ViDDAR:视觉语言基于任务模型的基于任务的有害内容检测用于增强现实
IEEE transactions on visualization and computer graphics
|March 12, 2025
概括
本研究介绍了ViDDAR,这是一个使用视觉语言模型 (VLMs) 来检测增强现实 (AR) 中有害虚拟内容的新系统. ViDDAR有效地识别障碍和信息操纵,提高AR任务性能和用户体验.
科学领域:
- 人与计算机的交互
- 计算机视觉 计算机视觉
- 增强现实系统 增强现实系统
背景情况:
- 增强现实 (AR) 增强了用户对虚拟内容的体验,但设计不佳的内容可能会阻碍现实世界的任务执行.
- 损害任务的虚拟内容包括阻碍 (阻止现实世界视图) 和信息操纵 (扭曲解释).
研究的目的:
- 推出ViDDAR (基于视觉语言模型的增强现实任务破坏性内容检测器),这是第一个利用视觉语言模型 (VLM) 来检测AR中有害的虚拟内容的系统.
- 提供一个数学框架来描述AR中的阻碍和信息操纵攻击.
- 为AR攻击评估开发和评估一个定制的开源数据集.
主要方法:
- 开发了ViDDAR,这是一个使用VLM和深度学习来监控和评估AR中的虚拟内容的综合系统.
- 实现了用户边缘云架构以优化性能并最大限度地降低延迟.
- 创建了一个自定义数据集,用于评估检测有害任务内容的检测.
主要成果:
- 在检测阻塞攻击时,ViDDAR的精度高达92.15%,延迟时间为533毫秒.
- ViDDAR在检测信息操纵攻击方面表现出82.46%的准确性,延迟时间为9.62秒.
- 评估证实ViDDAR能够理解复杂的场景并识别有害的虚拟内容.
结论:
- 在AR环境中,ViDDAR有效地检测到对任务有害的虚拟内容,解决了AR系统设计中的关键挑战.
- 该系统依赖于VLMs代表了AR内容监控和安全方面的重大进步.
- ViDDAR的架构平衡了检测准确性和可接受的延迟,适用于真实世界的AR应用程序.
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