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...
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| Principais autores: | , , , , , |
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| Format: | Artigo |
| Sprog: | Inglês |
| Udgivet: |
IEEE
2025-01-01
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| Serier: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Fag: | |
| Online adgang: | https://ieeexplore.ieee.org/document/11039638/ |
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