An Experimental Study of Trojan Vulnerabilities in UAV Autonomous Landing
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909867059445760 |
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| author | Ahmari, Reza Mohammadi, Ahmad Hemmati, Vahid Mynuddin, Mohammed Mahmoud, Mahmoud Nabil Kebria, Parham Homaifar, Abdollah Saif, Mehrdad |
| author_facet | Ahmari, Reza Mohammadi, Ahmad Hemmati, Vahid Mynuddin, Mohammed Mahmoud, Mahmoud Nabil Kebria, Parham Homaifar, Abdollah Saif, Mehrdad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20932 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Experimental Study of Trojan Vulnerabilities in UAV Autonomous Landing Ahmari, Reza Mohammadi, Ahmad Hemmati, Vahid Mynuddin, Mohammed Mahmoud, Mahmoud Nabil Kebria, Parham Homaifar, Abdollah Saif, Mehrdad Cryptography and Security Artificial Intelligence Computer Vision and Pattern Recognition Robotics 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. |
| title | An Experimental Study of Trojan Vulnerabilities in UAV Autonomous Landing |
| topic | Cryptography and Security Artificial Intelligence Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2510.20932 |