to serious risks of online harassment. Existing safety measures are largely reactive, while proactive solutions that detect harassment behavior during an incident often depend on sensitive biometric data, raising privacy concerns. In this paper, we present HarassGuard, a vision-language model (VLM) based system that detects physical harassment in social VR using only visual input. We construct an IRB-approved harassment vision dataset, apply prompt engineering, and fine-tune VLMs to detect harassment behavior by considering contextual information in social VR. Experimental results demonstrate that HarassGuard achieves competitive performance compared to state-of-the-art baselines (i.e., LSTM/CNN, Transformer), reaching an accuracy of up to 88.09% in binary classification and 68.85% in multi-class classification. Notably, HarassGuard matches these baselines while using significantly fewer fine-tuning samples (200 vs. 1,115), offering unique advantages in contextual reasoning and privacy-preserving detection.