Pattern Corruption-Assisted Physical Attacks Against Object Detection in UAV Remote Sensing

Zhang, Yu; Gong, Zhiqiang; Wen, Hao; Hu, Xikun; Xia, Xiaoyan; Jiang, Hejun; Zhong, Ping

doi:10.1109/jstars.2024.3422377

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

2024

DOI: 10.1109/jstars.2024.3422377

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Pattern Corruption-Assisted Physical Attacks Against Object Detection in UAV Remote Sensing

Yu Zhang,

Zhiqiang Gong,

Hao Wen

et al.

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Improving Transferability of Physical Adversarial Attacks on Object Detectors Through Multi-Model Optimization

Dimitriu,

Michaletzky,

Remeli

2024

Applied Sciences

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Physical adversarial attacks face significant challenges in achieving transferability across different object detection models, especially in real-world conditions. This is primarily due to variations in model architectures, training data, and detection strategies, which can make adversarial examples highly model-specific. This study introduces a multi-model adversarial training approach to improve the transferability of adversarial textures across diverse detection models, including one-stage, two-stage, and transformer-based architectures. Using the Truck Adversarial Camouflage Optimization (TACO) framework and a novel combination of YOLOv8n, YOLOv5m, and YOLOv3 models for optimization, our approach achieves an AP@0.5 detection score of 0.0972—over 50% lower than textures trained on single models alone. This result highlights the importance of multi-model training in enhancing attack effectiveness across object detectors, contributing to improved adversarial effectiveness.

show abstract

Improving Transferability of Physical Adversarial Attacks on Object Detectors Through Multi-Model Optimization

Dimitriu,

Michaletzky,

Remeli

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

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Pattern Corruption-Assisted Physical Attacks Against Object Detection in UAV Remote Sensing

Cited by 1 publication

References 42 publications

Improving Transferability of Physical Adversarial Attacks on Object Detectors Through Multi-Model Optimization

Improving Transferability of Physical Adversarial Attacks on Object Detectors Through Multi-Model Optimization

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