构建感知图模型，量化增强现实中的认知攻击影响。
Perception Graph for Cognitive Attack Reasoning in Augmented Reality
- 用语义结构模拟人类对混合现实环境的感知过程
- 可计算感知扭曲程度的定量评分
- 适用于军事级AR系统安全评估
增强现实（AR）系统在战术环境中日益普及，但其对无缝人机交互的依赖使其易受认知攻击影响，此类攻击会操纵用户感知并严重损害决策能力。为应对这一挑战，我们提出感知图（Perception Graph），一种新型模型，用于推理此类系统中的人类感知。该模型首先模仿人类从混合现实（MR）环境提取关键信息的过程，再以语义有意义的结构表示结果。我们展示了该模型如何计算反映感知扭曲程度的定量分数，提供了一种稳健且可量化的认知攻击检测与分析方法。
原文摘要 · Abstract (English)
Augmented reality (AR) systems are increasingly deployed in tactical environments, but their reliance on seamless human-computer interaction makes them vulnerable to cognitive attacks that manipulate a user's perception and severely compromise user decision-making. To address this challenge, we introduce the Perception Graph, a novel model designed to reason about human perception within these systems. Our model operates by first mimicking the human process of interpreting key information from an MR environment and then representing the outcomes using a semantically meaningful structure. We demonstrate how the model can compute a quantitative score that reflects the level of perception distortion, providing a robust and measurable method for detecting and analyzing the effects of such cognitive attacks.
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。