Fast sparse adversarial attack for synthetic aperture radar target recognition

With the rapid development of artificial intelligence technology, deep learning has achieved significant advantages in synthetic aperture radar automatic target recognition (SAR-ATR). However, previous research showed that the addition of small perturbations not easily detected by the human eye can lead to SAR-ATR model recognition errors; that is, they are affected by adversarial attacks. To solve the problem of long computation time in existing SAR sparse adversarial attack algorithms, we propose a SAR fast sparse adversarial attack (FSAA) algorithm. First, an end-to-end sparse adversarial attack framework is developed based on the lightweight generator ResNet model using two different upsampling modules to control the amplitude and position of the adversarial perturbation. A loss function for the generator is then constructed, which mainly consists of the linear addition of the attack loss, the amplitude distortion loss, and the sparsity loss. Finally, the SAR image is mapped through the trained generator model in a one-step process to generate sparse adversarial perturbations quickly and effectively. Compared with the existing SAR sparse adversarial attack algorithm, the experimental results show that the generation speed of the proposed method is at least 30 times higher when the perturbation is less than 0.05% of the pixels in the entire image, and the recognition rate of the model is >13%.