Template-based universal adversarial attack for synthetic aperture radar automatic target recognition network

Abstract

Existing synthetic aperture radar (SAR) adversarial attack algorithms primarily focus on the digital image domain, and constructing adversarial examples in real-world scenarios presents significant and challenging hurdles. This study proposes the template-based universal adversarial attack (TUAA) algorithm. Initially, a SAR interference template generator module is constructed to derive a universal adversarial perturbation in the image domain. The designed loss function guides the parameter updating of the generator, thereby improving the attack effectiveness and perturbation concealment. Subsequently, a SAR jamming signal generator module is developed, which swiftly generates the interference signal using the range convolutional and azimuth multiplication modulation jamming method. Consequently, the victim model can be effectively targeted by merely transmitting the jamming signal to the SAR receiver. Experimental results show that TUAA reduces the recognition rate of four typical DNN models to less than 15% under acceptable time costs and image deformation.