Enhancing SAR-ATR Systems’ Resistance to S2M Attacks via FUA: Optimizing Surrogate Models for Adversarial Example Transferability
The vulnerability of synthetic aperture radar (SAR)—automatic target recognition (ATR) models based on deep neural networks has garnered increasing attention in recent research. A novel and extreme prior-knowledge-limited attack scenario, synthetic-to-measured (S2M), has been proposed, where...
Gorde:
| Egile Nagusiak: | , , , , , |
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| Formatua: | Artigo |
| Hizkuntza: | Inglês |
| Argitaratua: |
IEEE
2025-01-01
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| Saila: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Gaiak: | |
| Sarrera elektronikoa: | https://ieeexplore.ieee.org/document/11039638/ |
| Etiketak: |
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