Built on

H. Zhang, Y. Yu, J. Jiao, E. P. Xing, L. E. Ghaoui, and M. I. Jordan, “Theoretically principled trade-off between robustness and accuracy,” CoRR , vol. abs/1901.08573, 2019. [Online]. Available: http://arxiv.org/abs/1901.08573
Original
1901
Earlier work this paper cites.
T. Pang, K. Xu, C. Du, N. Chen, and J. Zhu, “Improving adversarial robustness via promoting ensemble diversity,” CoRR , vol. abs/1901.08846, 2019. [Online]. Available: http://arxiv.org/abs/1901.08846
Original
1901
Earlier work this paper cites.
S. Moon, G. An, and H. O. Song, “Parsimonious black-box adversarial attacks via efficient combinatorial optimization,” CoRR , vol. abs/1905.06635, 2019. [Online]. Available: http://arxiv.org/abs/1905.06635
Original
1905
Earlier work this paper cites.
Z. Yan, Y. Guo, and C. Zhang, “Subspace attack: Exploiting promising subspaces for query-efficient black-box attacks,” CoRR , vol. abs/1906.04392, 2019. [Online]. Available: http://arxiv.org/abs/1906.04392
Original
1906
Earlier work this paper cites.
D. V. Vargas and S. Kotyan, “Evolving robust neural architectures to defend from adversarial attacks,” CoRR , vol. abs/1906.11667, 2019. [Online]. Available: http://arxiv.org/abs/1906.11667
Original
1906
Earlier work this paper cites.
Y. Xu, L. Xie, X. Zhang, X. Chen, G. Qi, Q. Tian, and H. Xiong, “PC-DARTS: partial channel connections for memory-efficient differentiable architecture search,” CoRR , vol. abs/1907.05737, 2019. [Online]. Available: http://arxiv.org/abs/1907.05737
Original
1907
Earlier work this paper cites.
Y. Balaji, T. Goldstein, and J. Hoffman, “Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets,” CoRR , vol. abs/1910.08051, 2019. [Online]. Available: http://arxiv.org/abs/1910.08051
Original
1910
Earlier work this paper cites.
N. A. Marlow, “A normal limit theorem for power sums of independent random variables,” The Bell System Technical Journal , vol. 46, no. 9, pp. 2081–2089, 1967
1967
Earlier work this paper cites.
J. Zhang, X. Xu, B. Han, G. Niu, L. Cui, M. Sugiyama, and M. S. Kankanhalli, “Attacks which do not kill training make adversarial learning stronger,” CoRR , vol. abs/2002.11242, 2020. [Online]. Available: https://arxiv.org/abs/2002.11242
Original
2002
Earlier work this paper cites.
F. Croce and M. Hein, “Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,” CoRR , vol. abs/2003.01690, 2020. [Online]. Available: https://arxiv.org/abs/2003.01690
Original
2003
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 , ser. NIPS’12. USA: Curran Associates Inc., 2012, pp. 1097–1105. [Online]. Available: http://dl.acm.org/citation.cfm?id=2999134.2999257
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 1097–1105. [Online]. Available: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
2012
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 e-prints , p. arXiv:1312.6199, Dec 2013
Original
2013
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” CoRR , vol. abs/1409.1556, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556
Original
2014
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate,” arXiv e-prints , p. arXiv:1409.0473, Sep 2014
Original
2014
Earlier work this paper cites.
A. M. Nguyen, J. Yosinski, and J. Clune, “Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,” CoRR , vol. abs/1412.1897, 2014. [Online]. Available: http://arxiv.org/abs/1412.1897
Original
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” 2014
2014
Earlier work this paper cites.
S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” CoRR , vol. abs/1511.04599, 2015. [Online]. Available: http://arxiv.org/abs/1511.04599
Original
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” CoRR , vol. abs/1603.05027, 2016. [Online]. Available: http://arxiv.org/abs/1603.05027
Original
2016
Earlier work this paper cites.
G. Huang, Z. Liu, and K. Q. Weinberger, “Densely connected convolutional networks,” CoRR , vol. abs/1608.06993, 2016. [Online]. Available: http://arxiv.org/abs/1608.06993
Original
2016
Earlier work this paper cites.
S. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard, “Universal adversarial perturbations,” CoRR , vol. abs/1610.08401, 2016. [Online]. Available: http://arxiv.org/abs/1610.08401
Original
2016
Earlier work this paper cites.
B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,” CoRR , vol. abs/1611.01578, 2016. [Online]. Available: http://arxiv.org/abs/1611.01578
Original
2016
Earlier work this paper cites.
B. Baker, O. Gupta, N. Naik, and R. Raskar, “Designing neural network architectures using reinforcement learning,” CoRR , vol. abs/1611.02167, 2016. [Online]. Available: http://arxiv.org/abs/1611.02167
Original
2016
Earlier work this paper cites.
A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,” CoRR , vol. abs/1611.01236, 2016. [Online]. Available: http://arxiv.org/abs/1611.01236
Original
2016
Earlier work this paper cites.

