Fast C&W: A Fast Adversarial Attack Algorithm to Fool SAR Target Recognition With Deep Convolutional Neural Networks

In recent years, deep convolutional neural networks (CNNs) pose superior synthetic aperture radar target recognition (SAR-TR) performance. However, CNN-based SAR classifiers would be vulnerable to adversarial attack (AA) when strong nonlinearity of CNN is contrapuntally utilized by AA. The AA can cause a CNN classifier to produce erroneous predictions with extremely high confidence by injecting a tiny adversarial perturbation to the input SAR images. In this letter, an accelerated SAR-TR AA algorithm is proposed named Fast C&W. We introduce a well-trained deep encoder network to replace the process of searching for the optimal perturbation of the input SAR image iteratively in the vanilla C&W algorithm. In this way, an adversarial perturbation can be generated much faster through the rapid forward mapping during an attack. Meanwhile, as a feature extraction network, the encoder network can learn the separable data region by optimizing the attack loss function. Through the encoder network, the added perturbation energy can be mainly concentrated on a region of target instead of background clutter area. This property would be of advantages in the perturbation location control in an SAR image. In the experiments, we use the proposed AA algorithm to interfere with the deep CNN-based high-accuracy SAR-TR model trained on the moving and stationary target acquisition and recognition (MSTAR) data set, which demonstrates its excellent effectiveness and thousands of times of efficiency improvement.

Automated summary

In this letter, the Fast C&W algorithm is proposed as an accelerated SAR-TR AA algorithm, which replaces the iterative search process with a well-trained deep encoder network for faster generation of adversarial perturbations. The encoder network acts as a feature extraction network, concentrating perturbation energy on the target region and demonstrating effectiveness and efficiency improvement in experiments.