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基于生成对抗网络的对抗攻击防御模型

孔锐 蔡佳纯 黄钢

孔锐, 蔡佳纯, 黄钢. 基于生成对抗网络的对抗攻击防御模型. 自动化学报, 2020, 41(x): 1−17 doi: 10.16383/j.aas.2020.c200033
引用本文: 孔锐, 蔡佳纯, 黄钢. 基于生成对抗网络的对抗攻击防御模型. 自动化学报, 2020, 41(x): 1−17 doi: 10.16383/j.aas.2020.c200033
Kong Rui, Cai Jia-Chun, Huang Gang. Defense to adversarial attack with generative adversarial network. Acta Automatica Sinica, 2020, 41(x): 1−17 doi: 10.16383/j.aas.2020.c200033
Citation: Kong Rui, Cai Jia-Chun, Huang Gang. Defense to adversarial attack with generative adversarial network. Acta Automatica Sinica, 2020, 41(x): 1−17 doi: 10.16383/j.aas.2020.c200033

基于生成对抗网络的对抗攻击防御模型

doi: 10.16383/j.aas.2020.c200033
基金项目: 广东省自然科学基金(2020A151501718)资助
详细信息
    作者简介:

    孔锐:暨南大学智能科学与工程学院教授. 主要研究方向为机器学习, 图像识别. E-mail: tkongrui@jnu.edu.cn

    蔡佳纯:暨南大学学信息科学技术学院硕士研究生. 主要研究方向为生成对抗网络, 模式识别. E-mail: gptcjc1126@163.com

    黄钢:暨南大学学信息科学技术学院硕士研究生. 主要研究方向为生成对抗网络, 模式识别. 本文通信作者. E-mail: hhhgggpps@gmail.com

    通讯作者:

    暨南大学学信息科学技术学院硕士研究生. 主要研究方向为生成对抗网络, 模式识别. 本文通信作者. E-mail: hhhgggpps@gmail.com

Defense to Adversarial Attack with Generative Adversarial Network

Funds: Supported by Natural Science Foundation of Guangdong Province, P. R. China (2020A151501718)
More Information
    Corresponding author: HUANG Gang Master student at the College of Information Science and Technology, Jinan University. His research interest covers generative adversarial networks, pattern recognition. Corresponding author of this paper
  • 摘要: 深度神经网络在解决复杂问题方面取得了惊人的成功, 广泛应用于生活中各个领域, 但是最近的研究表明, 深度神经网络容易受到精心设计的对抗样本的攻击, 导致网络模型输出错误的预测结果, 这对于深度学习网络的安全性是一种极大的挑战. 对抗攻击是深度神经网络发展过程中必须克服的一大障碍, 设计一种高效且能够防御多种对抗攻击算法, 且具有强鲁棒性的防御模型是有效推动对抗攻击防御的方向之一, 探究能否利用对抗性攻击来训练网络分类器从而提高其鲁棒性具有重要意义. 本文将生成对抗网络(Generative adversarial networks, GAN)和现有的攻击算法结合, 提出一种基于生成对抗网络的对抗攻击防御模型(AC-DefGAN), 利用对抗攻击算法生成攻击样本作为GAN的训练样本, 同时在网络中加入条件约束来稳定模型的训练过程, 利用分类器对生成器所生成样本的分类来指导GAN的训练过程, 通过自定义分类器需要防御的攻击算法来生成对抗样本以完成判别器的训练, 从而得到能够防御多种对抗攻击的分类器. 通过在MNIST、CIFAR-10和ImageNet数据集上进行实验, 证明训练完成后, AC-DefGAN可以直接对原始样本和对抗样本进行正确分类, 对各类对抗攻击算法达到很好的防御效果, 且比已有方法防御效果好、鲁棒性强.
  • 图  1  GAN架构图

    Fig.  1  GAN architecture diagram

    图  2  AC-DefGAN架构图

    Fig.  2  AC-DefGAN architecture diagram

    图  3  生成器架构图

    Fig.  3  The structure of generator

    图  4  判别器架构图

    Fig.  4  The structure of discriminator

    图  5  ${\Omega _{attack}}$算法流程图

    Fig.  5  Algorithm flowchart of ${\Omega _{attack}}$

    图  6  MNIST上d_loss变化趋势

    Fig.  6  Trends of d_loss on MNIST

    图  7  CIFAR-10上d_loss变化情况

    Fig.  7  Trends of d_loss on CIFAR-10

    图  8  CIFAR-10数据集下AC-DefGAN判别器识别对抗样本$x_{real}^{adv}$的准确率

    Fig.  8  Accuracy of AC-DefGAN discriminator in identifying adversarial samples$x_{real}^{adv}$

    表  1  MNIST数据集中各目标网络、AC-DefGAN对各类对抗样本的误分类率

    Table  1  Misclassification rates of various adversarial examples for target models and AC-DefGAN on MNIST

    攻击方法VGG11ResNet-18Dense-Net40InceptionV3
    Target modelAC-DefGANTarget modelAC-DefGANTarget modelAC-DefGANTarget modelAC-DefGAN
    FGSM(%)95.310.6687.830.5879.320.5692.910.61
    BIM(%)95.700.7888.510.6982.010.6493.120.73
    DeepFool(%)96.241.4289.741.1388.611.1093.801.25
    C&W(%)99.371.7997.521.7196.211.6898.931.75
    PGD(%)98.131.6195.811.5293.261.3797.151.58
    下载: 导出CSV

    表  2  MNIST数据集中AC-DefGAN对各攻击算法所生成对抗样本的误分类率

    Table  2  Misclassification rates of multiple adversarial examples for AC-DefGAN on MNIST

