A Neurosymbolic Framework for Interpretable Cognitive Attack Detection in Augmented Reality
This addresses the need for interpretable and effective detection of cognitive attacks in AR, which is important for AR users and developers, but it is incremental as it builds on existing neurosymbolic and VLM methods.
The paper tackles the problem of detecting cognitive attacks in augmented reality (AR) that manipulate users' semantic perception, and it presents CADAR, a neurosymbolic approach that improves accuracy by up to 10.7% over baselines on challenging AR attack scenarios.
Augmented Reality (AR) enriches perception by overlaying virtual elements on the physical world. Due to its growing popularity, cognitive attacks that alter AR content to manipulate users' semantic perception have received increasing attention. Existing detection methods often focus on visual changes, which are restricted to pixel- or image-level processing and lack semantic reasoning capabilities, or they rely on pre-trained vision-language models (VLMs), which function as black-box approaches with limited interpretability. In this paper, we present CADAR, a novel neurosymbolic approach for cognitive attack detection in AR. It fuses multimodal vision-language inputs using neural VLMs to obtain a symbolic perception-graph representation, incorporating prior knowledge, salience weighting, and temporal correlations. The model then enables particle-filter based statistical reasoning -- a sequential Monte Carlo method -- to detect cognitive attacks. Thus, CADAR inherits the adaptability of pre-trained VLM and the interpretability and reasoning rigor of particle filtering. Experiments on an extended AR cognitive attack dataset show accuracy improvements of up to 10.7% over strong baselines on challenging AR attack scenarios, underscoring the promise of neurosymbolic methods for effective and interpretable cognitive attack detection.