基于动量迭代快速梯度符号的SAR-ATR深度神经网络黑盒攻击算法

万烜申 刘伟 牛朝阳 卢万杰

万烜申, 刘伟, 牛朝阳, 等. 基于动量迭代快速梯度符号的SAR-ATR深度神经网络黑盒攻击算法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR23220
引用本文: 万烜申, 刘伟, 牛朝阳, 等. 基于动量迭代快速梯度符号的SAR-ATR深度神经网络黑盒攻击算法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR23220
WAN Xuanshen, LIU Wei, NIU Chaoyang, et al. Black-box attack algorithm for SAR-ATR deep neural networks based on MI-FGSM[J]. Journal of Radars, in press. doi: 10.12000/JR23220
Citation: WAN Xuanshen, LIU Wei, NIU Chaoyang, et al. Black-box attack algorithm for SAR-ATR deep neural networks based on MI-FGSM[J]. Journal of Radars, in press. doi: 10.12000/JR23220

基于动量迭代快速梯度符号的SAR-ATR深度神经网络黑盒攻击算法

doi: 10.12000/JR23220
基金项目: 国家自然科学基金(42201472)
详细信息
    作者简介:

    万烜申,硕士生,主要研究方向为SAR智能解译与对抗

    刘 伟,副教授,博士,主要研究方向为智能信息处理、遥感图像分析

    牛朝阳,副教授,博士,主要研究方向为SAR信息处理与对抗

    卢万杰,讲师,博士,主要研究方向为智能信息处理、遥感图像分析

    通讯作者:

    刘伟 greatliuliu@163.com

  • 责任主编:李宁 Corresponding Editor: LI Ning
  • 中图分类号: TP391

Black-box Attack Algorithm for SAR-ATR Deep Neural Networks Based on MI-FGSM

Funds: The National Natural Science Foundation of China (42201472)
More Information
  • 摘要: 合成孔径雷达自动目标识别(SAR-ATR)领域缺乏有效的黑盒攻击算法,为此,该文结合动量迭代快速梯度符号(MI-FGSM)思想提出了一种基于迁移的黑盒攻击算法。首先结合SAR图像特性进行随机斑点噪声变换,缓解模型对斑点噪声的过拟合,提高算法的泛化性能;然后设计了能够快速寻找最优梯度下降方向的ABN寻优器,通过模型梯度快速收敛提升算法攻击有效性;最后引入拟双曲动量算子获得稳定的模型梯度下降方向,使梯度在快速收敛过程中避免陷入局部最优,进一步增强对抗样本的黑盒攻击成功率。通过仿真实验表明,与现有的对抗攻击算法相比,该文算法在MSTAR和FUSAR-Ship数据集上对主流的SAR-ATR深度神经网络的集成模型黑盒攻击成功率分别提高了3%~55%和6%~57.5%,而且生成的对抗样本具有高度的隐蔽性。

     

  • 图  1  TBAA算法的原理图

    Figure  1.  Schematic diagram of the TBAA algorithm

    图  2  对抗样本的可迁移性

    Figure  2.  Transferability of adversarial examples

    图  3  MSTAR数据集的SAR图像

    Figure  3.  SAR images of the MSTAR dataset

    图  4  FUSAR-Ship数据集的SAR图像

    Figure  4.  SAR images of the FUSAR-Ship dataset

    图  5  攻击成功率随概率p变化折线图

    Figure  5.  The attack success rate changes with probability p

    图  6  黑盒模型下攻击成功率变化折线图

    Figure  6.  Line chart of attack success rate change under black-box model

    图  7  TBAA干净样本、对抗扰动以及对抗样本展示

    Figure  7.  TBAA clean examples, adversarial perturbations and adversarial examples display

