# A Neurosymbolic Framework for Interpretable Cognitive Attack Detection in Augmented Reality

arXiv: https://arxiv.org/abs/2508.09185 · HTML: https://mlsanity.com/paper/2508.09185/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 20 of 20)

- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Interaction Networks for Learning about Objects, Relations and Physics](https://mlsanity.com/paper/1612.00222/) (2016)
- [LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action](https://mlsanity.com/paper/2207.04429/) (2022)
- [AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models](https://mlsanity.com/paper/2308.15366/) (2023)
- [A Comprehensive Survey of Scene Graphs: Generation and Application](https://mlsanity.com/paper/2104.01111/) (2021)
- [ObjectFormer for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2203.14681/) (2022)
- [Confusing Image Quality Assessment: Towards Better Augmented Reality Experience](https://mlsanity.com/paper/2204.04900/) (2022)
- [Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey](https://mlsanity.com/paper/2410.23687/) (2024)
- [Neurosymbolic AI for Reasoning over Knowledge Graphs: A Survey](https://mlsanity.com/paper/2302.07200/) (2023)
- [A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial Attacks](https://mlsanity.com/paper/2310.00633/) (2023)
- [ViDDAR: Vision Language Model-Based Task-Detrimental Content Detection for Augmented Reality](https://mlsanity.com/paper/2501.12553/) (2025)
- [MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation](https://mlsanity.com/paper/2406.18815/) (2024)
- [Network Diffuser for Placing-Scheduling Service Function Chains with Inverse Demonstration](https://mlsanity.com/paper/2501.05673/) (2025)
- [Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee](https://mlsanity.com/paper/2405.15245/) (2024)
- [Detecting Visual Information Manipulation Attacks in Augmented Reality: A Multimodal Semantic Reasoning Approach](https://mlsanity.com/paper/2507.20356/) (2025)
- [Multimodal Attack Detection for Action Recognition Models](https://mlsanity.com/paper/2404.10790/) (2024)
- [SoK: Come Together -- Unifying Security, Information Theory, and Cognition for a Mixed Reality Deception Attack Ontology & Analysis Framework](https://mlsanity.com/paper/2502.09763/) (2025)

## Built on by (most cited first, 8 of 8)

- [Advances in Global Solvers for 3D Vision](https://mlsanity.com/paper/2602.14662/) (2026)
- [Agent Alpha: Tree Search Unifying Generation, Exploration and Evaluation for Computer-Use Agents](https://mlsanity.com/paper/2602.02995/) (2026)
- [InSPO: Unlocking Intrinsic Self-Reflection for LLM Preference Optimization](https://mlsanity.com/paper/2512.23126/) (2025)
- [Perception Graph for Cognitive Attack Reasoning in Augmented Reality](https://mlsanity.com/paper/2509.05324/) (2025)
- [MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation](https://mlsanity.com/paper/2602.05048/) (2026)
- [ACDZero: MCTS Agent for Mastering Automated Cyber Defense](https://mlsanity.com/paper/2601.02196/) (2026)
- [Reasoning Knowledge-Gap in Drone Planning via LLM-based Active Elicitation](https://mlsanity.com/paper/2603.07824/) (2026)
- [Benchmarking Vision-Language Models under Contradictory Virtual Content Attacks in Augmented Reality](https://mlsanity.com/paper/2604.05510/) (2026)

## Abstract (arXiv)

Augmented Reality (AR) enriches human perception by overlaying virtual elements onto the physical world. However, this tight coupling between virtual and real content makes AR vulnerable to cognitive attacks: manipulations that distort users' semantic understanding of the environment. Existing detection methods largely focus on visual inconsistencies at the pixel or image level, offering limited semantic reasoning or interpretability. To address these limitations, we introduce CADAR, a neuro-symbolic framework for cognitive attack detection in AR that integrates neural and symbolic reasoning. CADAR fuses multimodal vision-language representations from pre-trained models into a perception graph that captures objects, relations, and temporal contextual salience. Building on this structure, a particle-filter-based statistical reasoning module infers anomalies in semantic dynamics to reveal cognitive attacks. This combination provides both the adaptability of modern vision-language models and the interpretability of probabilistic symbolic reasoning. Preliminary experiments on an AR cognitive-attack dataset demonstrate consistent advantages over existing approaches, highlighting the potential of neuro-symbolic methods for robust and interpretable AR security.