Built on

Y. LeCun, L. Bottou, Y. Bengio, P. Haffner et al. , “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
D. Lowd and C. Meek, “Adversarial learning,” in Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining . ACM, 2005, pp. 641–647
2005
Earlier work this paper cites.
M. Barreno, B. Nelson, A. D. Joseph, and J. D. Tygar, “The security of machine learning,” Machine Learning , vol. 81, no. 2, pp. 121–148, 2010
2010
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics , 2011, pp. 315–323
2011
Earlier work this paper cites.
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli, “Evasion attacks against machine learning at test time,” in Joint European conference on machine learning and knowledge discovery in databases . Springer, 2013, pp. 387–402
2013
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C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” arXiv preprint arXiv:1312.6199 , 2013
Original
2013
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I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples (2014),” arXiv preprint arXiv:1412.6572
Original
2014
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K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014
Original
2014
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I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572 , 2014
Original
2014
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F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao, “Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop,” arXiv preprint arXiv:1506.03365 , 2015
Original
2015
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J. Donahue, P. Krähenbühl, and T. Darrell, “Adversarial feature learning,” arXiv preprint arXiv:1605.09782 , 2016
Original
2016
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A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,” arXiv preprint arXiv:1611.01236 , 2016
Original
2016
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S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2574–2582
2016
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D. Meng and H. Chen, “Magnet: a two-pronged defense against adversarial examples,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2017, pp. 135–147
2017
Earlier work this paper cites.

Similar

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” arXiv preprint arXiv:1706.06083 , 2017
Original
2017
Cited alongside, same era.
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 2017, pp. 39–57
2017
Cited alongside, same era.
J. Rauber, W. Brendel, and M. Bethge, “Foolbox: A python toolbox to benchmark the robustness of machine learning models,” arXiv preprint arXiv:1707.04131 , 2017. [Online]. Available: http://arxiv.org/abs/1707.04131
Original
2017
Cited alongside, same era.
P. Samangouei, M. Kabkab, and R. Chellappa, “Defense-gan: Protecting classifiers against adversarial attacks using generative models,” arXiv preprint arXiv:1805.06605 , 2018
Original
2018
Cited alongside, same era.

Then

T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” arXiv preprint arXiv:1912.04958 , 2019
Original
2019
Later among the works it cites.
X. Yin, S. Kolouri, and G. K. Rohde, “Adversarial example detection and classification with asymmetrical adversarial training,” arXiv preprint arXiv:1905.11475 , 2019
Original
2019
Later among the works it cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 4401–4410
2019
Later among the works it cites.
X. Yuan, P. He, Q. Zhu, and X. Li, “Adversarial examples: Attacks and defenses for deep learning,” IEEE transactions on neural networks and learning systems , vol. 30, no. 9, pp. 2805–2824, 2019
2019
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Reading the bibliography…

2020

Adversarial Defense by Latent Style Transformations

Wang, Shuo, Nepal, Surya, Abuadbba, Alsharif et al.

Understand

Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples.

In this paper, we investigate an attack-agnostic defense against adversarial attacks on high-resolution images by detecting suspicious inputs.
The intuition behind our approach is that the essential characteristics of a normal image are generally consistent with non-essential style transformations, e.g., slightly changing the facial expression of human portraits.
In contrast, adversarial examples are generally sensitive to such transformations.

Built on

Y. LeCun, L. Bottou, Y. Bengio, P. Haffner et al. , “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
D. Lowd and C. Meek, “Adversarial learning,” in Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining . ACM, 2005, pp. 641–647
2005
Earlier work this paper cites.
M. Barreno, B. Nelson, A. D. Joseph, and J. D. Tygar, “The security of machine learning,” Machine Learning , vol. 81, no. 2, pp. 121–148, 2010
2010
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics , 2011, pp. 315–323
2011
Earlier work this paper cites.
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli, “Evasion attacks against machine learning at test time,” in Joint European conference on machine learning and knowledge discovery in databases . Springer, 2013, pp. 387–402
2013
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” arXiv preprint arXiv:1312.6199 , 2013
Original
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples (2014),” arXiv preprint arXiv:1412.6572
Original
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014
Original
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572 , 2014
Original
2014
Earlier work this paper cites.
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao, “Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop,” arXiv preprint arXiv:1506.03365 , 2015
Original
2015
Earlier work this paper cites.
J. Donahue, P. Krähenbühl, and T. Darrell, “Adversarial feature learning,” arXiv preprint arXiv:1605.09782 , 2016
Original
2016
Earlier work this paper cites.
A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,” arXiv preprint arXiv:1611.01236 , 2016
Original
2016
Earlier work this paper cites.
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2574–2582
2016
Earlier work this paper cites.
D. Meng and H. Chen, “Magnet: a two-pronged defense against adversarial examples,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2017, pp. 135–147
2017
Earlier work this paper cites.

