HarassGuard: Detecting Harassment Behaviors in Social Virtual Reality with Vision-Language Models
This addresses online harassment in social VR for users and developers, offering a proactive and privacy-preserving solution, though it is incremental as it builds on existing vision-language models.
The paper tackles the problem of detecting physical harassment in social VR platforms by developing HarassGuard, a vision-language model system that uses only visual input, achieving up to 88.09% accuracy in binary classification and 68.85% in multi-class classification while requiring fewer fine-tuning samples than baselines.
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.