Multi-view robust adversarial attack: a method based on channel attention weighted feature similarity constraint

Yongqian Li; Yu Zhang; Yichuang Zhang; Jiahao Qi; Ping Zhong

doi:10.1117/12.3012493

21 December 2023 Multi-view robust adversarial attack: a method based on channel attention weighted feature similarity constraint

Yongqian Li, Yu Zhang, Yichuang Zhang, Jiahao Qi, Ping Zhong

Author Affiliations +

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129703B (2023) https://doi.org/10.1117/12.3012493
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Although deep learning has developed rapidly in recent years, the existence of adversarial examples casts a shadow over its future. Deep neural networks (DNNs) can be unreliable when facing these carefully crafted adversarial examples, limiting their real-world applications. While adversarial examples usually lack robustness and tend to lose effect when the viewing angle is changed. Moreover, it is impossible to control the angle at which adversarial examples are seen by the airborne detector. Therefore, we propose a multi-view robust adversarial attack by introducing perspective transformation and feature similarity loss to mitigate the negative effect that viewing angle changes bring to the features. We evaluated our adversarial patch on our multi-view test dataset and the results show that our proposed method reduced the mAP by 73.4%. Furthermore, compared to other methods, our approach performs best in the data with low depression angles, demonstrating its excellent multi-view robustness.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yongqian Li, Yu Zhang, Yichuang Zhang, Jiahao Qi, and Ping Zhong "Multi-view robust adversarial attack: a method based on channel attention weighted feature similarity constraint", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129703B (21 December 2023); https://doi.org/10.1117/12.3012493

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