    1  基于迁移的SAR-ATR黑盒攻击算法

    1.   SAR-ATR Transfer-based Black-box Attack Algorithm (TBAA)

     输入:干净样本xK个深度神经网络模型${f_1},{f_2},\cdots,{f_K}$,对应
     的网络模型逻辑值${l_1},{l_2},\cdots,{l_K}$以及相应的网络模型集成权重
     ${w_1},{w_2},\cdots,{w_K}$,扰动量大小$\varepsilon $,步长$\alpha $,迭代次数T,系数$v,\beta $,
     ${\beta _1}$和${\beta _2}$
     输出:对抗样本${x^{{\text{adv}}}}$
     步骤1 $\alpha \leftarrow \varepsilon /T,{g_0} \leftarrow 0,{m_0} \leftarrow 0,{n_0} \leftarrow 0$
     步骤2 ${g_0} \leftarrow 0,{m_0} \leftarrow 0,{s_0} \leftarrow 0,x_0^{{\text{adv}}} \leftarrow x$
     步骤3 For $t = 0$ to $T - 1$ do
     步骤4 Update ${m_t}$ by ${m_t} = {\beta _1} \cdot {m_{t - 1}} + (1 - {\beta _1}){g_t}$
     步骤5 Update ${\hat m_t} = \dfrac{{{m_t}}}{{1 - \beta _1^t}}$
     步骤6 Update ${s_t} = {\beta _2} \cdot {s_{t - 1}} + (1 - {\beta _2}){({\hat g_t} - {m_t})^2}$
     步骤7 Update ${\hat s_t} = \dfrac{{{s_t} + \zeta }}{{1 - \beta _2^t}}$
     步骤8 $ \tilde x_t^{{\text{adv}}} = x_t^{{\text{adv}}} + \dfrac{\alpha }{{\sqrt {{{\hat s}_t} + \zeta } }}{\hat m_t} $
     步骤9 $l(\tilde x_t^{{\text{adv}}}) = \sum\limits_{k = 1}^K {{w_k}{l_k}\left( {{\text{ST}}(\tilde x_t^{{\text{adv}}};p)} \right)} $
     步骤10 Update $g_t^*$ by $ g_t^* = {\nabla _{x_t^{{\text{adv}}}}}J\left( {{\text{ST}}(\tilde x_t^{{\text{adv}}};p),y} \right) $
     步骤11 Update ${g_{t + 1}}$ by ${g_{t + 1}} = \beta {g_t} + (1 - \beta ) \cdot \dfrac{{g_t^*}}{{{{\left\| {g_t^*} \right\|}_1}}}$
     步骤12 Update ${\tilde g_{t + 1}}$ by ${\tilde g_{t + 1}} = (1 - v){g_{t + 1}} + v \cdot \dfrac{{g_t^*}}{{{{\left\| {g_t^*} \right\|}_1}}}$
     步骤13 $x_{t + 1}^{{\text{adv}}} = {\mathrm{Clip}}_x^\varepsilon \left\{ {x_t^{{\text{adv}}} + \alpha \cdot {\mathrm{sign}}({{\tilde g}_{t + 1}})} \right\}$
     步骤14 End for
     步骤15 Return $x_t^{{\text{adv}}} = x_{t + 1}^{{\text{adv}}}$
    下载: 导出CSV

    表  1  MSTAR数据中SOC下的SAR图像类别与样本数量

    Table  1.   SAR image categories and number of samples under SOC in MSTAR dataset

    目标类别 训练集 测试集
    俯仰角(°) 数量 俯仰角(°) 数量
    2S1 17 299 15 274
    BRDM2 17 298 15 274
    BTR60 17 233 15 195
    D7 17 299 15 274
    T62 17 299 15 273
    ZIL131 17 299 15 274
    BMP2 17 233 15 195
    ZSU23/4 17 299 15 274
    T72 17 232 15 196
    BTR70 17 233 15 196
    下载: 导出CSV

    表  2  FUSAR-Ship数据集中SAR图像类别与样本数量

    Table  2.   SAR image categories and number of samples in FUSAR-Ship dataset

    目标类别训练集数量测试集数量
    BulkCarrier9725
    CargoShip12632
    Fishing7519
    Tanker3610
    下载: 导出CSV

    表  3  模型识别精度

    Table  3.   Model recognition accuracy

    模型 MSTAR ACC (%) FUSAR-Ship ACC (%)
    AlexNet 95.1 69.47
    VGG16 95.6 70.23
    ResNet18 96.6 68.10
    ResNet50 97.7
    InceptionV3 99.1
    A-ConvNet 99.8
    MobileNet 97.8
    SqueezeNet 95.4 72.25
    PVTv2 98.8
    MobileViTv2 99.4 72.70
    下载: 导出CSV

    表  4  MSTAR数据集单模型攻击成功率(%)

    Table  4.   Single model attack success rate on the MSTAR dataset (%)

