Adversarial Defense via Perturbation-Disentanglement in Hyperspectral Image Classification

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Adversarial Defense via Perturbation-Disentanglement in Hyperspectral Image Classification
Title:
Adversarial Defense via Perturbation-Disentanglement in Hyperspectral Image Classification
Journal Title:
2023 IEEE International Conference on Image Processing (ICIP)
Keywords:
Publication Date:
11 September 2023
Citation:
Shi, C., Liu, Y., Zhao, M., You, Z., & Zhao, Z. (2023). Adversarial Defense via Perturbation-Disentanglement in Hyperspectral Image Classification. 2023 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip49359.2023.10223057
Abstract:
In recent years, deep neural networks (DNNs) have been widely used in hyperspectral image (HSI) classification. However, it has a strong vulnerability to crafted adversarial examples. Therefore, defense against adversarial examples is an urgent problem to be solved. To date, most defense methods are difficult to defend against unknown attacks. In this paper, we propose a perturbation-disentanglement-based adversarial defense method (PD-Defense) to protect HSI classification networks from unknown attacks. In the proposed method, the adversarial examples are decoupled into attack-invariant features and perturbation features, and the defense is conducted on the attack-invariant feature to defend against unknown attacks. Extensive experiments are performed on two benchmark HSI datasets, including PaviaU and HoustonU 2018. The results indicate that the proposed PD-Defense method achieves an excellent defense performance compared to four state-of-the-art defense methods.
License type:
Publisher Copyright
Funding Info:
This work was supported by the National Natural Science Foundation of China (61902313, 62002272) and Young Talent Fund of Association for Science and Technology in Shaanxi, China.
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISBN:
978-1-7281-9835-4
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