Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network

Deep neural networks (DNNs) are widely used for image recognition, speech recognition, pattern analysis, and intrusion detection. Recently, the adversarial example attack, in which the input data are only slightly modified, although not an issue for human interpretation, is a serious threat to a DNN...

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Autores principales:	Hyun Kwon, Yongchul Kim, Ki-Woong PARK, Hyunsoo Yoon, Daeseon Choi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2018
Materias:	Deep neural network (DNN) evasion attack adversarial example machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/d847cd17c9f642d58113ec58df1a3762
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id	oai:doaj.org-article:d847cd17c9f642d58113ec58df1a3762
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spelling	oai:doaj.org-article:d847cd17c9f642d58113ec58df1a37622021-11-19T00:02:09ZMulti-Targeted Adversarial Example in Evasion Attack on Deep Neural Network2169-353610.1109/ACCESS.2018.2866197https://doaj.org/article/d847cd17c9f642d58113ec58df1a37622018-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8439941/https://doaj.org/toc/2169-3536Deep neural networks (DNNs) are widely used for image recognition, speech recognition, pattern analysis, and intrusion detection. Recently, the adversarial example attack, in which the input data are only slightly modified, although not an issue for human interpretation, is a serious threat to a DNN as an attack as it causes the machine to misinterpret the data. The adversarial example attack has been receiving considerable attention owing to its potential threat to machine learning. It is divided into two categories: targeted adversarial example and untargeted adversarial example. The untargeted adversarial example happens when machines misclassify an object into an incorrect class. In contrast, the targeted adversarial example attack causes machines to misinterpret the image as the attacker’s desired class. Thus, the latter is a more elaborate and powerful attack than the former. The existing targeted adversarial example is a single targeted attack that allows only one class to be recognized. However, in some cases, a multi-targeted adversarial example can be useful for an attacker to make multiple models recognize a single original image as different classes. For example, an attacker can use a single road sign generated by a multi-targeted adversarial example scheme to make model A recognize it as a stop sign and model B recognize it as a left turn, whereas a human might recognize it as a right turn. Therefore, in this paper, we propose a multi-targeted adversarial example that attacks multiple models within each target class with a single modified image. To produce such examples, we carried out a transformation to maximize the probability of different target classes by multiple models. We used the MNIST datasets and TensorFlow library for our experiment. The experimental results showed that the proposed scheme for generating a multi-targeted adversarial example achieved a 100% attack success rate.Hyun KwonYongchul KimKi-Woong PARKHyunsoo YoonDaeseon ChoiIEEEarticleDeep neural network (DNN)evasion attackadversarial examplemachine learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 6, Pp 46084-46096 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep neural network (DNN) evasion attack adversarial example machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Deep neural network (DNN) evasion attack adversarial example machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hyun Kwon Yongchul Kim Ki-Woong PARK Hyunsoo Yoon Daeseon Choi Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network
description	Deep neural networks (DNNs) are widely used for image recognition, speech recognition, pattern analysis, and intrusion detection. Recently, the adversarial example attack, in which the input data are only slightly modified, although not an issue for human interpretation, is a serious threat to a DNN as an attack as it causes the machine to misinterpret the data. The adversarial example attack has been receiving considerable attention owing to its potential threat to machine learning. It is divided into two categories: targeted adversarial example and untargeted adversarial example. The untargeted adversarial example happens when machines misclassify an object into an incorrect class. In contrast, the targeted adversarial example attack causes machines to misinterpret the image as the attacker’s desired class. Thus, the latter is a more elaborate and powerful attack than the former. The existing targeted adversarial example is a single targeted attack that allows only one class to be recognized. However, in some cases, a multi-targeted adversarial example can be useful for an attacker to make multiple models recognize a single original image as different classes. For example, an attacker can use a single road sign generated by a multi-targeted adversarial example scheme to make model A recognize it as a stop sign and model B recognize it as a left turn, whereas a human might recognize it as a right turn. Therefore, in this paper, we propose a multi-targeted adversarial example that attacks multiple models within each target class with a single modified image. To produce such examples, we carried out a transformation to maximize the probability of different target classes by multiple models. We used the MNIST datasets and TensorFlow library for our experiment. The experimental results showed that the proposed scheme for generating a multi-targeted adversarial example achieved a 100% attack success rate.
format	article
author	Hyun Kwon Yongchul Kim Ki-Woong PARK Hyunsoo Yoon Daeseon Choi
author_facet	Hyun Kwon Yongchul Kim Ki-Woong PARK Hyunsoo Yoon Daeseon Choi
author_sort	Hyun Kwon
title	Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network
title_short	Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network
title_full	Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network
title_fullStr	Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network
title_full_unstemmed	Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network
title_sort	multi-targeted adversarial example in evasion attack on deep neural network
publisher	IEEE
publishDate	2018
url	https://doaj.org/article/d847cd17c9f642d58113ec58df1a3762
work_keys_str_mv	AT hyunkwon multitargetedadversarialexampleinevasionattackondeepneuralnetwork AT yongchulkim multitargetedadversarialexampleinevasionattackondeepneuralnetwork AT kiwoongpark multitargetedadversarialexampleinevasionattackondeepneuralnetwork AT hyunsooyoon multitargetedadversarialexampleinevasionattackondeepneuralnetwork AT daeseonchoi multitargetedadversarialexampleinevasionattackondeepneuralnetwork
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Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network

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