Electromagnetic signal fast adversarial attack method based on Jacobian saliency map
In order to generate high-quality electromagnetic signal countermeasure examples,a fast Jacobian saliency map attack(FJSMA)method was proposed.The Jacobian matrix of the attack target class was calculated and feature sa-liency maps based on the matrix were generated,then the most salient feature points were iteratively selected and pertur-bations in their neighborhood were continuously added while introducing a single point perturbation constraint,finally adversarial examples were generated.Experimental results show that,compared with Jacobian saliency map attack method,FJSMA improves the generation speed by about 10 times while maintaining the same high attack success rate,and improves the similarity by more than 11%,and compared with other gradient-based methods,the attack success rate is improved by more than 20%,and the similarity is improved by 20%to 30%.
deep neural networkadversarial sampleelectromagnetic signal modulation recognitionJacobian saliency maptarget attack
万方数据
维普