    代理模型 攻击算法 受害者模型
    AlexNet VGGNet16 ResNet18 ResNet50 InceptionV3 A-ConvNet MobileNet SqueezeNet PVTv2 MobileViTv2
    AlexNet MI-FGSM 100* 10.9 12.0 9.0 5.0 28.0 35.0 18.9 14.0 19.6
    NAM 100* 12.0 13.0 10.0 6.9 36.0 37.0 22.9 20.7 22.0
    VMI-FGSM 100* 19.5 19.5 17.0 6.0 29.5 39.5 27.0 40.5 21.5
    DI-FGSM 100* 21.0 26.5 16.0 7.5 29.5 43.5 32.5 32.0 20.5
    Attack-Unet-GAN 98.69* 7.0 8.0 7.5 4.0 20.5 32.5 14.5 12.5 9.5
    Fast C&W 100* 4.5 7.0 6.0 3.0 17.5 19.5 12.5 8.0 3.5
    TBAA 100* 23.5 29.0 20.4 14.5 64.0 53.4 33.9 56.0 32.8
    VGGNet16 MI-FGSM 61.0 100* 58.0 56.0 40.0 55.0 41.0 43.0 26.0 30.0
    NAM 60.0 100* 61.0 59.0 42.0 61.0 45.0 47.0 31.0 35.0
    VMI-FGSM 62.5 100* 59.5 58.5 42.5 57.5 41.0 46.5 38.5 38.5
    DI-FGSM 63.5 100* 60.5 67.5 46.5 59.5 42.5 48.0 37.5 38.5
    Attack-Unet-GAN 53.0 100* 40.5 32.5 24.5 32.5 38.5 39.0 23.0 24.5
    Fast C&W 44.5 100* 31.0 37.5 24.0 31.5 22.0 24.5 13.5 14.5
    TBAA 69.5 100* 72.0 78.5 56.9 74.0 56.5 63.5 48.0 48.5
    ResNet18 MI-FGSM 13.0 9.9 100* 20.9 13.9 39.0 26.0 15.0 14.0 5.0
    NAM 15.0 9.0 100* 20.9 16.0 38.0 31.0 17.0 21.0 5.3
    VMI-FGSM 17.0 16.5 100* 25.0 15.8 45.5 31.5 25.0 32.5 10.5
    DI-FGSM 18.0 14.0 100* 21.0 19.0 41.0 29.5 30.0 23.5 8.6
    Attack-Unet-GAN 12.5 6.5 100* 11.5 5.0 19.5 18.5 11.5 11.0 3.0
    Fast C&W 10.0 4.0 100* 6.0 3.0 9.0 11.5 12.0 13.5 4.0
    TBAA 29.0 19.0 100* 25.0 35.5 64.0 42.5 30.0 54.0 24.0
    ResNet50 MI-FGSM 8.0 12.0 10.5 100* 21.0 16.0 22.9 10.0 12.0 9.0
    NAM 10.0 14.0 14.0 100* 22.0 27.0 24.0 13.0 17.0 14.9
    VMI-FGSM 19.5 19.0 14.5 100* 22.0 33.0 33.5 21.0 28.5 13.5
    DI-FGSM 19.5 19.0 22.5 100* 23.5 26.5 23.0 22.5 28.0 11.5
    Attack-Unet-GAN 6.5 10.5 6.5 100* 7.0 15.0 18.0 8.0 8.0 7.0
    Fast C&W 5.0 4.5 7.5 100* 13.0 7.5 10.5 7.5 10.5 6.0
    TBAA 24.5 19.9 27.5 100* 27.4 48.5 44.9 25.5 43.9 22.0
    InceptionV3 MI-FGSM 29.0 31.4 65.5 38.0 100* 65.0 31.0 39.0 12.0 28.0
    NAM 36.0 35.0 67.9 42.0 100* 66.9 33.9 41.9 18.0 29.5
    VMI-FGSM 33.0 31.5 52.5 39.0 100* 68.5 34.0 43.0 30.0 29.5
    DI-FGSM 34.0 34.5 56.0 41.0 100* 66.0 33.5 41.5 23.5 28.5
    Attack-Unet-GAN 20.6 24.5 53.0 31.0 100* 32.5 25.0 26.5 9.0 24.5
    Fast C&W 11.0 16.5 30.0 28.0 100* 20.0 12.0 15.0 10.5 16.5
