Reinforced adversarial attacks on deep neural networks using ADMM

Pu Zhao; Kaidi Xu; Tianyun Zhang; Makan Fardad; Yanzhi Wang; Xue Lin

doi:10.1109/GlobalSIP.2018.8646651

Reinforced adversarial attacks on deep neural networks using ADMM

Pu Zhao, Kaidi Xu, Tianyun Zhang, Makan Fardad, Yanzhi Wang, Xue Lin

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Entry/Poem › Conference contribution

2 Scopus citations

Abstract

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 CW attacks. Our ADMM attack is also able to break defenses such as defensive distillation and adversarial training, and provide strong attack transferability.

Original language	English (US)
Title of host publication	2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1169-1173
Number of pages	5
ISBN (Electronic)	9781728112954
DOIs	https://doi.org/10.1109/GlobalSIP.2018.8646651
State	Published - Jul 2 2018
Event	2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States Duration: Nov 26 2018 → Nov 29 2018

Publication series

Name	2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Conference

Conference	2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
Country/Territory	United States
City	Anaheim
Period	11/26/18 → 11/29/18

Keywords

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

ASJC Scopus subject areas

Information Systems
Signal Processing

Access to Document

10.1109/GlobalSIP.2018.8646651

Cite this

Zhao, P., Xu, K., Zhang, T., Fardad, M., Wang, Y., & Lin, X. (2018). Reinforced adversarial attacks on deep neural networks using ADMM. In 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings (pp. 1169-1173). Article 8646651 (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2018.8646651

Reinforced adversarial attacks on deep neural networks using ADMM. / Zhao, Pu; Xu, Kaidi; Zhang, Tianyun et al.
2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1169-1173 8646651 (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings).

Research output: Chapter in Book/Entry/Poem › Conference contribution

Zhao, P, Xu, K, Zhang, T, Fardad, M, Wang, Y & Lin, X 2018, Reinforced adversarial attacks on deep neural networks using ADMM. in 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings., 8646651, 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1169-1173, 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018, Anaheim, United States, 11/26/18. https://doi.org/10.1109/GlobalSIP.2018.8646651

Zhao P, Xu K, Zhang T, Fardad M, Wang Y, Lin X. Reinforced adversarial attacks on deep neural networks using ADMM. In 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1169-1173. 8646651. (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings). doi: 10.1109/GlobalSIP.2018.8646651

Zhao, Pu ; Xu, Kaidi ; Zhang, Tianyun et al. / Reinforced adversarial attacks on deep neural networks using ADMM. 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1169-1173 (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings).

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title = "Reinforced adversarial attacks on deep neural networks using ADMM",

abstract = "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 CW attacks. Our ADMM attack is also able to break defenses such as defensive distillation and adversarial training, and provide strong attack transferability.",

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author = "Pu Zhao and Kaidi Xu and Tianyun Zhang and Makan Fardad and Yanzhi Wang and Xue Lin",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 ; Conference date: 26-11-2018 Through 29-11-2018",

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AB - 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 CW attacks. Our ADMM attack is also able to break defenses such as defensive distillation and adversarial training, and provide strong attack transferability.

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Reinforced adversarial attacks on deep neural networks using ADMM

Abstract

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Conference

Keywords

ASJC Scopus subject areas

Access to Document

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