基于生成對抗網(wǎng)絡(luò )的對抗攻擊防御模型

孔銳; 蔡佳純; 黃鋼

doi:10.16383/j.aas.2020.c200033

基于生成對抗網(wǎng)絡(luò )的對抗攻擊防御模型

doi: 10.16383/j.aas.2020.c200033

孔銳^{1, 2,},
蔡佳純^2,,
黃鋼^2, ,

1.
暨南大學(xué)智能科學(xué)與工程學(xué)院珠海 519070
2.
暨南大學(xué)信息科學(xué)技術(shù)學(xué)院廣州 510632

基金項目: 廣東省自然科學(xué)基金(2020A151501718)資助

詳細信息

作者簡(jiǎn)介:
孔銳：暨南大學(xué)智能科學(xué)與工程學(xué)院教授. 主要研究方向為機器學(xué)習, 圖像識別. E-mail: tkongrui@jnu.edu.cn

蔡佳純：暨南大學(xué)學(xué)信息科學(xué)技術(shù)學(xué)院碩士研究生. 主要研究方向為生成對抗網(wǎng)絡(luò ), 模式識別. E-mail: gptcjc1126@163.com

黃鋼：暨南大學(xué)學(xué)信息科學(xué)技術(shù)學(xué)院碩士研究生. 主要研究方向為生成對抗網(wǎng)絡(luò ), 模式識別. 本文通信作者. E-mail: hhhgggpps@gmail.com

通訊作者:
暨南大學(xué)學(xué)信息科學(xué)技術(shù)學(xué)院碩士研究生. 主要研究方向為生成對抗網(wǎng)絡(luò ), 模式識別. 本文通信作者. E-mail: hhhgggpps@gmail.com

計量
- 文章訪(fǎng)問(wèn)數: 2556
- HTML全文瀏覽量: 1537
- 被引次數: 0
出版歷程
- 收稿日期: 2020-01-16
- 修回日期: 2020-05-31

Defense to Adversarial Attack with Generative Adversarial Network

KONG Rui^{1, 2
,},
CAI Jia-Chun^2
,,
HUANG Gang^{2
, ,}

1.
School of Intelligent Systems Science and Engineering, Jinan University (Zhuhai Campus), ZhuHai 519070
2.
College of Information Science and Technology, Jinan University, GuangZhou 510632

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

摘要: 深度神經(jīng)網(wǎng)絡(luò )在解決復雜問(wèn)題方面取得了驚人的成功, 廣泛應用于生活中各個(gè)領(lǐng)域, 但是最近的研究表明, 深度神經(jīng)網(wǎng)絡(luò )容易受到精心設計的對抗樣本的攻擊, 導致網(wǎng)絡(luò )模型輸出錯誤的預測結果, 這對于深度學(xué)習網(wǎng)絡(luò )的安全性是一種極大的挑戰. 對抗攻擊是深度神經(jīng)網(wǎng)絡(luò )發(fā)展過(guò)程中必須克服的一大障礙, 設計一種高效且能夠防御多種對抗攻擊算法, 且具有強魯棒性的防御模型是有效推動(dòng)對抗攻擊防御的方向之一, 探究能否利用對抗性攻擊來(lái)訓練網(wǎng)絡(luò )分類(lèi)器從而提高其魯棒性具有重要意義. 本文將生成對抗網(wǎng)絡(luò )(Generative adversarial networks, GAN)和現有的攻擊算法結合, 提出一種基于生成對抗網(wǎng)絡(luò )的對抗攻擊防御模型(AC-DefGAN), 利用對抗攻擊算法生成攻擊樣本作為GAN的訓練樣本, 同時(shí)在網(wǎng)絡(luò )中加入條件約束來(lái)穩定模型的訓練過(guò)程, 利用分類(lèi)器對生成器所生成樣本的分類(lèi)來(lái)指導GAN的訓練過(guò)程, 通過(guò)自定義分類(lèi)器需要防御的攻擊算法來(lái)生成對抗樣本以完成判別器的訓練, 從而得到能夠防御多種對抗攻擊的分類(lèi)器. 通過(guò)在MNIST、CIFAR-10和ImageNet數據集上進(jìn)行實(shí)驗, 證明訓練完成后, AC-DefGAN可以直接對原始樣本和對抗樣本進(jìn)行正確分類(lèi), 對各類(lèi)對抗攻擊算法達到很好的防御效果, 且比已有方法防御效果好、魯棒性強.
- 對抗攻擊 /
- 生成對抗網(wǎng)絡(luò ) /
- 對抗訓練 /
- 防御模型
Abstract: Deep neural networks have achieved amazing success in solving complex problems and are widely used in various fields of life. However, recent research has shown that deep neural networks are vulnerable to well-designed adversarial samples, which can cause the network model to output incorrect prediction results. This is a great challenge for the security of deep learning networks. Adversarial attacks are a major obstacle that must be overcome during the development of deep neural networks. Designing an efficient and robust defense model that is capable of defending against a variety of adversarial attacks is one of the directions to effectively promote the defense against attacks. It is of great significance to explore whether it is possible to use adversarial attacks to train a network classifier to improve its robustness. In this paper, by combining generative adversarial networks with existing attack algorithms, we propose an adversarial attack defense model AC-DefGAN. We use the adversarial attack model to generate attack samples as training samples, and the classification of samples generated by the generator is used to guide the training process of GAN, and then we add the conditional constraints to stabilize the model training process. Once our model is trained, it can effectively defend the corresponding adversarial attacks. The experimental results on the MNIST, CIFAR-10 and ImageNet datasets show that AC-DefGAN can directly correctly classify the original samples and the adversarial samples after training. It can defend a variety of adversarial attack algorithms, and has better defense effect and robustness than existing defense methods.
- Adversarial attack /
- generative adversarial network /
- adversarial training /
- defense model

