URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection
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 straightforwa...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/4/591 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849719784933949440 |
|---|---|
| author | Hailong Xi Le Ru Jiwei Tian Bo Lu Shiguang Hu Wenfei Wang Xiaohui Luan |
| author_facet | Hailong Xi Le Ru Jiwei Tian Bo Lu Shiguang Hu Wenfei Wang Xiaohui Luan |
| author_sort | Hailong Xi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-67564dd1de9647f2a5679ec3e8fdd50d |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-67564dd1de9647f2a5679ec3e8fdd50d2025-08-20T03:12:05ZengMDPI AGMathematics2227-73902025-02-0113459110.3390/math13040591URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object DetectionHailong Xi0Le Ru1Jiwei Tian2Bo Lu3Shiguang Hu4Wenfei Wang5Xiaohui Luan6Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an 710043, ChinaEquipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an 710043, ChinaAir Traffic Control and Navigation School, Air Force Engineering University, Xi’an 710043, ChinaEquipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an 710043, ChinaEquipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an 710043, ChinaEquipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an 710043, ChinaChina Academy of Space Technology (Xi’an), Xi’an 710000, ChinaIn 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.https://www.mdpi.com/2227-7390/13/4/591unmanned aerial vehiclesobject detectionadversarial attackadversarial patch |
| spellingShingle | Hailong Xi Le Ru Jiwei Tian Bo Lu Shiguang Hu Wenfei Wang Xiaohui Luan URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection Mathematics unmanned aerial vehicles object detection adversarial attack adversarial patch |
| title | URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection |
| title_full | URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection |
| title_fullStr | URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection |
| title_full_unstemmed | URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection |
| title_short | URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection |
| title_sort | uradv a novel framework for generating ultra robust adversarial patches against uav object detection |
| topic | unmanned aerial vehicles object detection adversarial attack adversarial patch |
| url | https://www.mdpi.com/2227-7390/13/4/591 |
| work_keys_str_mv | AT hailongxi uradvanovelframeworkforgeneratingultrarobustadversarialpatchesagainstuavobjectdetection AT leru uradvanovelframeworkforgeneratingultrarobustadversarialpatchesagainstuavobjectdetection AT jiweitian uradvanovelframeworkforgeneratingultrarobustadversarialpatchesagainstuavobjectdetection AT bolu uradvanovelframeworkforgeneratingultrarobustadversarialpatchesagainstuavobjectdetection AT shiguanghu uradvanovelframeworkforgeneratingultrarobustadversarialpatchesagainstuavobjectdetection AT wenfeiwang uradvanovelframeworkforgeneratingultrarobustadversarialpatchesagainstuavobjectdetection AT xiaohuiluan uradvanovelframeworkforgeneratingultrarobustadversarialpatchesagainstuavobjectdetection |