Adversarial Attack Type I: Cheat Classifiers by Significant Changes

Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. In this paper, we propose another type of adversarial attack that can cheat classifiers by significant changes. For example, we can significantly change a face but...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 43; H. 3; S. 1100 - 1109
Hauptverfasser:	Tang, Sanli, Huang, Xiaolin, Chen, Mingjian, Sun, Chengjin, Yang, Jie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adversarial attack Aerospace electronics Artificial neural networks Classifiers Face recognition Neural networks Permutations Sun supervised variational autoencoder Task analysis Toy manufacturing industry Training type I error
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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Abstract	Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. In this paper, we propose another type of adversarial attack that can cheat classifiers by significant changes. For example, we can significantly change a face but well-trained neural networks still recognize the adversarial and the original example as the same person. Statistically, the existing adversarial attack increases Type II error and the proposed one aims at Type I error, which are hence named as Type II and Type I adversarial attack, respectively. The two types of attack are equally important but are essentially different, which are intuitively explained and numerically evaluated. To implement the proposed attack, a supervised variation autoencoder is designed and then the classifier is attacked by updating the latent variables using gradient information. Besides, with pre-trained generative models, Type I attack on latent spaces is investigated as well. Experimental results show that our method is practical and effective to generate Type I adversarial examples on large-scale image datasets. Most of these generated examples can pass detectors designed for defending Type II attack and the strengthening strategy is only efficient with a specific type attack, both implying that the underlying reasons for Type I and Type II attack are different.
AbstractList	Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. In this paper, we propose another type of adversarial attack that can cheat classifiers by significant changes. For example, we can significantly change a face but well-trained neural networks still recognize the adversarial and the original example as the same person. Statistically, the existing adversarial attack increases Type II error and the proposed one aims at Type I error, which are hence named as Type II and Type I adversarial attack, respectively. The two types of attack are equally important but are essentially different, which are intuitively explained and numerically evaluated. To implement the proposed attack, a supervised variation autoencoder is designed and then the classifier is attacked by updating the latent variables using gradient information. Besides, with pre-trained generative models, Type I attack on latent spaces is investigated as well. Experimental results show that our method is practical and effective to generate Type I adversarial examples on large-scale image datasets. Most of these generated examples can pass detectors designed for defending Type II attack and the strengthening strategy is only efficient with a specific type attack, both implying that the underlying reasons for Type I and Type II attack are different.Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. In this paper, we propose another type of adversarial attack that can cheat classifiers by significant changes. For example, we can significantly change a face but well-trained neural networks still recognize the adversarial and the original example as the same person. Statistically, the existing adversarial attack increases Type II error and the proposed one aims at Type I error, which are hence named as Type II and Type I adversarial attack, respectively. The two types of attack are equally important but are essentially different, which are intuitively explained and numerically evaluated. To implement the proposed attack, a supervised variation autoencoder is designed and then the classifier is attacked by updating the latent variables using gradient information. Besides, with pre-trained generative models, Type I attack on latent spaces is investigated as well. Experimental results show that our method is practical and effective to generate Type I adversarial examples on large-scale image datasets. Most of these generated examples can pass detectors designed for defending Type II attack and the strengthening strategy is only efficient with a specific type attack, both implying that the underlying reasons for Type I and Type II attack are different. Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. In this paper, we propose another type of adversarial attack that can cheat classifiers by significant changes. For example, we can significantly change a face but well-trained neural networks still recognize the adversarial and the original example as the same person. Statistically, the existing adversarial attack increases Type II error and the proposed one aims at Type I error, which are hence named as Type II and Type I adversarial attack, respectively. The two types of attack are equally important but are essentially different, which are intuitively explained and numerically evaluated. To implement the proposed attack, a supervised variation autoencoder is designed and then the classifier is attacked by updating the latent variables using gradient information. Besides, with pre-trained generative models, Type I attack on latent spaces is investigated as well. Experimental results show that our method is practical and effective to generate Type I adversarial examples on large-scale image datasets. Most of these generated examples can pass detectors designed for defending Type II attack and the strengthening strategy is only efficient with a specific type attack, both implying that the underlying reasons for Type I and Type II attack are different.