HTML全文

圖 1 GAN架構圖

Fig. 1 GAN architecture diagram

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圖 2 AC-DefGAN架構圖

Fig. 2 AC-DefGAN architecture diagram

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圖 3 生成器架構圖

Fig. 3 The structure of generator

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圖 4 判別器架構圖

Fig. 4 The structure of discriminator

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圖 5 ${\Omega _{attack}}$算法流程圖

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

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圖 6 MNIST上d_loss變化趨勢

Fig. 6 Trends of d_loss on MNIST

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圖 7 CIFAR-10上d_loss變化情況

Fig. 7 Trends of d_loss on CIFAR-10

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圖 8 CIFAR-10數據集下AC-DefGAN判別器識別對抗樣本$x_{real}^{adv}$的準確率

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

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表 1 MNIST數據集中各目標網(wǎng)絡(luò )、AC-DefGAN對各類(lèi)對抗樣本的誤分類(lèi)率

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

攻擊方法	VGG11		ResNet-18		Dense-Net40		InceptionV3
攻擊方法	Target model	AC-DefGAN	Target model	AC-DefGAN	Target model	AC-DefGAN	Target model	AC-DefGAN
FGSM(%)	95.31	0.66	87.83	0.58	79.32	0.56	92.91	0.61
BIM(%)	95.70	0.78	88.51	0.69	82.01	0.64	93.12	0.73
DeepFool(%)	96.24	1.42	89.74	1.13	88.61	1.10	93.80	1.25
C&W(%)	99.37	1.79	97.52	1.71	96.21	1.68	98.93	1.75
PGD(%)	98.13	1.61	95.81	1.52	93.26	1.37	97.15	1.58

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表 2 MNIST數據集中AC-DefGAN對各攻擊算法所生成對抗樣本的誤分類(lèi)率

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

攻擊方法	VGG11	ResNet-18	Dense-Net40	InceptionV3
BIM、FGSM(%)	0.69	0.64	0.59	0.67
BIM、DeepFool(%)	1.11	0.91	0.87	1.01
FGSM、DeepFool(%)	1.05	0.86	0.81	0.93
BIM、FGSM、DeepFool(%)	1.01	0.84	0.79	0.89

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表 3 MNIST數據集中AC-DefGAN與各防御模型對各類(lèi)對抗樣本的誤分類(lèi)率

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

攻擊方法	MagNet	Adv. training	APE-GAN_m	Defence-GAN-Rec	AC-DefGAN
FGSM(%)	80.91	18.40	2.80	1.11	0.66
BIM(%)	83.09	19.21	2.91	1.24	0.78
DeepFool(%)	89.93	23.16	2.43	1.53	1.42
C&W(%)	93.18	62.23	1.74	2.29	1.67
粗體表示最優(yōu)值.

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表 4 MNIST數據集中目標網(wǎng)絡(luò )、AC-DefGAN對不同擾動(dòng)閾值$\varepsilon $的FGSM所產(chǎn)生對抗樣本的誤分類(lèi)率(%)

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

FGSM的擾動(dòng)閾值$\varepsilon $	目標網(wǎng)絡(luò )	AC-DefGAN
$\varepsilon = 0.1$	96.29	0.68
$\varepsilon = 0.2$	96.98	0.83
$\varepsilon = 0.3$	97.35	0.91
$\varepsilon = 0.4$	98.76	1.69

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表 5 CIFAR-10數據集中各目標網(wǎng)絡(luò )、AC-DefGAN對各類(lèi)對抗樣本的誤分類(lèi)率