    攻击方法VGG11ResNet-18Dense-Net40InceptionV3
    BIM、FGSM(%)0.690.640.590.67
    BIM、DeepFool(%)1.110.910.871.01
    FGSM、DeepFool(%)1.050.860.810.93
    BIM、FGSM、DeepFool(%)1.010.840.790.89
    下载: 导出CSV

    表  3  MNIST数据集中AC-DefGAN与各防御模型对各类对抗样本的误分类率

    Table  3  Misclassification rates of various adversarial examplesfor AC-DefGAN and other defense strategies on MNIST

    攻击方法MagNetAdv. trainingAPE-GANmDefence-GAN-RecAC-DefGAN
    FGSM(%)80.9118.402.801.110.66
    BIM(%)83.0919.212.911.240.78
    DeepFool(%)89.9323.162.431.531.42
    C&W(%)93.1862.231.742.291.67
    粗体表示最优值.
    下载: 导出CSV

    表  4  MNIST数据集中目标网络、AC-DefGAN对不同扰动阈值$\varepsilon $的FGSM所产生对抗样本的误分类率(%)

    Table  4  Misclassification rates of adversarial examples generated from FGSM with different $\varepsilon $ for target model and AC-DefGAN on MNIST (%)

    FGSM的扰动阈值$\varepsilon $目标网络AC-DefGAN
    $\varepsilon = 0.1$96.290.68
    $\varepsilon = 0.2$96.980.83
    $\varepsilon = 0.3$97.350.91
    $\varepsilon = 0.4$98.761.69
    下载: 导出CSV

    表  5  CIFAR-10数据集中各目标网络、AC-DefGAN对各类对抗样本的误分类率

    Table  5  Misclassification rates of various adversarial examples for target models and AC-DefGAN on CIFAR-10

    攻击方法VGG19ResNet-18Dense-Net40InceptionV3
    Target modelAC-DefGANTarget modelAC-DefGANTarget modelAC-DefGANTarget modelAC-DefGAN
    FGSM(%)77.8116.9574.9213.0773.3713.7476.7415.49
    BIM(%)84.7319.8075.7414.2776.4413.8379.5218.93
    DeepFool(%)88.5223.4783.4822.5586.1621.7988.2623.15
    C&W(%)98.9431.1392.7930.2496.6829.8597.4330.97
    PGD(%)87.1328.3786.4126.2986.2825.9187.0426.74
    下载: 导出CSV

    表  6  CIFAR-10数据集中AC-DefGAN对各攻击算法所生成对抗样本的误分类率

    Table  6  Misclassification rates of multiple adversarial examples for AC-DefGAN on CIFAR-10

    攻击方法VGG19ResNet-18Dense-Net40InceptionV3
    BIM、FGSM(%)19.6213.7313.1816.45
    BIM、DeepFool(%)21.7118.6517.4222.14
    FGSM、DeepFool(%)20.9515.2116.3519.78
    BIM、FGSM、DeepFool(%)21.3717.5616.9320.81
    下载: 导出CSV

    表  7  CIFAR-10数据集中AC-DefGAN与各防御模型对各类对抗样本的误分类率

    Table  7  Misclassification rates of various adversarial examplesfor AC-DefGAN and other defense strategies on CIFAR-10

    攻击方法目标网络FAdv. trainingAPE-GANmDefence-GAN-RecAC-DefGAN
    FGSM(%)82.8332.6826.4122.5016.91
    BIM(%)89.7539.4924.3321.7219.83
    DeepFool(%)93.5444.7125.2928.0925.56
    C&W(%)98.7178.2330.5032.2130.24
    粗体表示最优值.
    下载: 导出CSV

    表  8  CIFAR-10数据集中目标网络、AC-DefGAN对不同扰动阈值$\varepsilon $的FGSM所产生对抗样本的误分类率(%)

    Table  8  Misclassification rates of adversarial examples generated from FGSM with different $\varepsilon $ for target model and AC-DefGAN on CIFAR-10 (%)

    FGSM的扰动阈值$\varepsilon $目标网络AC-DefGAN
    $\varepsilon = 0.1$77.8212.92
    $\varepsilon = 0.2$80.8917.47
    $\varepsilon = 0.3$82.3318.86
    $\varepsilon = 0.4$84.7424.13
    下载: 导出CSV

    表  9  ImageNet数据集中各类目标网络、AC-DefGAN对各类对抗样本的误分类率

    Table  9  Misclassification rates of various adversarial examples for target models and AC-DefGAN on ImageNet

    攻击方法VGG19ResNet-18Dense-Net40InceptionV3
    Target modelAC-DefGANTarget modelAC-DefGANTarget modelAC-DefGANTarget modelAC-DefGAN
    FGSM(%)71.2139.4269.1438.5268.4237.9269.6538.77
    DeepFool(%)88.4544.8085.7342.9686.2443.1787.6744.63
    C&W(%)97.3939.1396.1936.7595.8436.7496.4338.68
    下载: 导出CSV

    表  10  ImageNet数据集中AC-DefGAN与各防御模型对各攻击算法对抗样本的误分类率(%)

    Table  10  Misclassification rates of various adversarial examples for AC-DefGAN and other defense strategies on ImageNet (%)

    攻击方法目标网络APE-GANmAC-DefGAN
    FGSM72.9240.1438.94
    C&W97.8438.7036.52
    BIM76.7941.2840.78
    DeepFool94.7145.9344.31
    粗体表示最优值.
    下载: 导出CSV
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