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WAN Xuanshen, LIU Wei, NIU Chaoyang, et al. Black-box attack algorithm for SAR-ATR deep neural networks based on MI-FGSM[J]. Journal of Radars, in press. doi: 10.12000/JR23220
Citation: WAN Xuanshen, LIU Wei, NIU Chaoyang, et al. Black-box attack algorithm for SAR-ATR deep neural networks based on MI-FGSM[J]. Journal of Radars, in press. doi: 10.12000/JR23220
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Black-box Attack Algorithm for SAR-ATR Deep Neural Networks Based on MI-FGSM

doi: 10.12000/JR23220

  • PLA Strategic Support Force Information Engineering University, School of Data and Target Engineering, Zhengzhou 450000, China

Funds:  The National Natural Science Foundation of China (42201472)
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  • Corresponding author: LIU Wei, greatliuliu@163.com
  • Received Date: 2023-11-17
  • Rev Recd Date: 2024-01-14
    • Available Online: 2024-01-18
    Abstract
  • The field of Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR) lacks effective black-box attack algorithms. Therefore, this research proposes a migration-based black-box attack algorithm by combining the idea of the Momentum Iterative Fast Gradient Sign Method (MI-FGSM). First, random speckle noise transformation is performed according to the characteristics of SAR images to alleviate model overfitting to the speckle noise and improve the generalization performance of the algorithm. Second, an AdaBelief-Nesterov optimizer is designed to rapidly find the optimal gradient descent direction, and the attack effectiveness of the algorithm is improved through a rapid convergence of the model gradient. Finally, a quasihyperbolic momentum operator is introduced to obtain a stable model gradient descent direction so that the gradient can avoid falling into a local optimum during the rapid convergence and to further enhance the success rate of black-box attacks on adversarial examples. Simulation experiments show that compared with existing adversarial attack algorithms, the proposed algorithm improves the ensemble model black-box attack success rate of mainstream SAR-ATR deep neural networks by 3%～55% and 6%～57.5% on the MSTAR and FUSAR-Ship datasets, respectively; the generated adversarial examples are highly concealable.

     

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