Attributed Scattering Center Guided Adversarial Attack for DCNN SAR Target Recognition

Recently, deep learning has made significant progress in synthetic aperture radar automatic target recognition (SAR ATR). However, deep convolutional neural networks (DCNNs) are discovered to be susceptible to carefully crafted adversarial perturbations. Regarding the unique scattering mechanism in SAR imaging, the scattering feature such as attributed scattering centers (ASCs) should be deeply considered in the adversarial attack (AA) algorithms for DCNNs in SAR ATR. In this letter, an AA algorithm named ASC-STA is proposed to take advantage of the powerful SAR imaging property characterization capability of the ASC model. Considering the imaging characteristic that is presented in the ASC model, the spatial transformation module is specially designed to be performed on the strong backscattering structures guided by the reconstruction image of the ASC model. Spatial transformation shifts the robust scattering point features via altering the locally gray-scale relationships of the target microstructures in SAR images. Besides, a modified shape context evaluated metric is established to assess the validity of the feature alternation process. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) database demonstrate that the proposed method achieves a high success rate without complex parameter settings and generates the perturbed image in high image quality. Compared with the norm-based AA algorithms, which are not involved with ASCs, the point feature of the original image is efficiently altered. At the same time, the perturbations are focused on the target regions accurately.