联合误导性与逼真度优化的SAR ATR最优对抗样本生成方法

苏薪元; 全斯农; 蔡志豪; 邢世其; 汪俊澎

doi:10.12000/JR25179

联合误导性与逼真度优化的SAR ATR最优对抗样本生成方法

DOI: 10.12000/JR25179 CSTR: 32380.14.JR25179

国防科技大学电子信息系统复杂电磁环境效应国家重点实验室长沙 410073

基金项目: 国家自然科学基金(62471471)

详细信息

作者简介:
苏薪元，女，硕士，研究方向为新体制雷达与智能电子对抗

全斯农，男，副研究员，研究方向包括遥感信息处理、模式识别、机器学习等

蔡志豪，男，硕士，研究方向为雷达信号处理、雷达天线阵列设计

邢世其，男，副研究员，研究方向为雷达天线阵列设计、极化SAR数据校准、SAR层析成像和干涉SAR信号处理

汪俊澎，男，博士，研究方向为SAR智能解译、极化SAR数据处理

通讯作者:
全斯农 quansinong13@nudt.edu.cn

责任主编：韦顺军 Corresponding Editor: WEI Shunjun
中图分类号: TN95
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出版历程
- 收稿日期: 2025-09-18

Optimal adversarial sample generation method in SAR ATR based on joint misleading and fidelity optimization

State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (62471471)

More Information

Corresponding author: QUAN Sinong, quansinong13@nudt.edu.cn

摘要

摘要: 对抗样本生成研究是揭示深度神经网络脆弱性及提升合成孔径雷达自动目标识别(SAR ATR)系统鲁棒性的关键。本文针对对抗样本的误导效能与视觉隐蔽这一核心矛盾的平衡问题，提出了联合误导性与逼真度优化的SAR ATR最优对抗样本生成方法，将对抗样本的生成过程建模为一个以“误导性”和“逼真度”为目标的联合优化问题。本文首先提出了一种集成复合变换攻击法以增强攻击的有效性，并构建了融合目标模型分类准确率(ACC)与学习感知图像块相似度(LPIPS)的联合度量模型以量化两个优化目标。随后，提出一种改进的均匀性引导多目标雾凇算法，通过融合Tent混沌映射、混合动态权重和黄金正弦引导，高效地求解该模型，从而获得一组代表不同权衡程度的帕累托最优解集。最终，利用YOLOv10网络对解集中的样本进行扰动检测，以定位扰动被发现的临界点，实现最优参数的量化。在MSTAR和MiniSAR数据集上的实验表明，所提集成复合变换攻击法针对不同集成模型和分类网络的平均目标模型识别准确率为8.96%，总体误导效果较其他方法平均提升了2.25%，其中复杂模型平均提升5.56%；所提均匀性引导的多目标雾凇算法在解集多样性和收敛速度方面较对比方法提升均超过25%；最终该方法能够在ACC降至28.81%的同时，将LPIPS控制在0.407，扰动因子仅为0.031，实现了误导性与逼真度的最佳平衡。该参数在6种不同防御策略下均能保持有效误导，验证了其强鲁棒性，为SAR ATR领域的对抗攻击研究提供了新的思路与量化基准。
- 合成孔径雷达自动目标识别 /
- 对抗样本生成 /
- 集成复合变换攻击法 /
- 多目标优化 /
- 均匀性引导多目标雾凇算法
Abstract: Adversarial sample generation is a key research direction for uncovering the vulnerabilities of deep neural networks and improving the robustness of synthetic aperture radar automatic target recognition (SAR ATR) systems. This study proposes an optimal adversarial sample generation method for SAR ATR that jointly optimizes misleading effectiveness and fidelity, aiming to resolve the core contradiction between adversarial effectiveness and visual concealment. The generation process is modeled as a joint optimization problem with the goals of balancing “misleading” and “fidelity.” First, an integrated composite transform attack strategy is designed to enhance attack effectiveness, and a joint measurement model is developed that combines the classification accuracy of the target model with the learned perceptual image patch similarity to quantify the two optimization goals. Next, an improved uniformity-guided multiobjective RIME algorithm is proposed. By integrating the Tent chaotic map, hybrid dynamic weighting, and golden sine guidance, the model is efficiently solved, yielding a set of Pareto-optimal solutions that represent various tradeoff degrees. Finally, the YOLO object detection network is employed to identify perturbations in the samples within the solution set, thereby locating the critical points where disturbances occur and enabling the quantification of optimal parameters. Experiments on MSTAR and MiniSAR datasets show that the proposed ensemble compound transform attack method achieves an average target model recognition accuracy of 8.96% across different ensemble models and classification networks, improving the overall misleading effect by an average of 2.25% compared to other methods. Among them, the complex model increases by an average of 5.56%, while the proposed uniformity-guided multiobjective RIME algorithm improves the solution set diversity and convergence speed by over 25% compared with the comparison method. Using this method, the learned perceptual image patch similarity is maintained at 0.407 and the perturbation factor at 0.031, while classification accuracy decreases to 28.81%, demonstrating a tradeoff between misleading effectiveness and visual fidelity. This parameter maintains effective misleading performance under six different defense strategies, demonstrating strong robustness and providing a new approach and quantitative benchmark for adversarial attack research in SAR ATR.

