用于白盒目标攻击的GAN对抗样本生成

doi:10.16451/j.cnki.issn1003-6059.202009007

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Abstract Deep neural networks(DNNs) are easily affected by adversarial examples and consequently generate wrong outputs. Adversarial examples are generated by the traditional methods from an optimization perspective. In this paper, a method for generating adversarial examples is proposed with generative adversarial network(GAN) and GAN is exploited for target attack in the white-box setting. Adversarial perturbations are generated by a trained generator to form adversarial examples. Four kinds of loss functions are utilized to constrain the quality of adversarial examples and improve attack success rates. The effectiveness of the proposed method is testified through extensive experiments on MNIST, CIFAR-10 and ImageNet datasets and the proposed method produces higher attack success rates.

Key words： Adversarial Example Generative Adversarial Network(GAN) Target Attack White-Box Attack

Received: 15 June 2020

ZTFLH:

TP 181

Fund:National Natural Science Foundation of China(No.61673396)

Corresponding Authors: SHAO Mingwen, Ph.D., professor. His research interests include rough sets, granular computing and deep learning.

About author:: ZHANG Gaozhi, master student. His research interests include adversarial machine learning.LIU Xinping, Ph.D., associate professor. His research interests include computer inte-lligent control and intelligent information processing.

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	ZHANG Gaozhi
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Cite this article:

ZHANG Gaozhi,LIU Xinping,SHAO Mingwen. Generating Adversarial Example with GAN for White-Box Target Attacks[J]. , 2020, 33(9): 830-838.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202009007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I9/830

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