Enhancing SAR-ATR Systems’ Resistance to S2M Attacks via FUA: Optimizing Surrogate Models for Adversarial Example Transferability

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, wh...

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Bibliographic Details
Main Authors: Xiaying Jin, Shuangju Zhou, Chenyu Wang, Mingxin Fu, Quan Pan, Yang Li
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Adversarial attack
black box attack
synthetic aperture radar (SAR)
transfer attack
Online Access:https://ieeexplore.ieee.org/document/11039638/
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