Adversarial attack and defense in reinforcement learning-from AI security view

Abstract Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV). Therefore, a reliable RL system is the foundation for the security critical applications...

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Main Authors:	Tong Chen, Jiqiang Liu, Yingxiao Xiang, Wenjia Niu, Endong Tong, Zhen Han
Format:	Article
Language:	English
Published:	SpringerOpen 2019-03-01
Series:	Cybersecurity
Subjects:	Reinforcement learning Artificial intelligence Security Adversarial attack Adversarial example Defense
Online Access:	http://link.springer.com/article/10.1186/s42400-019-0027-x

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spelling	doaj-d7e8aae0c60b43ea86a71372a118e3d42020-11-25T02:24:19ZengSpringerOpenCybersecurity2523-32462019-03-012112210.1186/s42400-019-0027-xAdversarial attack and defense in reinforcement learning-from AI security viewTong Chen0Jiqiang Liu1Yingxiao Xiang2Wenjia Niu3Endong Tong4Zhen Han5Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong UniversityBeijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong UniversityBeijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong UniversityBeijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong UniversityBeijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong UniversityBeijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong UniversityAbstract Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV). Therefore, a reliable RL system is the foundation for the security critical applications in AI, which has attracted a concern that is more critical than ever. However, recent studies discover that the interesting attack mode adversarial attack also be effective when targeting neural network policies in the context of reinforcement learning, which has inspired innovative researches in this direction. Hence, in this paper, we give the very first attempt to conduct a comprehensive survey on adversarial attacks in reinforcement learning under AI security. Moreover, we give briefly introduction on the most representative defense technologies against existing adversarial attacks.http://link.springer.com/article/10.1186/s42400-019-0027-xReinforcement learningArtificial intelligenceSecurityAdversarial attackAdversarial exampleDefense
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Tong Chen Jiqiang Liu Yingxiao Xiang Wenjia Niu Endong Tong Zhen Han
spellingShingle	Tong Chen Jiqiang Liu Yingxiao Xiang Wenjia Niu Endong Tong Zhen Han Adversarial attack and defense in reinforcement learning-from AI security view Cybersecurity Reinforcement learning Artificial intelligence Security Adversarial attack Adversarial example Defense
author_facet	Tong Chen Jiqiang Liu Yingxiao Xiang Wenjia Niu Endong Tong Zhen Han
author_sort	Tong Chen
title	Adversarial attack and defense in reinforcement learning-from AI security view
title_short	Adversarial attack and defense in reinforcement learning-from AI security view
title_full	Adversarial attack and defense in reinforcement learning-from AI security view
title_fullStr	Adversarial attack and defense in reinforcement learning-from AI security view
title_full_unstemmed	Adversarial attack and defense in reinforcement learning-from AI security view
title_sort	adversarial attack and defense in reinforcement learning-from ai security view
publisher	SpringerOpen
series	Cybersecurity
issn	2523-3246
publishDate	2019-03-01
description	Abstract Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV). Therefore, a reliable RL system is the foundation for the security critical applications in AI, which has attracted a concern that is more critical than ever. However, recent studies discover that the interesting attack mode adversarial attack also be effective when targeting neural network policies in the context of reinforcement learning, which has inspired innovative researches in this direction. Hence, in this paper, we give the very first attempt to conduct a comprehensive survey on adversarial attacks in reinforcement learning under AI security. Moreover, we give briefly introduction on the most representative defense technologies against existing adversarial attacks.
topic	Reinforcement learning Artificial intelligence Security Adversarial attack Adversarial example Defense
url	http://link.springer.com/article/10.1186/s42400-019-0027-x
work_keys_str_mv	AT tongchen adversarialattackanddefenseinreinforcementlearningfromaisecurityview AT jiqiangliu adversarialattackanddefenseinreinforcementlearningfromaisecurityview AT yingxiaoxiang adversarialattackanddefenseinreinforcementlearningfromaisecurityview AT wenjianiu adversarialattackanddefenseinreinforcementlearningfromaisecurityview AT endongtong adversarialattackanddefenseinreinforcementlearningfromaisecurityview AT zhenhan adversarialattackanddefenseinreinforcementlearningfromaisecurityview
_version_	1724856259573710848

Adversarial attack and defense in reinforcement learning-from AI security view

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