在增强现实中检测视觉信息操纵攻击:一种多模式语义推理方法
IEEE transactions on visualization and computer graphics
|October 14, 2025
概括
增强现实 (AR) 视觉信息操纵 (VIM) 攻击可以改变现实世界的场景. 一个新的多式联络框架VIM-Sense能够以88.94%的准确度检测到这些微妙的AR攻击.
科学领域:
- 计算机科学 计算机科学
- 人与计算机的交互
- 网络安全 网络安全
背景情况:
- 增强现实 (AR) 系统将虚拟内容叠加到现实世界的视图上.
- 虚拟内容可以被操纵,导致语义误解和用户错误.
- 视觉信息操纵 (VIM) 攻击在AR中微妙地改变了现实世界的场景含义.
研究的目的:
- 在AR中引入一个分类系统来对VIM攻击进行分类.
- 创建一个数据集 (AR-VIM) 来评估VIM攻击检测.
- 提出和评估一个多式联络框架来检测AR中的VIM攻击.
主要方法:
- 开发了VIM攻击的分类 (字符,短语,模式操纵;信息更换,模糊,额外的错误信息).
- 构建了AR-VIM数据集,包含202个场景中的452个AR视频对.
- 提出了VIM-Sense,这是一个多式模式框架,结合了视觉语言模型 (VLM) 和光学字符识别 (OCR).
主要成果:
- 在AR-VIM数据集上,VIM-Sense实现了88.94%的攻击检测准确度.
- 该框架的性能优于仅视觉和仅文本的基线方法.
- 在模拟中,检测延迟时间约为7.07秒,在移动AR应用中为7.17秒.
结论:
- 拟议的分类学有效地将VIM攻击归类为AR.
- 使用多式语义推理,VIM-Sense证明了对VIM攻击的有效检测.
- 该框架显示了现实世界AR应用的实际可行性.
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