URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection

Hailong Xi, Le Ru (), Jiwei Tian, Bo Lu, Shiguang Hu, Wenfei Wang and Xiaohui Luan
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Hailong Xi: Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an 710043, China
Le Ru: Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an 710043, China
Jiwei Tian: Air Traffic Control and Navigation School, Air Force Engineering University, Xi’an 710043, China
Bo Lu: Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an 710043, China
Shiguang Hu: Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an 710043, China
Wenfei Wang: Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an 710043, China
Xiaohui Luan: China Academy of Space Technology (Xi’an), Xi’an 710000, China

Mathematics, 2025, vol. 13, issue 4, 1-22

Abstract: In recent years, deep learning has been extensively deployed on unmanned aerial vehicles (UAVs), particularly for object detection. As the cornerstone of UAV-based object detection, deep neural networks are susceptible to adversarial attacks, with adversarial patches being a relatively straightforward method to implement. However, current research on adversarial patches, especially those targeting UAV object detection, is limited. This scarcity is notable given the complex and dynamically changing environment inherent in UAV image acquisition, which necessitates the development of more robust adversarial patches to achieve effective attacks. To address the challenge of adversarial attacks in UAV high-altitude reconnaissance, this paper presents a robust adversarial patch generation framework. Firstly, the dataset is reconstructed by considering various environmental factors that UAVs may encounter during image collection, and the influences of reflections and shadows during photography are integrated into patch training. Additionally, a nested optimization method is employed to enhance the continuity of attacks across different altitudes. Experimental results demonstrate that the adversarial patches generated by the proposed method exhibit greater robustness in complex environments and have better transferability among similar models.

Keywords: unmanned aerial vehicles; object detection; adversarial attack; adversarial patch (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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