A Physically Realizable Adversarial Attack Method Against SAR Target Recognition Model
| Autor: | Fan Zhang, Yameng Yu, Fei Ma, Yongsheng Zhou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 11943-11957 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1939-1404 2151-1535 |
| DOI: | 10.1109/JSTARS.2024.3420690 |
| Popis: | Deep learning has shown remarkable proficiency in tasks related to synthetic aperture radar (SAR) interpretation. However, several studies have highlighted the inherent vulnerability of deep neural networks when faced with deliberately constructed adversarial examples (AEs). Current SAR adversarial attack research only focuses on generating AEs in the image domain, without considering SAR imaging systems. This approach can lead to two issues. First, the generated SAR AEs lack visual coherence. Second, they cannot be obtained as corresponding attack images through real SAR imaging systems. In this article, we propose a physically realizable SAR adversarial attack method, which includes two submodules based on optimization methods: Target perturbation generation and background perturbation generation. The former module utilizes attention mechanisms to extract the target regions from SAR images, followed by selecting the optimal small regions for perturbation placement. The latter module, on the other hand, utilizes scattering models to generate realistic scatterer images as perturbations, which are then optimized to identify the optimal position in the background regions. The individual attack performance of these two attack modules on five well-established SAR automatic target recognition models is demonstrated to be highly effective. Moreover, the combination of these attack modules achieves a fooling rate of approximately 90% and demonstrates superior adversarial transferability. The experimental results of this study provide an effective foundation for physical realization of SAR AEs. |
| Databáze: | Directory of Open Access Journals |
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