Perception Graph for Cognitive Attack Reasoning in Augmented Reality
This addresses security risks in tactical AR environments by providing a measurable method for attack detection, though it appears incremental as it builds on existing reasoning models.
The paper tackles the vulnerability of augmented reality systems to cognitive attacks that manipulate user perception by introducing the Perception Graph model, which computes a quantitative score to detect and analyze perception distortion.
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.