基于频域多目标优化的SAR图像对抗样本生成方法

刘洁怡; 李明哲; 杨曜铭; 李豪; 周宇; 党可林

doi:10.12263/DZXB.20250095

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基于频域多目标优化的SAR图像对抗样本生成方法

学术论文 | 更新时间：2025-10-16

- 基于频域多目标优化的SAR图像对抗样本生成方法
- A Multi-Objective Optimization Method in the Frequency Domain for SAR Image Adversarial Sample Generation
- 电子学报 2025年53卷第6期页码：1958-1968
- 作者机构：
  
  西安电子科技大学电子工程学院，陕西西安 710071
- 作者简介：
  
  [ "刘洁怡女，1991年2月出生于陕西省西安市.现为西安电子科技大学电子工程学院副教授.主要研究方向为智能信号处理、多站雷达系统干扰对抗技术.E-mail: jieyiliu@xidian.edu.cn" ]
  [ "李明哲男，2004年5月出生于黑龙江省齐齐哈尔市.现为西安电子科技大学电子工程学院本科生.主要研究方向为深度学习、对抗样本生成.E-mail: mingzheli@stu.xidian.edu.cn" ]
  [ "杨曜铭男，2001年4月出生于海南省海口市.现为西安电子科技大学硕士研究生.主要研究方向为深度学习、对抗攻击.Email: yangyaoming@stu.xidian.edu.cn" ]
  [ "李豪男，1990年12月出生于河南省洛阳市.现为西安电子科技大学电子工程学院副教授.主要研究方向为计算智能、深度学习.中国电子学会会员编号：E190022928M.E-mail: haoli@xidian.edu.cn" ]
  [ "周宇男，1983年8月出生于湖南省常德市.现为西安电子科技大学电子工程学院教授.主要研究方向为智能系统（集群）的自主感知和决策以及协同控制和优化.E-mail: zhouyu@xidian.edu.cn" ]
  [ "党可林男，2000年8月出生于陕西省渭南市.现为西安电子科技大学博士研究生.主要研究方向为深度神经网络、对抗性攻击和遥感图像处理.E-mail: 24021110902@stu.xidian.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62106185)
- DOI：10.12263/DZXB.20250095
  中图分类号： TN974;
- 收稿：2025-02-06，
  
  修回：2025-05-29，
  
  纸质出版：2025-06-25
- 稿件说明：
移动端阅览
刘洁怡, 李明哲, 杨曜铭, 等. 基于频域多目标优化的SAR图像对抗样本生成方法[J]. 电子学报, 2025, 53(06): 1958-1968.

LIU Jie-yi, LI Ming-zhe, YANG Yao-ming, et al. A Multi-Objective Optimization Method in the Frequency Domain for SAR Image Adversarial Sample Generation[J]. Acta Electronica Sinica, 2025, 53(06): 1958-1968.
刘洁怡, 李明哲, 杨曜铭, 等. 基于频域多目标优化的SAR图像对抗样本生成方法[J]. 电子学报, 2025, 53(06): 1958-1968. DOI：10.12263/DZXB.20250095

LIU Jie-yi, LI Ming-zhe, YANG Yao-ming, et al. A Multi-Objective Optimization Method in the Frequency Domain for SAR Image Adversarial Sample Generation[J]. Acta Electronica Sinica, 2025, 53(06): 1958-1968. DOI：10.12263/DZXB.20250095

摘要

基于深度学习的合成孔径雷达（Synthetic Aperture Radar，SAR）目标识别方法在军事侦察、灾害监测等领域应用广泛，然而深度神经网络易受到对抗攻击的威胁，导致模型决策的可靠性下降.现有黑盒对抗攻击方法在SAR图像对抗样本生成过程中面临参数设计维度高、易被察觉等问题.针对以上问题，提出一种基于频域多目标优化的对抗攻击方法，通过二维离散傅里叶变换将SAR图像从空间域映射至频域，降低扰动设计维度，进而在频域中修改单一频率分量，以生成图像域纹理状扰动.同时，结合基于超体积度量的多目标进化算法平衡对抗样本的攻击性能与视觉隐蔽性.实验结果表明，以T62类别为例，运用本文方法后，在VGG16、AConvNet和YOLO系列模型架构上，对抗样本分别实现了90.39%、71.43%、44.28%以上的置信度错误分类.同时，生成的对抗样本与原始图像的相似度均高于99%，为SAR图像的安全性与鲁棒性测试提供了有效的技术支持.

Abstract

Deep learning-based synthetic aperture radar (SAR) target recognition methods are widely used in military reconnaissance and disaster monitoring. However

deep neural networks (DNNs) are vulnerable to adversarial attacks

which compromise the reliability of model decisions. Existing black-box adversarial attack methods for SAR images face challenges such as high-dimensional parameter design and perceptible perturbations. To address these issues

a frequency-domain multi-objective optimization-based adversarial attack method is proposed. By transforming SAR images from the spatial domain to the frequency domain via 2D Discrete Fourier Transform

the method reduces perturbation design complexity and modifies a single frequency component to generate texture-like perturbations in the spatial domain. A hypervolume metric-guided multi-objective evolutionary algorithm is integrated to balance attack performance and visual stealthiness. Experimental results demonstrate that

for the T62 category

the adversarial samples generated by our method achieve misclassification confidence rates of more than 90.39%

71.43%

44.28% on VGG16

AConvNet

and YOLO series models

respectively. Additionally

the similarity between adversarial and original images exceeds 99% across all cases

providing effective technical support for security and robustness evaluation of SAR imaging systems.

关键词

Keywords

references

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