用视觉模型检测虚拟现实中的骚扰行为，不依赖隐私数据。
HarassGuard: Detecting Harassment Behaviors in Social Virtual Reality with Vision-Language Models
- 仅用视觉输入，通过提示工程和微调视觉语言模型识别骚扰行为。
- 二分类准确率达88.09%，多分类达68.85%，仅需200个样本微调。
- 兼顾隐私保护与上下文理解，适合社交VR安全系统部署。
社交虚拟现实（VR）平台提供沉浸式社交体验，但也使用户面临严重的网络骚扰风险。现有安全措施多为被动响应，而主动检测骚扰行为的方法常依赖敏感生物特征数据，引发隐私担忧。本文提出HarassGuard，一种基于视觉语言模型（VLM）的系统，仅使用视觉输入即可在社交VR中检测身体骚扰行为。我们构建了经机构审查委员会（IRB）批准的骚扰视觉数据集，采用提示工程并微调VLM，通过考虑社交VR中的上下文信息实现行为识别。实验结果表明，HarassGuard在二分类任务中达到88.09%的准确率，在多分类任务中达68.85%，性能媲美先进基线（如LSTM/CNN、Transformer），且仅需200个微调样本（相比1,115个），展现出卓越的上下文推理能力和隐私保护优势。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。