Volume 13 Issue 6
Dec.  2024
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RUAN Hang, CUI Jiahao, MAO Xiuhua, et al. A survey of adversarial attacks on SAR target recognition: From digital domain to physical domain[J]. Journal of Radars, 2024, 13(6): 1298–1326. doi: 10.12000/JR24142
Citation: RUAN Hang, CUI Jiahao, MAO Xiuhua, et al. A survey of adversarial attacks on SAR target recognition: From digital domain to physical domain[J]. Journal of Radars, 2024, 13(6): 1298–1326. doi: 10.12000/JR24142
shu

A Survey of Adversarial Attacks on SAR Target Recognition: From Digital Domain to Physical Domain

DOI: 10.12000/JR24142
  • 1.

    Beijing Institute of Tracking and Communication Technology, Beijing 100094, China

  • 2.

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

  • 3.

    Science & Technology on Integrated Information System Laboratory, Beijing 100094, China

Funds:  The National Natural Science Foundation of China (42171458, 42271481)
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    Abstract
  • Deep Neural Network (DNN)-based Synthetic Aperture Radar (SAR) image target recognition has become a prominent area of interest in SAR applications. However, deep neural network models are vulnerable to adversarial example attacks. Adversarial examples are input samples that introduce minute perturbations within the dataset, causing the model to make highly confident yet incorrect judgments. Existing generation techniques of SAR adversarial examples fundamentally operate on two-dimensional images, which are classified as digital-domain adversarial examples. Although recent research has started to incorporate SAR imaging scattering mechanisms in adversarial example generation, two important flaws still remain: (1) imaging scattering mechanisms are only applied to SAR images without being integrated into the actual SAR imaging process, and (2) the mechanisms achieve only pseudo-physical-domain adversarial attacks, failing to realize true three-dimensional physical-domain adversarial attacks. This study investigates the current state and development trends in adversarial attacks on SAR intelligent target recognition. First, the development trajectory of traditional generation technologies of SAR-image adversarial examples is meticulously traced and a comparative analysis of various technologies is conducted, thus summarizing their deficiencies. Building on the principles and actual processes of SAR imaging, physical-domain adversarial attack techniques are then proposed. These techniques manipulate the target object’s backscattering properties or emit finely adjustable interference signals in amplitude and phase to counter SAR intelligent target recognition algorithms. The paper also envisions practical implementations of SAR adversarial attacks in the physical domain. Finally, this paper concludes by discussing the future directions of SAR intelligent adversarial attack technologies.

     

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