Similar

Then

C. Xiao, B. Li, J. Zhu, W. He, M. Liu, and D. Song, “Generating adversarial examples with adversarial networks,” CoRR , vol. abs/1801.02610, 2018. [Online]. Available: http://arxiv.org/abs/1801.02610
Original
2018
Later among the works it cites.
A. Athalye, N. Carlini, and D. A. Wagner, “Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,” CoRR , vol. abs/1802.00420, 2018. [Online]. Available: http://arxiv.org/abs/1802.00420
Original
2018
Later among the works it cites.
T.-W. Weng, H. Zhang, P.-Y. Chen, J. Yi, D. Su, Y. Gao, C.-J. Hsieh, and L. Daniel, “Evaluating the robustness of neural networks: An extreme value theory approach,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=BkUHlMZ0b
2018
Later among the works it cites.
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean, “Efficient neural architecture search via parameter sharing,” CoRR , vol. abs/1802.03268, 2018. [Online]. Available: http://arxiv.org/abs/1802.03268
Original

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2020

Adversarially Robust Neural Architectures

Dong, Minjing, Li, Yanxi, Wang, Yunhe et al.

Understand

Deep Neural Networks (DNNs) are vulnerable to adversarial attacks.

Existing methods are devoted to developing various robust training strategies or regularizations to update the weights of the neural network.
But beyond the weights, the overall structure and information flow in the network are explicitly determined by the neural architecture, which remains unexplored.
This paper thus aims to improve the adversarial robustness of the network from the architecture perspective.

Built on

H. Zhang, Y. Yu, J. Jiao, E. P. Xing, L. E. Ghaoui, and M. I. Jordan, “Theoretically principled trade-off between robustness and accuracy,” CoRR , vol. abs/1901.08573, 2019. [Online]. Available: http://arxiv.org/abs/1901.08573
Original
1901
Earlier work this paper cites.
T. Pang, K. Xu, C. Du, N. Chen, and J. Zhu, “Improving adversarial robustness via promoting ensemble diversity,” CoRR , vol. abs/1901.08846, 2019. [Online]. Available: http://arxiv.org/abs/1901.08846
Original
1901
Earlier work this paper cites.
S. Moon, G. An, and H. O. Song, “Parsimonious black-box adversarial attacks via efficient combinatorial optimization,” CoRR , vol. abs/1905.06635, 2019. [Online]. Available: http://arxiv.org/abs/1905.06635
Original
1905
Earlier work this paper cites.
Z. Yan, Y. Guo, and C. Zhang, “Subspace attack: Exploiting promising subspaces for query-efficient black-box attacks,” CoRR , vol. abs/1906.04392, 2019. [Online]. Available: http://arxiv.org/abs/1906.04392
Original
1906
Earlier work this paper cites.
D. V. Vargas and S. Kotyan, “Evolving robust neural architectures to defend from adversarial attacks,” CoRR , vol. abs/1906.11667, 2019. [Online]. Available: http://arxiv.org/abs/1906.11667
Original
1906
Earlier work this paper cites.
Y. Xu, L. Xie, X. Zhang, X. Chen, G. Qi, Q. Tian, and H. Xiong, “PC-DARTS: partial channel connections for memory-efficient differentiable architecture search,” CoRR , vol. abs/1907.05737, 2019. [Online]. Available: http://arxiv.org/abs/1907.05737
Original
1907
Earlier work this paper cites.
Y. Balaji, T. Goldstein, and J. Hoffman, “Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets,” CoRR , vol. abs/1910.08051, 2019. [Online]. Available: http://arxiv.org/abs/1910.08051
Original
1910
Earlier work this paper cites.
N. A. Marlow, “A normal limit theorem for power sums of independent random variables,” The Bell System Technical Journal , vol. 46, no. 9, pp. 2081–2089, 1967
1967
Earlier work this paper cites.
J. Zhang, X. Xu, B. Han, G. Niu, L. Cui, M. Sugiyama, and M. S. Kankanhalli, “Attacks which do not kill training make adversarial learning stronger,” CoRR , vol. abs/2002.11242, 2020. [Online]. Available: https://arxiv.org/abs/2002.11242
Original
2002
Earlier work this paper cites.
F. Croce and M. Hein, “Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,” CoRR , vol. abs/2003.01690, 2020. [Online]. Available: https://arxiv.org/abs/2003.01690
Original
2003
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 , ser. NIPS’12. USA: Curran Associates Inc., 2012, pp. 1097–1105. [Online]. Available: http://dl.acm.org/citation.cfm?id=2999134.2999257
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 1097–1105. [Online]. Available: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
2012
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 e-prints , p. arXiv:1312.6199, Dec 2013
Original
2013
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” CoRR , vol. abs/1409.1556, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556
Original
2014
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate,” arXiv e-prints , p. arXiv:1409.0473, Sep 2014
Original
2014
Earlier work this paper cites.
A. M. Nguyen, J. Yosinski, and J. Clune, “Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,” CoRR , vol. abs/1412.1897, 2014. [Online]. Available: http://arxiv.org/abs/1412.1897
Original
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” 2014
2014
Earlier work this paper cites.
S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” CoRR , vol. abs/1511.04599, 2015. [Online]. Available: http://arxiv.org/abs/1511.04599
Original
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” CoRR , vol. abs/1603.05027, 2016. [Online]. Available: http://arxiv.org/abs/1603.05027
Original
2016
Earlier work this paper cites.
G. Huang, Z. Liu, and K. Q. Weinberger, “Densely connected convolutional networks,” CoRR , vol. abs/1608.06993, 2016. [Online]. Available: http://arxiv.org/abs/1608.06993
Original
2016
Earlier work this paper cites.
S. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard, “Universal adversarial perturbations,” CoRR , vol. abs/1610.08401, 2016. [Online]. Available: http://arxiv.org/abs/1610.08401
Original
2016
Earlier work this paper cites.
B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,” CoRR , vol. abs/1611.01578, 2016. [Online]. Available: http://arxiv.org/abs/1611.01578
Original
2016
Earlier work this paper cites.
B. Baker, O. Gupta, N. Naik, and R. Raskar, “Designing neural network architectures using reinforcement learning,” CoRR , vol. abs/1611.02167, 2016. [Online]. Available: http://arxiv.org/abs/1611.02167
Original
2016
Earlier work this paper cites.
A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,” CoRR , vol. abs/1611.01236, 2016. [Online]. Available: http://arxiv.org/abs/1611.01236
Original
2016
Earlier work this paper cites.

