Perception Graph for Cognitive Attack Reasoning in Augmented Reality
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908521965027328 |
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| author | Chen, Rongqian Hong, Shu Islam, Rifatul Imani, Mahdi Tan, G. Gary Lan, Tian |
| author_facet | Chen, Rongqian Hong, Shu Islam, Rifatul Imani, Mahdi Tan, G. Gary Lan, Tian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05324 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Perception Graph for Cognitive Attack Reasoning in Augmented Reality Chen, Rongqian Hong, Shu Islam, Rifatul Imani, Mahdi Tan, G. Gary Lan, Tian Artificial Intelligence 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. |
| title | Perception Graph for Cognitive Attack Reasoning in Augmented Reality |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.05324 |