CMDN: Pre-Trained Visual Representations Boost Adversarial Robustness for UAV Tracking
Visual object tracking is widely adopted to unmanned aerial vehicle (UAV)-related applications, which demand reliable tracking precision and real-time performance. However, UAV trackers are highly susceptible to adversarial attacks, while research on developing effective adversarial defense methods...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/11/607 |
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| author | Ruilong Yu Zhewei Wu Qihe Liu Shijie Zhou Min Gou Bingchen Xiang |
| author_facet | Ruilong Yu Zhewei Wu Qihe Liu Shijie Zhou Min Gou Bingchen Xiang |
| author_sort | Ruilong Yu |
| collection | DOAJ |
| description | Visual object tracking is widely adopted to unmanned aerial vehicle (UAV)-related applications, which demand reliable tracking precision and real-time performance. However, UAV trackers are highly susceptible to adversarial attacks, while research on developing effective adversarial defense methods for UAV tracking remains limited. To tackle these challenges, we propose CMDN, a novel pre-processing defense network that effectively purifies adversarial perturbations by reconstructing video frames. This network learns robust visual representations from video frames, guided by meaningful features from both the search region and the template. Comprehensive experiments on three benchmarks demonstrate that CMDN is capable of enhancing a UAV tracker’s adversarial robustness in both adaptive and non-adaptive attack scenarios. In addition, CMDN maintains stable defense effectiveness when transferred to heterogeneous trackers. Real-world tests on the UAV platform also validate its reliable defense effectiveness and real-time performance, with CMDN achieving 27 FPS on NVIDIA Jetson Orin 16 GB (25 W mode). |
| format | Article |
| id | doaj-art-56f3d39bfad2430f9c4a7ac9f68ff07f |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-56f3d39bfad2430f9c4a7ac9f68ff07f2025-08-20T02:28:09ZengMDPI AGDrones2504-446X2024-10-0181160710.3390/drones8110607CMDN: Pre-Trained Visual Representations Boost Adversarial Robustness for UAV TrackingRuilong Yu0Zhewei Wu1Qihe Liu2Shijie Zhou3Min Gou4Bingchen Xiang5School of Information and Software Engineering, University of Electronic Science and Technology of China, No. 4, Section 2 Jianshebei Road, Chengdu 610000, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, No. 4, Section 2 Jianshebei Road, Chengdu 610000, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, No. 4, Section 2 Jianshebei Road, Chengdu 610000, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, No. 4, Section 2 Jianshebei Road, Chengdu 610000, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, No. 4, Section 2 Jianshebei Road, Chengdu 610000, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, No. 4, Section 2 Jianshebei Road, Chengdu 610000, ChinaVisual object tracking is widely adopted to unmanned aerial vehicle (UAV)-related applications, which demand reliable tracking precision and real-time performance. However, UAV trackers are highly susceptible to adversarial attacks, while research on developing effective adversarial defense methods for UAV tracking remains limited. To tackle these challenges, we propose CMDN, a novel pre-processing defense network that effectively purifies adversarial perturbations by reconstructing video frames. This network learns robust visual representations from video frames, guided by meaningful features from both the search region and the template. Comprehensive experiments on three benchmarks demonstrate that CMDN is capable of enhancing a UAV tracker’s adversarial robustness in both adaptive and non-adaptive attack scenarios. In addition, CMDN maintains stable defense effectiveness when transferred to heterogeneous trackers. Real-world tests on the UAV platform also validate its reliable defense effectiveness and real-time performance, with CMDN achieving 27 FPS on NVIDIA Jetson Orin 16 GB (25 W mode).https://www.mdpi.com/2504-446X/8/11/607unmanned aerial vehicleadversarial defensevisual object tracking |
| spellingShingle | Ruilong Yu Zhewei Wu Qihe Liu Shijie Zhou Min Gou Bingchen Xiang CMDN: Pre-Trained Visual Representations Boost Adversarial Robustness for UAV Tracking Drones unmanned aerial vehicle adversarial defense visual object tracking |
| title | CMDN: Pre-Trained Visual Representations Boost Adversarial Robustness for UAV Tracking |
| title_full | CMDN: Pre-Trained Visual Representations Boost Adversarial Robustness for UAV Tracking |
| title_fullStr | CMDN: Pre-Trained Visual Representations Boost Adversarial Robustness for UAV Tracking |
| title_full_unstemmed | CMDN: Pre-Trained Visual Representations Boost Adversarial Robustness for UAV Tracking |
| title_short | CMDN: Pre-Trained Visual Representations Boost Adversarial Robustness for UAV Tracking |
| title_sort | cmdn pre trained visual representations boost adversarial robustness for uav tracking |
| topic | unmanned aerial vehicle adversarial defense visual object tracking |
| url | https://www.mdpi.com/2504-446X/8/11/607 |
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