Adversarial example generation method for SAR images based on mask extraction

ZHANG Jianwu; NAI Hao; LI Jie; QIAN Jianhua; FANG Yinfeng

doi:10.11959/j.issn.1000-0801.2024081

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Adversarial example generation method for SAR images based on mask extraction

Research and Development | 更新时间：2024-06-05

- Adversarial example generation method for SAR images based on mask extraction
- Telecommunications Science Vol. 40, Issue 3, Pages: 64-74(2024)
- 作者机构：
  
  1.杭州电子科技大学，浙江杭州 310018
  2.中国联通（浙江）产业互联网有限公司，浙江杭州 311199
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China (No.IEC\NSFC\181300), The Natural Science Foundation of Zhejiang Province
- DOI：10.11959/j.issn.1000-0801.2024081
  CLC： TN957
- Received：30 September 2023，
  
  Revised：10 January 2024，
  
  Published：20 March 2024
- 稿件说明：
移动端阅览
章坚武,能豪,李杰等.基于掩模提取的SAR图像对抗样本生成方法[J].电信科学,2024,40(03):64-74.

ZHANG Jianwu,NAI Hao,LI Jie,et al.Adversarial example generation method for SAR images based on mask extraction[J].Telecommunications Science,2024,40(03):64-74.
章坚武,能豪,李杰等.基于掩模提取的SAR图像对抗样本生成方法[J].电信科学,2024,40(03):64-74. DOI： 10.11959/j.issn.1000-0801.2024081.

ZHANG Jianwu,NAI Hao,LI Jie,et al.Adversarial example generation method for SAR images based on mask extraction[J].Telecommunications Science,2024,40(03):64-74. DOI： 10.11959/j.issn.1000-0801.2024081.

摘要

合成孔径雷达（synthetic aperture radar，SAR）图像的对抗样本生成在当前已经有很多方法，但仍存在对抗样本扰动量较大、训练不稳定以及对抗样本的质量无法保证等问题。针对上述问题，提出了一种SAR图像对抗样本生成模型，该模型基于AdvGAN模型架构，首先根据SAR图像的特点设计了一种由增强Lee滤波器和最大类间方差法（OTSU）自适应阈值分割等模块组成的掩模提取模块，这种方法产生的扰动量更小，与原始样本的结构相似性（structural similarity，SSIM）值达到0.997以上。其次将改进的相对均值生成对抗网络（relativistic average generative adversarial network，RaGAN）损失引入AdvGAN中，使用相对均值判别器，让判别器在训练中同时依赖于真实数据和生成的数据，提高了训练的稳定性与攻击效果。在MSTAR数据集上与相关方法进行了实验对比，实验表明，此方法生成的SAR图像对抗样本在攻击防御模型时的攻击成功率较传统方法提高了10%～15%。

Abstract

There are many ways to generate adversarial samples for synthetic aperture radar (SAR) images at present

but some problems such as large amount of perturbation of adversarial samples

unstable training

and unguaranteed quality of adversarial samples still exist. To solve the above problems

a SAR image adversarial sample generation model was proposed. The model was based on the AdvGAN model architecture. Firstly

according to the characteristics of the SAR images

an adaptive threshold segmentation method based on the enhanced Lee filter OTSU was designed. The mask extraction module composed of equal modules

this method produced a smaller amount of disturbance

and the structural similarity (SSIM) with the original sample reached that more than 0.997. Secondly

the improved relativistic average GAN (RaGAN) loss was introduced into AdvGAN

and the relative mean discriminator was used to make the discriminator rely on both real data and generated data during training

which improved the stability of training and the attack effect. Experiments were compared with related methods on the MSTAR dataset. Experiments show that the attack success rate of SAR image adversarial samples generated by this method is increased by 10%～15% than that of traditional methods when attacking defense models.

关键词

Keywords

references

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