Similar

Then

C. Xiao, B. Li, J. Zhu, W. He, M. Liu, and D. Song, “Generating adversarial examples with adversarial networks,” CoRR , vol. abs/1801.02610, 2018. [Online]. Available: http://arxiv.org/abs/1801.02610
Original
2018
Later among the works it cites.
A. Athalye, N. Carlini, and D. A. Wagner, “Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,” CoRR , vol. abs/1802.00420, 2018. [Online]. Available: http://arxiv.org/abs/1802.00420
Original
2018
Later among the works it cites.
T.-W. Weng, H. Zhang, P.-Y. Chen, J. Yi, D. Su, Y. Gao, C.-J. Hsieh, and L. Daniel, “Evaluating the robustness of neural networks: An extreme value theory approach,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=BkUHlMZ0b
2018
Later among the works it cites.
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean, “Efficient neural architecture search via parameter sharing,” CoRR , vol. abs/1802.03268, 2018. [Online]. Available: http://arxiv.org/abs/1802.03268
Original

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Srndic, P. Laskov, G. Giacinto, and F. Roli, “Evasion attacks against machine learning at test time,” CoRR , vol. abs/1708.06131, 2017. [Online]. Available: http://arxiv.org/abs/1708.06131

Original

2017

Cited alongside, same era.

I. Bello, B. Zoph, V. Vasudevan, and Q. V. Le, “Neural optimizer search with reinforcement learning,” CoRR , vol. abs/1709.07417, 2017. [Online]. Available: http://arxiv.org/abs/1709.07417

Original

2017

Cited alongside, same era.

P.-Y. Chen, H. Zhang, Y. Sharma, J. Yi, and C.-J. Hsieh, “Zoo,” Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security - AISec ’17 , 2017. [Online]. Available: http://dx.doi.org/10.1145/3128572.3140448

2017

Cited alongside, same era.

A. Ilyas, L. Engstrom, A. Athalye, and J. Lin, “Query-efficient black-box adversarial examples,” CoRR , vol. abs/1712.07113, 2017. [Online]. Available: http://arxiv.org/abs/1712.07113

Original

2017

Cited alongside, same era.

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” 2017

2017

Cited alongside, same era.

J. H. Metzen, M. C. Kumar, T. Brox, and V. Fischer, “Universal adversarial perturbations against semantic image segmentation,” 2017

2017

Cited alongside, same era.

C. Xie, J. Wang, Z. Zhang, Y. Zhou, L. Xie, and A. L. Yuille, “Adversarial examples for semantic segmentation and object detection,” CoRR , vol. abs/1703.08603, 2017. [Online]. Available: http://arxiv.org/abs/1703.08603

Original

2017

Cited alongside, same era.

N. Papernot and P. D. McDaniel, “Extending defensive distillation,” CoRR , vol. abs/1705.05264, 2017. [Online]. Available: http://arxiv.org/abs/1705.05264

Original

2017

Cited alongside, same era.

2018