# An Experimental Study of Trojan Vulnerabilities in UAV Autonomous Landing

arXiv: https://arxiv.org/abs/2510.20932 · HTML: https://mlsanity.com/paper/2510.20932/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [A Survey of Neural Trojan Attacks and Defenses in Deep Learning](https://mlsanity.com/paper/2202.07183/) (2022)
- [Image Segmentation to Identify Safe Landing Zones for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2111.14557/) (2021)
- [MATTER: Multi-stage Adaptive Thermal Trojan for Efficiency & Resilience degradation](https://mlsanity.com/paper/2412.00226/) (2024)
- [Vision-based autonomous structural damage detection using data-driven methods](https://mlsanity.com/paper/2501.16662/) (2025)
- [GPS Spoofing Attack Detection in Autonomous Vehicles Using Adaptive DBSCAN](https://mlsanity.com/paper/2510.10766/) (2025)

## Built on by (most cited first, 3 of 3)

- [Visual Heading Prediction for Autonomous Aerial Vehicles](https://mlsanity.com/paper/2512.09898/) (2025)
- [Volatility in Certainty (VC): A Metric for Detecting Adversarial Perturbations During Inference in Neural Network Classifiers](https://mlsanity.com/paper/2511.11834/) (2025)
- [Confidence-Gated Vision-Only Heading Alignment for UAV-UGV Cooperative Systems](https://mlsanity.com/paper/2607.18713/) (2026)

## Abstract (arXiv)

This study investigates the vulnerabilities of autonomous navigation and landing systems in Urban Air Mobility (UAM) vehicles. Specifically, it focuses on Trojan attacks that target deep learning models, such as Convolutional Neural Networks (CNNs). Trojan attacks work by embedding covert triggers within a model's training data. These triggers cause specific failures under certain conditions, while the model continues to perform normally in other situations. We assessed the vulnerability of Urban Autonomous Aerial Vehicles (UAAVs) using the DroNet framework. Our experiments showed a significant drop in accuracy, from 96.4% on clean data to 73.3% on data triggered by Trojan attacks. To conduct this study, we collected a custom dataset and trained models to simulate real-world conditions. We also developed an evaluation framework designed to identify Trojan-infected models. This work demonstrates the potential security risks posed by Trojan attacks and lays the groundwork for future research on enhancing the resilience of UAM systems.