基于雅可比显著图的电磁信号快速对抗攻击方法

doi:10.11959/j.issn.1000-436x.2024021

Abstract

Abstract:

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 saliency maps based on the matrix were generated, then the most salient feature points were iteratively selected and perturbations 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%.

Key words: deep neural network, adversarial sample, electromagnetic signal modulation recognition, Jacobian saliency map, target attack

CLC Number:

TP183

Jian ZHANG, Xia ZHOU, Yiran ZHANG, Zicong WANG. Electromagnetic signal fast adversarial attack method based on Jacobian saliency map[J]. Journal on Communications, 2024, 45(1): 180-193.

Figures/Tables 13

References 28

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攻击方法	ASR	ACAC	ACTC	ATS/s	SSIM	L₀	L₂	L_∞
JSMA	97.76%	76.33%	0.003%	611.22	80.79%	3.61%	0.11	3.08
DI-FGSM	84.75%	65.71%	7.331%	0.58	53.66%	75.00%	0.68	2.26
PGD	72.58%	53.82%	4.884%	0.63	67.39%	93.84%	0.45	2.18
FJSMA(ε=0.2)	96.91%	66.96%	0.097%	78.44	93.93%	26.81%	0.09	0.19
FJSMA(ε=0.4)	97.33%	69.27%	0.051%	63.18	92.92%	18.94%	0.12	0.39
FJSMA(ε=0.6)	97.64%	74.85%	0.013%	50.21	90.25%	15.64%	0.18	0.56
FJSMA(AVG)	97.29%	70.51%	0.054%	63.94	92.37%	20.46%	0.13	0.38
MA-NA-FGSM	95.20%	72.33%	1.94%	1.76	76.8%	87.21%	0.49	1.04
Grad-CAM	80.91%	65.26%	4.791%	11.46	85.41%	24.33%	0.31	1.74

攻击方法	ASR	ACAC	ACTC	ATS/s	SSIM	L₀	L₂	L_∞
JSMA	97.12%	73.03%	0.128%	432.08	76.48%	8.07%	0.13	3.11
DI-FGSM	83.88%	66.81%	2.146%	0.42	56.71%	88.96%	0.73	2.69
PGD	74.12%	49.85%	17.251%	0.36	65.46%	100.00%	0.65	2.13
FJSMA(ε=0.2)	94.55%	64.91%	0.107%	46.38	92.38%	28.26%	0.11	0.18
FJSMA(ε=0.4)	95.48%	70.33%	0.081%	41.25	90.31%	18.93%	0.16	0.37
FJSMA(ε=0.6)	96.35%	75.82%	0.034%	33.89	87.90%	10.58%	0.21	0.58
FJSMA(AVG)	95.46%	70.35%	0.074%	40.51	90.20%	19.26%	0.16	0.38
MA-NA-FGSM	94.36%	73.21%	1.88%	1.55	77.31%	88.90%	0.53	1.13
Grad-CAM	81.42%	66.34%	4.63%	10.98	86.37%	25.61%	0.29	1.58

攻击方法	ASR	ACAC	ACTC	ATS/s	SSIM	L₀	L₂	L_∞
JSMA	97.33%	74.83%	0.038%	488.36	78.23%	6.59%	0.13	2.79
DI-FGSM	83.97%	62.18%	6.012%	0.56	54.62%	87.89%	0.77	2.12
PGD	71.44%	52.91%	7.297%	0.73	64.93%	92.47%	0.61	2.36
FJSMA(ε=0.2)	96.35%	67.48%	0.307%	53.93	91.87%	27.33%	0.13	0.19
FJSMA(ε=0.4)	96.12%	70.35%	0.196%	47.29	88.69%	19.26%	0.19	0.38
FJSMA(ε=0.6)	97.24%	73.67%	0.103%	40.66	86.31%	13.95%	0.25	0.53
FJSMA(AVG)	96.57%	69.55%	0.202%	47.29	88.96%	20.18%	0.19	0.37
MA-NA-FGSM	94.97%	72.95%	1.76%	1.58	74.29%	87.13%	0.47	0.98
Grad-CAM	80.57%	64.47%	5.81%	11.23	85.92%	25.82%	0.34	2.01

Electromagnetic signal fast adversarial attack method based on Jacobian saliency map

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