Similar

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” arXiv preprint arXiv:1706.06083 , 2017
Original
2017
Cited alongside, same era.
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 2017, pp. 39–57
2017
Cited alongside, same era.
J. Rauber, W. Brendel, and M. Bethge, “Foolbox: A python toolbox to benchmark the robustness of machine learning models,” arXiv preprint arXiv:1707.04131 , 2017. [Online]. Available: http://arxiv.org/abs/1707.04131
Original
2017
Cited alongside, same era.
P. Samangouei, M. Kabkab, and R. Chellappa, “Defense-gan: Protecting classifiers against adversarial attacks using generative models,” arXiv preprint arXiv:1805.06605 , 2018
Original
2018
Cited alongside, same era.

Then

T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” arXiv preprint arXiv:1912.04958 , 2019
Original
2019
Later among the works it cites.
X. Yin, S. Kolouri, and G. K. Rohde, “Adversarial example detection and classification with asymmetrical adversarial training,” arXiv preprint arXiv:1905.11475 , 2019
Original
2019
Later among the works it cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 4401–4410
2019
Later among the works it cites.
X. Yuan, P. He, Q. Zhu, and X. Li, “Adversarial examples: Attacks and defenses for deep learning,” IEEE transactions on neural networks and learning systems , vol. 30, no. 9, pp. 2805–2824, 2019
2019
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

R. Bao, S. Liang, and Q. Wang, “Featurized bidirectional gan: Adversarial defense via adversarially learned semantic inference,” arXiv preprint arXiv:1805.07862 , 2018

Original

2018

Cited alongside, same era.

Y. Song, R. Shu, N. Kushman, and S. Ermon, “Generative adversarial examples,” arXiv preprint arXiv:1805.07894 , 2018

Original

2018

Cited alongside, same era.

S. Tian, G. Yang, and Y. Cai, “Detecting adversarial examples through image transformation,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018

2018

Cited alongside, same era.

S. Dathathri, S. Zheng, T. Yin, R. M. Murray, and Y. Yue, “Detecting adversarial examples via neural fingerprinting,” arXiv preprint arXiv:1803.03870 , 2018

Original

2018

Cited alongside, same era.

C. Xiao, J. Y. Zhu, B. Li, W. He, M. Liu, and D. Song, “Spatially transformed adversarial examples,” in 6th International Conference on Learning Representations, ICLR 2018 , 2018

2018

Cited alongside, same era.

R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 586–595

2018

Cited alongside, same era.

Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li, “Boosting adversarial attacks with momentum,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 9185–9193

2018

Cited alongside, same era.

A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok, “Synthesizing robust adversarial examples,” in International conference on machine learning . PMLR, 2018, pp. 284–293

2018

Cited alongside, same era.

S. Gowal, C. Qin, P.-S. Huang, T. Cemgil, K. Dvijotham, T. Mann, and P. Kohli, “Achieving robustness in the wild via adversarial mixing with disentangled representations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 1211–1220

2020

Closest in time.

E. Collins, R. Bala, B. Price, and S. Susstrunk, “Editing in style: Uncovering the local semantics of gans,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 5771–5780

2020

Closest in time.

E. Härkönen, A. Hertzmann, J. Lehtinen, and S. Paris, “Ganspace: Discovering interpretable gan controls,” arXiv preprint arXiv:2004.02546 , 2020

Original

2020

Closest in time.

Y. Shen, J. Gu, X. Tang, and B. Zhou, “Interpreting the latent space of gans for semantic face editing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9243–9252

2020

Closest in time.

S. Pidhorskyi, D. Adjeroh, and G. Doretto, “Adversarial latent autoencoders,” arXiv preprint arXiv:2004.04467 , 2020

Original

2020

Closest in time.

S. Wen, A. Rios, and L. Itti, “Beneficial perturbations network for defending adversarial examples,” arXiv preprint arXiv:2009.12724 , 2020

Original

2020

Closest in time.

F. Tramer, N. Carlini, W. Brendel, and A. Madry, “On adaptive attacks to adversarial example defenses,” arXiv preprint arXiv:2002.08347 , 2020

Original

2020

Closest in time.

J. Bose, G. Gidel, H. Berard, A. Cianflone, P. Vincent, S. Lacoste-Julien, and W. Hamilton, “Adversarial example games,” Advances in Neural Information Processing Systems , vol. 33, 2020

2020

Closest in time.

Z. Wu, D. Lischinski, and E. Shechtman, “Stylespace analysis: Disentangled controls for stylegan image generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 12 863–12 872

2021

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O. Tov, Y. Alaluf, Y. Nitzan, O. Patashnik, and D. Cohen-Or, “Designing an encoder for stylegan image manipulation,” ACM Transactions on Graphics (TOG) , vol. 40, no. 4, pp. 1–14, 2021

2021

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