Black-box Attack Algorithm for SAR-ATR Deep Neural Networks Based on MI-FGSM
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 6.0%～57.5%on the MSTAR and FUSAR-Ship datasets,respectively;the generated adversarial examples are highly concealable.
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维普
