雷达像智能识别对抗研究进展

高勋章; 张志伟; 刘梅; 龚政辉; 黎湘

doi:10.12000/JR23098

雷达像智能识别对抗研究进展

DOI: 10.12000/JR23098

国防科技大学电子科学学院长沙 410073

基金项目: 国家自然科学基金(61921001)

详细信息

作者简介:
高勋章，研究员，博士生导师，主要研究方向为雷达目标识别、智能信息处理

张志伟，博士生，主要研究方向为雷达目标识别、智能对抗攻击与防御

刘　梅，硕士生，主要研究方向为智能感知与处理、雷达目标识别

龚政辉，助理研究员，主要研究方向为雷达对抗、雷达抗干扰、雷达通信一体化

黎　湘，教授，博士生导师，主要研究方向为雷达目标特性与识别

通讯作者:
高勋章 gaoxunzhang@nudt.edu.cn

张志伟 514131141@qq.com

责任主编：徐丰 Corresponding Editor: XU Feng
中图分类号: TP753
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- 被引次数: 0
出版历程
- 收稿日期: 2023-05-29
- 修回日期: 2023-07-13
- 网络出版日期: 2023-07-26
- 刊出日期: 2023-08-28

Intelligent Radar Image Recognition Countermeasures: A Review

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (61921001)

More Information

Corresponding author: GAO Xunzhang, gaoxunzhang@nudt.edu.cn; ZHANG Zhiwei, 514131141@qq.com

摘要

摘要: 基于深度神经网络的雷达像智能识别技术已经成为雷达信息处理领域的前沿和热点。然而，深度神经网络模型易受到对抗攻击的威胁。攻击者可以在隐蔽的条件下误导智能目标识别模型做出错误预测，严重影响其识别精度和鲁棒性。该文梳理了近年来雷达像智能识别对抗技术发展现状，总结分析了现有雷达一维/二维像识别对抗攻击方法和防御方法的特点，最后讨论了当前雷达像智能识别对抗研究领域值得关注的5个开放问题。
- 雷达像识别 /
- 深度神经网络 /
- 对抗攻击 /
- 后门攻击 /
- 对抗防御
Abstract: Intelligent radar image recognition based on Deep Neural Networks (DNN) has become an important topic in radar information processing. However, DNN models are susceptible to adversarial attacks. Malicious attackers can cause intelligent image recognition models to make incorrect predictions, considerably reducing their recognition accuracy and robustness. This article reviews recent research progress on intelligent radar image recognition countermeasures. Then it summarizes the adversarial attack methods on one/two-dimensional radar image recognition models and adversarial defense methods. Finally, it discusses five open questions worthy of in-depth research in intelligent radar image recognition countermeasures.
- Radar image recognition /
- Deep Neural Networks (DNN) /
- Adversarial attack /
- Backdoor attack /
- Adversarial defense

HTML全文

图 1 雷达目标对抗样本示例

Figure 1. Radar target adversarial sample

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图 2 深度模型识别原理图

Figure 2. Recognition process of deep neural network

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图 3 对抗攻击方法分类

Figure 3. Categories of adversarial attack

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图 4 基于全距离单元扰动^[63]的HRRP对抗攻击示意图

Figure 4. All-range-cell^[63] adversarial attacks on radar HRRP

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图 5 基于特定距离单元扰动^[66]的HRRP对抗攻击示意图

Figure 5. Certain-range-cell^[66] adversarial attacks on radar HRRP

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图 6 对抗防御方法分类

Figure 6. Categories of adversarial defense

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图 7 基于优化目标函数的防御方法

Figure 7. Adversarial defense based on optimizing objective function

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表 1 基于属性散射中心模型的典型雷达二维像对抗攻击方法

Table 1. Typical radar image adversarial attacks based on attribute scattering center model

方法	干净样本	扰动	对抗样本	关键技术
文献[46]				散射中心提取，空域形变
文献[47]				单散射中心攻击
文献[48]				改进OMP算法，黑盒攻击