    TBAA 41.0 50.0 73.5 52.0 100* 76.5 49.5 47.0 45.9 43.9
    A-ConvNet MI-FGSM 19.9 15.5 29.5 20.9 11.5 100* 29.0 15.0 21.9 9.0
    NAM 23.5 17.5 35.5 24.5 18.9 100* 32.5 18.0 24.0 13.0
    VMI-FGSM 25.5 19.0 37.0 25.5 19.5 100* 36.5 31.0 31.0 12.5
    DI-FGSM 28.0 17.5 37.0 23.0 21.5 100* 36.5 29.5 26.5 10.5
    Attack-Unet-GAN 10.8 5.6 9.0 13.0 7.0 98.0* 11.6 11.0 14.7 8.0
    Fast C&W 11.5 4.0 8.5 5.0 3.0 97.5* 10.5 12.5 13.5 4.0
    TBAA 29.5 21.9 40.5 30.5 24.5 100* 38.0 32.9 36.0 24.0
    MobileNet MI-FGSM 16.0 15.1 10.0 15.0 15.6 18.0 100* 18.9 8.0 9.0
    NAM 18.0 14.9 12.0 18.9 18.9 25.0 100* 26.9 9.5 10.5
    VMI-FGSM 21.0 18.0 12.0 18.0 21.5 23.0 100* 23.5 22.0 14.0
    DI-FGSM 19.0 17.5 10.5 17.5 18.0 19.5 100* 20.5 22.0 14.5
    Attack-Unet-GAN 9.0 3.5 7.5 7.8 2.5 12.5 100* 11.0 7.3 5.0
    Fast C&W 10.0 4.0 6.0 5.0 3.0 7.0 100* 10.0 6.5 4.0
    TBAA 24.0 20.5 18.9 26.0 25.4 32.9 100* 30.0 24.0 25.0
    SqueezeNet MI-FGSM 19.5 9.5 20.5 18.0 6.0 40.5 31.4 100* 18.0 18.0
    NAM 18.5 10.3 20.9 19.5 6.5 40.5 32.9 100* 24.0 21.0
    VMI-FGSM 26.5 15.5 28.5 25.5 11.0 42.5 32.0 100* 28.5 19.5
    DI-FGSM 21.0 11.5 30.5 22.5 12.0 41.0 31.5 100* 23.0 21.5
    Attack-Unet-GAN 13.0 8.0 16.5 17.0 4.5 17.5 17.0 100* 12.5 14.5
    Fast C&W 10.0 4.5 7.0 5.5 3.0 18.0 10.0 100* 13.5 14.0
    TBAA 28.0 18.5 32.5 31.0 12.5 53.5 38.5 100* 41.9 39.0
    PVTv2 MI-FGSM 10.0 7.3 9.0 12.0 15.5 6.0 18.0 7.8 100* 11.3
    NAM 13.0 3.5 10.7 13.5 21.5 10.4 19.9 9.0 100* 18.5
    VMI-FGSM 12.0 12.0 9.5 20.0 22.5 16.0 23.0 11.0 100* 19.5
    DI-FGSM 11.0 13.5 11.0 15.0 23.5 12.0 23.5 12.6 100* 13.0
    Attack-Unet-GAN 8.5 5.0 7.5 7.9 12.5 3.5 11.6 4.5 100* 9.0
    Fast C&W 10.0 4.0 6.5 4.5 13.0 5.5 9.0 3.7 100* 4.0
    TBAA 26.0 23.0 22.0 27.0 35.0 28.9 37.0 25.0 100* 32.0
    MobileViTv2 MI-FGSM 14.0 16.0 19.0 18.3 7.9 43.8 30.0 18.0 52.0 100*
    NAM 21.4 24.0 26.2 20.7 11.6 47.8 33.9 25.4 58.0 100*
    VMI-FGSM 21.0 25.0 29.0 21.5 11.5 45.0 35.0 27.0 56.0 99.5*
    DI-FGSM 22.5 23.1 29.0 24.0 10.5 46.3 38.0 27.5 58.5 98.0*
    Attack-Unet-GAN 11.0 6.5 14.0 11.5 5.5 29.0 15.5 15.0 46.0 100*
    Fast C&W 11.0 4.0 8.5 5.5 3.0 17.5 10.5 11.5 45.0 99.5*
    TBAA 40.0 31.9 33.9 35.9 20.3 66.0 48.0 45.9 65.9 100*
    注:标红字体为最优值,标蓝字体为次优值。*表示白盒攻击成功率,其余数值表示黑盒攻击成功率
    下载: 导出CSV

