DE-JSMA: a sparse adversarial attack algorithm for SAR-ATR models

Abstract

The vulnerability of DNN makes the SAR-ATR system that uses an intelligent algorithm for recognition also somewhat vulnerable. In order to verify the vulnerability, this paper proposes DE-JSMA, a novel sparse adversarial attack algorithm based on a salient map's adversarial attack algorithm and differential evolution algorithm, with the synthetic aperture radar (SAR) image feature sparsity considered. After accurately screening out the salient features that have a great impact on the model inference results, the DE-JSMA algorithm optimizes the appropriate feature values for the salient features. In order to verify its effectiveness more comprehensively, a new metric that combines the attack success rate with the average confidence interval of adversarial examples is proposed. The experimental results show that DE-JSMA extends JSMA, which can be used only for targeted attack scenario, to untargeted attack scenario without increasing too much time consumption but ensuring a high attack success rate, thus achieving sparse adversarial attack with higher reliability and better sparsity in both attack scenarios. The pixel perturbations of only 0.31% and 0.85% can achieve the untargeted and targeted attack success rates up to 100% and 78.79% respectively.

Keywords

• synthetic aperture radar
• automatic target recognition
• deep learning
• adversarial attack
• sparse attack