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图 1 联合误导性与逼真度优化的SAR ATR最优对抗样本生成方法流程图

Figure 1. Optimal adversarial sample generation method flow chart

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图 2 对抗样本的误导性与逼真度的矛盾关系

Figure 2. The contradictory relationship between misleading and fidelity of adversarial samples

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图 3 复合变换前后对比图

Figure 3. Comparison diagram before and after composite transformation

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图 4 EGCT方法在不同代理模型下攻击有效性对比图

Figure 4. Comparison of attack effectiveness of EGCT method under different surrogate models

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图 5 集成策略有效性对比及实验结果图

Figure 5. Comparison of the effectiveness of the ensemble strategy and the experimental results

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图 6 集成复合变换攻击法有效性可视化对比图

Figure 6. Visual comparison diagram of effectiveness of EGCT

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图 7 集成复合变换攻击法在不同扰动因子下生成的扰动与对抗样本

Figure 7. Perturbation and adversarial samples generated by EGCT under different perturbation factors

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图 8 不同样本生成方法在各集成策略下的迁移性比较

Figure 8. Migration comparison of different sample generation methods under various integration strategies

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图 9 UMORIME求解得到的帕累托解集

Figure 9. The Pareto solution set obtained by UMORIME solution

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图 12 YOLOv10网络训练集及其热力图

Figure 12. YOLOv10 network training set and its heat map

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图 10 最优平衡对抗样本及场景检测图

Figure 10. Optimal balanced adversarial samples and scene detection graph

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图 11 不同检测网络对应最优平衡对抗样本检测图

Figure 11. Different detection networks correspond to the optimal balanced adversarial sample detection graph

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表 1 每类数据集样本数

Table 1. Number of samples for each type of data set

目标种类	攻击方数据集A	目标方数据集B	MSTAR
BMP2	313	312	625
BTR60	226	225	451
T72	309	309	618
T62	286	286	572
BTR70	215	214	429
BRDM2	286	286	572
D7	287	286	573
ZIL131	287	286	573
ZSU234	287	286	573
2S1	286	286	572

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表 2 不同优化算法下Pareto解集的参数值

Table 2. Parameter values of Pareto solution set under different optimization algorithms

优化算法	Spacing	Spacing误差	运行时间(s)	运行时间误差(s)
UMORIME	0.4778	1.6635	18.5102	0.0232
MORIME	0.6198	1.9320	23.7342	0.0298
MOEDO	0.6349	2.0143	23.5368	0.0279
MOCOA	0.6534	2.0459	22.5189	0.0254
MOGOA	0.8541	2.5952	24.2761	0.0319
NSGA-II	2.2300	4.8263	40.1437	0.0750

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表 3 不同改进策略有效性对比实验结果

Table 3. Experimental results of effectiveness comparison of different improvement strategies

Tent混沌映射	混合动态权重	黄金正弦引导	Spacing	运行时间(s)
×	×	×	0.6198	23.7342
√	×	×	0.5446	23.4121
×	√	×	0.5729	21.3463
×	×	√	0.5841	21.1359
√	√	×	0.5133	21.1135
√	×	√	0.5142	20.8102
×	√	√	0.5428	19.8576
√	√	√	0.4778	18.5102

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表 4 不同检测网络下最优平衡对抗样本参数值

Table 4. Optimal balanced adversarial sample parameter values under different detection networks

检测网络	LPIPS	ACC	扰动因子
Faster R-CNN	0.570	12.37	0.0452
DETR	0.431	25.19	0.0330
YOLOv10	0.407	28.81	0.0310

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表 5 不同防御策略下最优平衡对抗样本的参数值

Table 5. The parameter values of the optimal balanced adversarial samples under different defense strategies

防御策略	LPIPS	ACC	扰动因子
FGSM	0.469	19.90	0.0369
I-FGSM	0.444	23.25	0.0342
MI-FGSM	0.439	24.05	0.0338
NI-FGSM	0.431	25.19	0.0330
PGD	0.416	27.41	0.0315
VT	0.407	28.81	0.0310

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