Reinforced Adversarial Attacks on Deep Neural Networks Using ADMM

被引：0

作者：

Zhao, Pu ^{[1
]}

Xu, Kaidi ^{[1
]}

Zhang, Tianyun ^{[2
]}

Fardad, Makan ^{[2
]}

Wang, Yanzhi ^{[1
]}

Lin, Xue ^{[1
]}

机构：

[1] Northeastern Univ, Dept Elect & Comp Engn, Boston, MA 02115 USA

[2] Syracuse Univ, Coll Engn & Comp Sci, Syracuse, NY USA

来源：

2018 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP 2018) | 2018年

基金：

美国国家科学基金会;

关键词：

Deep Neural Networks; Adversarial Attacks; ADMM (Alternating Direction Method of Multipliers);

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

As deep learning penetrates into wide application domains, it is essential to evaluate the robustness of deep neural networks (DNNs) under adversarial attacks, especially for some security-critical applications. To better understand the security properties of DNNs, we propose a general framework for constructing adversarial examples, based on ADMM (Alternating Direction Method of Multipliers). This general framework can be adapted to implement L2 and L0 attacks with minor changes. Our ADMM attacks require less distortion for incorrect classification compared with C&W attacks. Our ADMM attack is also able to break defenses such as defensive distillation and adversarial training, and provide strong attack transferability.

引用

页码：1169 / 1173

页数：5

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