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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 2: AAAI-22 Technical Tracks 2

FCA: Learning a 3D Full-Coverage Vehicle Camouflage for Multi-View Physical Adversarial Attack

February 1, 2023

Authors

Donghua Wang

College of Computer Science and Technology, Zhejiang University


Tingsong Jiang

Defense Innovation Institute, Chinese Academy of Military Science


Jialiang Sun

Defense Innovation Institute, Chinese Academy of Military Science


Weien Zhou

Defense Innovation Institute, Chinese Academy of Military Science


Zhiqiang Gong

Defense Innovation Institute, Chinese Academy of Military Science


Xiaoya Zhang

Defense Innovation Institute, Chinese Academy of Military Science


Wen Yao

Defense Innovation Institute, Chinese Academy of Military Science


Xiaoqian Chen

Defense Innovation Institute, Chinese Academy of Military Science


Proceedings:

No. 2: AAAI-22 Technical Tracks 2

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision II

Downloads:

Download PDF

Abstract:

Physical adversarial attacks in object detection have attracted increasing attention. However, most previous works focus on hiding the objects from the detector by generating an individual adversarial patch, which only covers the planar part of the vehicle’s surface and fails to attack the detector in physical scenarios for multi-view, long-distance and partially occluded objects. To bridge the gap between digital attacks and physical attacks, we exploit the full 3D vehicle surface to propose a robust Full-coverage Camouflage Attack (FCA) to fool detectors. Specifically, we first try rendering the nonplanar camouflage texture over the full vehicle surface. To mimic the real-world environment conditions, we then introduce a transformation function to transfer the rendered camouflaged vehicle into a photo-realistic scenario. Finally, we design an efficient loss function to optimize the camouflage texture. Experiments show that the full-coverage camouflage attack can not only outperform state-of-the-art methods under various test cases but also generalize to different environments, vehicles, and object detectors.

DOI:

10.1609/aaai.v36i2.20141


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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