    表  5  FUSAR-Ship数据集单模型攻击成功率(%)

    Table  5.   Single model attack success rate on the FUSAR-Ship dataset (%)

    代理模型 攻击算法 受害者模型
    AlexNet VGGNet16 ResNet18 SqueezeNet MobileViTv2
    AlexNet MI-FGSM 100* 38.00 40.00 33.90 68.00
    NAM 100* 48.00 62.00 45.90 76.00
    VMI-FGSM 100* 47.10 63.60 42.60 70.00
    DI-FGSM 98.41* 47.40 63.56 44.93 74.94
    Attack-Unet-GAN 100* 23.80 33.20 15.60 30.00
    Fast C&W 99.96* 18.70 29.10 12.40 24.00
    TBAA 100* 60.00 84.00 56.00 80.00
    VGGNet16 MI-FGSM 28.00 100* 24.00 40.00 46.00
    NAM 33.90 100* 30.00 38.00 50.00
    VMI-FGSM 33.90 98.62* 28.10 42.80 52.40
    DI-FGSM 37.60 98.76* 36.60 42.40 52.60
    Attack-Unet-GAN 13.50 100* 20.40 24.00 26.00
    Fast C&W 9.30 99.96 19.60 22.90 24.00
    TBAA 43.90 100* 48.00 62.00 56.00
    ResNet18 MI-FGSM 6.00 7.90 100* 15.90 40.00
    NAM 7.90 9.90 100* 21.90 50.00
    VMI-FGSM 9.30 10.60 100* 28.60 53.80
    DI-FGSM 13.50 12.80 99.96* 29.91 54.60
    Attack-Unet-GAN 4.50 5.60 100* 10.40 17.80
    Fast C&W 5.30 6.20 99.98* 6.70 12.90
    TBAA 38.00 18.00 100* 50.00 60.00
    SqueezeNet MI-FGSM 16.00 9.90 28.00 100* 45.90
    NAM 21.90 14.00 43.90 100* 56.00
    VMI-FGSM 25.10 19.30 44.20 99.86* 53.30
    DI-FGSM 25.07 16.32 45.39 99.59* 59.40
    Attack-Unet-GAN 14.50 7.90 22.00 100* 28.00
    Fast C&W 10.10 6.30 20.10 98.69* 26.90
    TBAA 39.90 31.90 65.90 100* 64.00
    MobileViTv2 MI-FGSM 4.00 7.90 42.00 21.90 100*
    NAM 9.00 16.00 48.00 26.00 100*
    VMI-FGSM 12.80 23.50 45.90 24.10 100*
    DI-FGSM 13.20 18.60 47.00 26.40 99.52*
    Attack-Unet-GAN 2.90 5.30 25.60 18.00 100*
    Fast C&W 2.60 4.20 21.60 14.60 100*
    TBAA 24.00 16.00 67.90 43.90 100*
    注:标红字体为最优值,标蓝字体为次优值。*表示白盒攻击成功率,其余数值表示黑盒攻击成功率
    下载: 导出CSV

    表  6  集成模型攻击成功率(%)

    Table  6.   Ensemble model attack success rate (%)

    数据集 攻击算法 AlexNet VGGNet16 ResNet18 ResNet50 InceptionV3 A-ConvNet MobileNet SqueezeNet PVTv2 MobileViTv2
    MSTAR MI-FGSM 62.9 39.0 52.0 65.0 42.0 67.9 50.0 51.5 68.0 46.0
    NAM 63.1 41.5 68.2 70.5 45.0 75.7 53.2 54.0 75.6 51.4
    VMI-FGSM 66.4 43.5 72.5 65.8 46.5 74.6 52.0 53.0 76.3 56.0
    DI-FGSM 69.0 44.3 74.0 70.0 50.3 76.0 55.0 55.8 70.0 51.0
    Attack-Unet-GAN 53.6 30.5 47.0 35.0 30.0 35.0 41.0 43.0 52.3 31.0
    Fast C&W 46.0 26.8 35.0 38.0 28.0 33.0 28.5 30.0 51.0 24.0
    TBAA 72.0 62.0 86.0 88.0 70.0 88.0 70.0 66.0 92.0 78.0
    FUSAR-
    Ship
    MI-FGSM 31.9 40.9 48.0 45.9 71.9
    NAM 34.5 50.5 68.5 48.5 78.0
    VMI-FGSM 35.8 53.2 67.0 51.3 76.5
    DI-FGSM 36.0 56.0 68.0 50.0 78.0
    Attack-Unet-GAN 16.0 25.0 38.0 28.5 38.4
    Fast C&W 12.5 22.5 34.2 26.0 32.0
    TBAA 70.0 62.0 86.0 64.0 88.0
    注:标红数字为最优值,标蓝数字为次优值。
    下载: 导出CSV