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

攻擊方法	VGG19		ResNet-18		Dense-Net40		InceptionV3
攻擊方法	Target model	AC-DefGAN	Target model	AC-DefGAN	Target model	AC-DefGAN	Target model	AC-DefGAN
FGSM(%)	77.81	16.95	74.92	13.07	73.37	13.74	76.74	15.49
BIM(%)	84.73	19.80	75.74	14.27	76.44	13.83	79.52	18.93
DeepFool(%)	88.52	23.47	83.48	22.55	86.16	21.79	88.26	23.15
C&W(%)	98.94	31.13	92.79	30.24	96.68	29.85	97.43	30.97
PGD(%)	87.13	28.37	86.41	26.29	86.28	25.91	87.04	26.74

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表 6 CIFAR-10數據集中AC-DefGAN對各攻擊算法所生成對抗樣本的誤分類(lèi)率

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

攻擊方法	VGG19	ResNet-18	Dense-Net40	InceptionV3
BIM、FGSM(%)	19.62	13.73	13.18	16.45
BIM、DeepFool(%)	21.71	18.65	17.42	22.14
FGSM、DeepFool(%)	20.95	15.21	16.35	19.78
BIM、FGSM、DeepFool(%)	21.37	17.56	16.93	20.81

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表 7 CIFAR-10數據集中AC-DefGAN與各防御模型對各類(lèi)對抗樣本的誤分類(lèi)率

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

攻擊方法	目標網(wǎng)絡(luò )F	Adv. training	APE-GAN_m	Defence-GAN-Rec	AC-DefGAN
FGSM(%)	82.83	32.68	26.41	22.50	16.91
BIM(%)	89.75	39.49	24.33	21.72	19.83
DeepFool(%)	93.54	44.71	25.29	28.09	25.56
C&W(%)	98.71	78.23	30.50	32.21	30.24
粗體表示最優(yōu)值.

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表 8 CIFAR-10數據集中目標網(wǎng)絡(luò )、AC-DefGAN對不同擾動(dòng)閾值$\varepsilon $的FGSM所產(chǎn)生對抗樣本的誤分類(lèi)率(%)

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

FGSM的擾動(dòng)閾值$\varepsilon $	目標網(wǎng)絡(luò )	AC-DefGAN
$\varepsilon = 0.1$	77.82	12.92
$\varepsilon = 0.2$	80.89	17.47
$\varepsilon = 0.3$	82.33	18.86
$\varepsilon = 0.4$	84.74	24.13

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表 9 ImageNet數據集中各類(lèi)目標網(wǎng)絡(luò )、AC-DefGAN對各類(lèi)對抗樣本的誤分類(lèi)率

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

攻擊方法	VGG19		ResNet-18		Dense-Net40		InceptionV3
攻擊方法	Target model	AC-DefGAN	Target model	AC-DefGAN	Target model	AC-DefGAN	Target model	AC-DefGAN
FGSM(%)	71.21	39.42	69.14	38.52	68.42	37.92	69.65	38.77
DeepFool(%)	88.45	44.80	85.73	42.96	86.24	43.17	87.67	44.63
C&W(%)	97.39	39.13	96.19	36.75	95.84	36.74	96.43	38.68

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表 10 ImageNet數據集中AC-DefGAN與各防御模型對各攻擊算法對抗樣本的誤分類(lèi)率(%)

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

攻擊方法	目標網(wǎng)絡(luò )	APE-GAN_m	AC-DefGAN
FGSM	72.92	40.14	38.94
C&W	97.84	38.70	36.52
BIM	76.79	41.28	40.78
DeepFool	94.71	45.93	44.31
粗體表示最優(yōu)值.

下載: 導出CSV

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基于生成對抗網(wǎng)絡(luò )的對抗攻擊防御模型

doi: 10.16383/j.aas.2020.c200033

通訊作者:
暨南大學(xué)學(xué)信息科學(xué)技術(shù)學(xué)院碩士研究生. 主要研究方向為生成對抗網(wǎng)絡(luò ), 模式識別. 本文通信作者. E-mail: hhhgggpps@gmail.com

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Defense to Adversarial Attack with Generative Adversarial Network

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

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基于生成對抗網(wǎng)絡(luò )的對抗攻擊防御模型

doi: 10.16383/j.aas.2020.c200033

通訊作者: 暨南大學(xué)學(xué)信息科學(xué)技術(shù)學(xué)院碩士研究生. 主要研究方向為生成對抗網(wǎng)絡(luò ), 模式識別. 本文通信作者. E-mail: hhhgggpps@gmail.com

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Defense to Adversarial Attack with Generative Adversarial Network

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

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出版歷程

目錄

通訊作者:
暨南大學(xué)學(xué)信息科學(xué)技術(shù)學(xué)院碩士研究生. 主要研究方向為生成對抗網(wǎng)絡(luò ), 模式識別. 本文通信作者. E-mail: hhhgggpps@gmail.com

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