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表 2 雷达二维像对抗攻击研究现状

Table 2. Summary of adversarial attacks on radar two-dimensional image

文献	攻击先验	扰动范数	验证模型	数据集	攻击特异性	优缺点
[33]	白盒	${L_\infty }$	VGG^[50] ResNet^[51] DenseNet^[52] GoogleNet^[53] InceptionV3^[54]	MSTAR SENSAR^[58]	非定向	验证光学方法的适用性和差异性，未结合雷达像特性
[34]	白盒	${L_0}$	A-ConvNet^[10]	MSTAR	非定向
[35]	白盒	${L_2}/{L_\infty }$	自定义CNN	MSTAR	非定向
[36]	白盒	${L_2}$	ResNet	MSTAR	定向/非定向	结合了雷达像自身特性和识别场景，未考虑对抗样本的物理实现问题
[37]	白盒/黑盒	${L_2}/{L_0}$	A-ConvNet ResNet	MSTAR OpenSARship^[59]	定向/非定向
[38]	白盒	${L_0}$	ResNet VGG MobleNet-v2^[55]	So2Sat-LCZ42^[60]	非定向
[40]	黑盒	${L_\infty }$	AlexNet^[56] ResNet DenseNet VGG A-ConvNet	MSTAR SARSIM^[61]	非定向
[41]	白盒	${L_\infty }$	CNN	MSTAR	非定向	对扰动区域进行初步限制，未建立扰动像素与雷达信号的对应关系
[42]	白盒/黑盒	${L_2}$	GoogleNet DenseNet InceptionV3 ResNet	MSTAR	非定向
[43]	白盒	${L_2}$	自定义CNN	MSTAR	非定向
[44]	黑盒	${L_\infty }$	AconvNet VGG ResNet DenseNet InceptionV4^[57]	MSTAR	非定向
[46]	白盒	双线性变换	ResNet MobileNet-v2	MSTAR	非定向	考虑了单帧静止目标对抗样本的物理实现，未考虑目标运动过程中的扰动变化
[47]	白盒	${L_0}/{L_2}/{L_\infty }$	A-convNet VGG ResNet DenseNet MobileNet-v2	MSTAR SARBake^[62]	非定向
[48]	黑盒	${L_2}$	VGG ResNet MobileNet	MSTAR	非定向

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表 3 雷达一维像对抗攻击研究现状

Table 3. Summary of adversarial attacks on radar HRRP

文献	攻击先验	扰动方式	验证模型	数据集	攻击目标	特点
[63]	白盒	全距离单元	自定义CNN	3类飞机目标：雅克42；塞斯纳S/II；安26	非定向	仅设计数字域攻击，未考虑物理实现性
[64]	白盒	全距离单元	自定义CNN	3类飞机目标：雅克42；塞斯纳 S/II；安26	定向/非定向
[65]	白盒/黑盒	全距离单元	自定义CNN；全连接网络	MSTAR数据集的HRRP还原数据^[67]	定向/非定向
[66]	白盒/黑盒	特定距离单元	AlexNet ResNet DenseNet InceptionNet	3类飞机目标：雅克42；塞斯纳S/II；安26	定向/非定向	较好的物理实现性，未考虑目标姿态等因素带来的影响

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表 4 雷达像识别对抗防御方法

Table 4. Summary of adversarial defense in radar image recognition

防御层级	文献	验证模型	数据集	可防御	优缺点
输入端	[73]	ResNet	UC-Merced^[90]	FGSM/PGD^[91]/CW^[20]/ DeepFool^[19]/ HopSkipJump^[92]/ Square^[93]	仅需在数据端操作，影响干净样本识别率
输入端	[74]	ResNet DenseNet MobileNet ShuffleNet A-ConvNet	MSTAR OpenSAR-Ship^[59]	FGSM/PGD/DeepFool/ CW/SparseFool^[94]/ HopSkipJump/Square	仅需在数据端操作，影响干净样本识别率
模型端	[77]	A-ConvNet VGG ResNet ShuffleNet	MSTAR^[11]	FGSM^[17]/PGD	已知攻击类型时防御效果好，训练耗时
	[47]	A-ConvNet	MSTAR SARBake^[62]	PGD/SMGAA^[47]	已知攻击类型时防御效果好，训练耗时
	[78]	自定义CNN	MSTAR	DeepFool	具有较好的可解释性，难以兼容主流模型
	[79]	SAR-BagNet^[80] ResNet	MSTAR	FGSM/PGD/ CW/DeepFool	具有较好的可解释性，难以兼容主流模型
输出端	[87]	VGG ResNet DenseNet	MSTAR SARBake	FGSM/BIM^[18]/ CW/DeepFool	无需更改模型结构，难以单独胜任识别任务
	[88]	ResNet	MSTAR	FGSM/BIM/ CW/DeepFool
	[89]	VGG ResNet DenseNet	MSTAR SARBake	FGSM/BIM/ CW/DeepFool

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