Author	Tang, Sanli Huang, Xiaolin Yang, Jie Chen, Mingjian Sun, Chengjin
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References	ref35 szegedy (ref9) 2014 ref12 goodfellow (ref6) 2015 ref36 kingma (ref34) 2015 ref31 ref11 ref32 chen (ref13) 2018 ref1 ref39 krizhevsky (ref2) 2012 baluja (ref19) 2018 huang (ref38) 2007 wang (ref10) 2017 ref18 huang (ref22) 2018 madry (ref14) 2018 lample (ref30) 2017 dai (ref29) 2017 ref23 ref26 ref25 ref41 ref21 song (ref15) 2018 kannan (ref40) 2018 kingma (ref5) 2014 makhzani (ref16) 2015 kingma (ref28) 2014 berthelot (ref33) 2017 ref8 ref7 lee (ref20) 2017 ref3 goodfellow (ref4) 2014 tramer (ref42) 2018 mirza (ref27) 2014 odena (ref17) 2017 gilmer (ref24) 2018 abadi (ref37) 2016
References_xml	– ident: ref8 doi: 10.1109/TNNLS.2018.2886017 – ident: ref41 doi: 10.1109/5.726791 – year: 2018 ident: ref40 article-title: Adversarial logit pairing – ident: ref7 doi: 10.4204/EPTCS.257.3 – year: 2015 ident: ref16 article-title: Adversarial autoencoders – ident: ref31 doi: 10.1109/CVPR.2017.645 – start-page: 5967 year: 2017 ident: ref30 article-title: Fader networks: Manipulating images by sliding attributes publication-title: Proc Int Conf Neural Inf Process – ident: ref21 doi: 10.24963/ijcai.2018/543 – ident: ref11 doi: 10.1109/SP.2016.41 – ident: ref12 doi: 10.1109/SP.2017.49 – start-page: 2642 year: 2017 ident: ref17 article-title: Conditional image synthesis with auxiliary classifier GANs publication-title: Proc Int Conf Mach Learn – start-page: 2672 year: 2014 ident: ref4 article-title: Generative adversarial nets publication-title: Proc Int Conf Neural Inf Process – year: 2017 ident: ref20 article-title: Generative adversarial trainer: Defense to adversarial perturbations with GAN – start-page: 2687 year: 2018 ident: ref19 article-title: Learning to Attack: Adversarial Transformation Networks publication-title: Proc 32rd AAAI Conf Artif Intell – ident: ref26 doi: 10.1201/9781351251389-8 – start-page: 10 year: 2018 ident: ref13 article-title: EAD: Elastic-net attacks to deep neural networks via adversarial examples publication-title: Proc 32nd AAAI Conf Artif Intell – year: 2015 ident: ref34 article-title: Adam: A method for stochastic optimization publication-title: Proc Int Conf Learn Representations – year: 2017 ident: ref10 article-title: A theoretical framework for robustness of (deep) classifiers against adversarial examples publication-title: Proc Int Conf Learn Representations Workshop – year: 2014 ident: ref5 article-title: Auto-encoding variational Bayes publication-title: Proc Int Conf Learn Representations – start-page: 52 year: 2018 ident: ref22 article-title: IntroVAE: Introspective variational autoencoders for photographic image synthesis publication-title: Proc Int Conf Neural Inf Process – ident: ref25 doi: 10.1109/CVPR.2016.282 – ident: ref18 doi: 10.1109/CVPR.2019.00453 – year: 2018 ident: ref42 article-title: Ensemble adversarial training: Attacks and defenses publication-title: Proc Int Conf Learn Representations – ident: ref36 doi: 10.1109/ICCV.2015.425 – ident: ref39 doi: 10.14722/ndss.2018.23198 – year: 2018 ident: ref14 article-title: Towards deep learning models resistant to adversarial attacks publication-title: Proc Int Conf Learn Representations – year: 2007 ident: ref38 article-title: Labeled faces in the wild: A database for studying face recognition in unconstrained environments – ident: ref3 doi: 10.1109/CVPR.2017.660 – ident: ref1 doi: 10.1109/CVPR.2016.90 – year: 2016 ident: ref37 article-title: TensorFlow: Large-scale machine learning on heterogeneous distributed systems publication-title: arXiv 1603 04467 – ident: ref35 doi: 10.1109/5.726791 – year: 2017 ident: ref33 article-title: BEGAN: Boundary equilibrium generative adversarial networks publication-title: arXiv 1703 10717 – year: 2015 ident: ref6 article-title: Explaining and harnessing adversarial examples publication-title: Proc Int Conf Learn Representations – start-page: 3581 year: 2014 ident: ref28 article-title: Semi-supervised learning with deep generative models publication-title: Proc Int Conf Neural Inf Process – start-page: 8312 year: 2018 ident: ref15 article-title: Constructing unrestricted adversarial examples with generative models publication-title: Proc Int Conf Neural Inf Process – start-page: 6513 year: 2017 ident: ref29 article-title: Good semi-supervised learning that requires a bad GAN publication-title: Proc Int Conf Neural Inf Process – ident: ref32 doi: 10.1109/CVPR.2015.7298682 – year: 2014 ident: ref9 article-title: Intriguing properties of neural networks publication-title: Proc Int Conf Learn Representations – start-page: 1097 year: 2012 ident: ref2 article-title: ImageNet classification with deep convolutional neural networks publication-title: Proc Int Conf Neural Inf Process – year: 2018 ident: ref24 article-title: Adversarial spheres – ident: ref23 doi: 10.1109/CVPR.2015.7298640 – year: 2014 ident: ref27 article-title: Conditional generative adversarial nets publication-title: arXiv preprint arXiv 1411 1784
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SubjectTerms	Adversarial attack Aerospace electronics Artificial neural networks Classifiers Face recognition Neural networks Permutations Sun supervised variational autoencoder Task analysis Toy manufacturing industry Training type I error
Title	Adversarial Attack Type I: Cheat Classifiers by Significant Changes
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