    表  7  消融实验方法设置

    Table  7.   Ablation experiment method setup

    攻击算法 QHM ABN ST
    MI-FGSM
    AN-QHMI-FGSM
    ABN-QHMI-FGSM
    TBAA
    下载: 导出CSV

    表  8  消融实验攻击成功率(%)

    Table  8.   Ablation experiment attack success rate (%)

    数据集 攻击算法 AlexNet VGGNet16 ResNet18 ResNet50 InceptionV3 A-ConvNet MobileNet SqueezeNet PVTv2 MobileViTv2
    MSTAR MI-FGSM 62.9 39.0 52.0 65.0 42.0 67.9 50.0 51.5 68.0 46
    AN-QHMI-FGSM 65.7 48.0 75.0 78.0 56.0 82.0 56.0 57.2 82.0 58.0
    ABN-QHMI-FGSM 69.3 51.6 82.0 81.0 63.0 85.2 68.3 60.8 88.3 69.5
    TBAA 72.0 62.0 86.0 88.0 70.0 88.0 70.0 66.0 92.0 78.0
    FUSAR-
    Ship
    MI-FGSM 31.9 40.9 38.0 45.9 71.9
    AN-QHMI-FGSM 36.9 52.0 76.0 50.0 81.6
    ABN-QHMI-FGSM 43.9 59.9 81.5 52.0 85.0
    TBAA 70.0 62.0 86.0 64.0 88.0
    注:标红数字为最优值,标蓝数字为次优值。
    下载: 导出CSV

    表  9  MSTAR数据集在集成模型攻击下原始SAR图像和SAR对抗样本的平均结构相似度

    Table  9.   ASS of original SAR images and SAR adversarial examples under ensemble model attack on MSTAR dataset

    攻击算法 AlexNet VGGNet16 ResNet18 ResNet50 InceptionV3 A-ConvNet MobileNet SqueezeNet PVTv2 MobileViTv2 Mean
    MI-FGSM 0.951 0.959 0.968 0.976 0.970 0.962 0.969 0.960 0.963 0.960 0.9638
    NAM 0.962 0.965 0.971 0.978 0.973 0.967 0.973 0.966 0.968 0.962 0.9685
    VMI-FGSM 0.965 0.961 0.972 0.976 0.975 0.969 0.977 0.967 0.969 0.965 0.9696
    DI-FGSM 0.960 0.970 0.974 0.974 0.976 0.971 0.979 0.974 0.970 0.963 0.9711
    Attack-Unet-
    GAN
    0.968 0.975 0.975 0.978 0.978 0.974 0.982 0.975 0.972 0.968 0.9745
    Fast C&W 0.969 0.974 0.976 0.979 0.979 0.974 0.980 0.975 0.971 0.967 0.9744
    TBAA 0.969 0.975 0.979 0.981 0.978 0.975 0.981 0.973 0.972 0.967 0.9750
    注:标红数字为最优值,标蓝数字为次优值。
    下载: 导出CSV

    表  10  对抗样本生成效率(s)

    Table  10.   Adversarial examples generation efficiency (s)

    攻击方法 VGGNet16 ResNet18 ResNet50 InceptionV3 A-ConvNet MobileNet Squeezenet Ensemble
    MI-FGSM 0.2970 0.2404 0.3621 0.5217 0.1797 0.3258 0.2550 1.8953
    NAM 0.3014 0.2410 0.3623 0.5303 0.1822 0.3248 0.2257 1.8973
    VMI-FGSM 0.2980 0.2498 0.3625 0.5289 0.1826 0.3289 0.2274 1.9766
    DI-FGSM 0.2984 0.2485 0.3623 0.5280 0.1823 0.3283 0.2294 1.9795
    Attack-Unet-GAN 0.0052 0.0052 0.0052 0.0052 0.0052 0.0052 0.0052 0.0052
    Fast C&W 0.0053 0.0053 0.0053 0.0053 0.0053 0.0053 0.0053 0.0053
    TBAA 0.3588 0.3046 0.4159 0.5676 0.2456 0.3876 0.2824 2.1357
    注:标红数字为最大值,标蓝数字为最小值。
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  • 收稿日期:  2023-11-17
  • 修回日期:  2024-01-14
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