Built on

M. Barreno, B. Nelson, R. Sears, A. D. Joseph, and J. D. Tygar, “Can machine learning be secure?” in Proceedings of the 2006 ACM Symposium on Information, computer and communications security . ACM, 2006, pp. 16–25
2006
Earlier work this paper cites.
Y. LeCun and C. Cortes, “MNIST handwritten digit database,” 2010. [Online]. Available: http://yann.lecun.com/exdb/mnist/
2010
Earlier work this paper cites.
L. Huang, A. D. Joseph, B. Nelson, B. I. Rubinstein, and J. Tygar, “Adversarial machine learning,” in Proceedings of the 4th ACM workshop on Security and Artificial Intelligence . ACM, 2011, pp. 43–58
2011
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 , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. A. Riedmiller, “Playing atari with deep reinforcement learning,” CoRR , vol. abs/1312.5602, 2013. [Online]. Available: http://arxiv.org/abs/1312.5602
Original
2013
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 Machine Learning and Knowledge Discovery in Databases , H. Blockeel, K. Kersting, S. Nijssen, and F. Železný, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 387–402
2013
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” CoRR , vol. abs/1312.6199, 2013. [Online]. Available: http://arxiv.org/abs/1312.6199
Original
2013
Earlier work this paper cites.
K. Simonyan, A. Vedaldi, and A. Zisserman, “Deep inside convolutional networks: Visualising image classification models and saliency maps,” CoRR , vol. abs/1312.6034, 2013. [Online]. Available: http://arxiv.org/abs/1312.6034
Original
2013
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.
C. Chen, A. Seff, A. Kornhauser, and J. Xiao, “Deepdriving: Learning affordance for direct perception in autonomous driving,” in The IEEE International Conference on Computer Vision (ICCV) , December 2015
2015
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in International Conference on Learning Representations , 2015
2015
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,” arXiv preprint arXiv:1511.04599 , 2015
Original
2015
Earlier work this paper cites.
A. Nguyen, J. Yosinski, and J. Clune, “Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2015, pp. 427–436
2015
Earlier work this paper cites.

Similar

S. Sabour, Y. Cao, F. Faghri, and D. J. Fleet, “Adversarial manipulation of deep representations,” CoRR , vol. abs/1511.05122, 2015. [Online]. Available: http://arxiv.org/abs/1511.05122
Original
2015
Cited alongside, same era.
A. Andoni, P. Indyk, T. Laarhoven, I. Razenshteyn, and L. Schmidt, “Practical and optimal lsh for angular distance,” in Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1 , ser. NIPS’15. Cambridge, MA, USA: MIT Press, 2015, pp. 1225–1233. [Online]. Available: http://dl.acm.org/citation.cfm?id=2969239.2969376
2015
Cited alongside, same era.
M. Bojarski, D. D. Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” CoRR , vol. abs/1604.07316, 2016. [Online]. Available: http://arxiv.org/abs/1604.07316
Original
2016
Cited alongside, same era.
N. Carlini and D. Wagner, “Defensive distillation is not robust to adversarial examples,” arXiv preprint arXiv:1607.04311 , 2016

Then

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” CoRR , vol. abs/1706.06083, 2017. [Online]. Available: http://arxiv.org/abs/1706.06083
Original
2017
Later among the works it cites.
W. Xu, D. Evans, and Y. Qi, “Feature squeezing: Detecting adversarial examples in deep neural networks,” CoRR , vol. abs/1704.01155, 2017. [Online]. Available: http://arxiv.org/abs/1704.01155
Original
2017
Later among the works it cites.
J. Kim and J. F. Canny, “Interpretable learning for self-driving cars by visualizing causal attention,” CoRR , vol. abs/1703.10631, 2017. [Online]. Available: http://arxiv.org/abs/1703.10631
Original
2017
Later among the works it cites.
Q. Zhang, Y. N. Wu, and S. Zhu, “Interpretable convolutional neural networks,” CoRR , vol. abs/1710.00935, 2017. [Online]. Available: http://arxiv.org/abs/1710.00935
Original
2017
Later among the works it cites.

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2019

On the Robustness of Deep K-Nearest Neighbors

Sitawarin, Chawin, Wagner, David

Understand

Despite a large amount of attention on adversarial examples, very few works have demonstrated an effective defense against this threat.

We examine Deep k-Nearest Neighbor (DkNN), a proposed defense that combines k-Nearest Neighbor (kNN) and deep learning to improve the model's robustness to adversarial examples.
It is challenging to evaluate the robustness of this scheme due to a lack of efficient algorithm for attacking kNN classifiers with large k and high-dimensional data.
We propose a heuristic attack that allows us to use gradient descent to find adversarial examples for kNN classifiers, and then apply it to attack the DkNN defense as well.

Built on

M. Barreno, B. Nelson, R. Sears, A. D. Joseph, and J. D. Tygar, “Can machine learning be secure?” in Proceedings of the 2006 ACM Symposium on Information, computer and communications security . ACM, 2006, pp. 16–25
2006
Earlier work this paper cites.
Y. LeCun and C. Cortes, “MNIST handwritten digit database,” 2010. [Online]. Available: http://yann.lecun.com/exdb/mnist/
2010
Earlier work this paper cites.
L. Huang, A. D. Joseph, B. Nelson, B. I. Rubinstein, and J. Tygar, “Adversarial machine learning,” in Proceedings of the 4th ACM workshop on Security and Artificial Intelligence . ACM, 2011, pp. 43–58
2011
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 , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. A. Riedmiller, “Playing atari with deep reinforcement learning,” CoRR , vol. abs/1312.5602, 2013. [Online]. Available: http://arxiv.org/abs/1312.5602
Original
2013
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 Machine Learning and Knowledge Discovery in Databases , H. Blockeel, K. Kersting, S. Nijssen, and F. Železný, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 387–402
2013
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” CoRR , vol. abs/1312.6199, 2013. [Online]. Available: http://arxiv.org/abs/1312.6199
Original
2013
Earlier work this paper cites.
K. Simonyan, A. Vedaldi, and A. Zisserman, “Deep inside convolutional networks: Visualising image classification models and saliency maps,” CoRR , vol. abs/1312.6034, 2013. [Online]. Available: http://arxiv.org/abs/1312.6034
Original
2013
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.
C. Chen, A. Seff, A. Kornhauser, and J. Xiao, “Deepdriving: Learning affordance for direct perception in autonomous driving,” in The IEEE International Conference on Computer Vision (ICCV) , December 2015
2015
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in International Conference on Learning Representations , 2015
2015
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,” arXiv preprint arXiv:1511.04599 , 2015
Original
2015
Earlier work this paper cites.
A. Nguyen, J. Yosinski, and J. Clune, “Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2015, pp. 427–436
2015
Earlier work this paper cites.

Similar

S. Sabour, Y. Cao, F. Faghri, and D. J. Fleet, “Adversarial manipulation of deep representations,” CoRR , vol. abs/1511.05122, 2015. [Online]. Available: http://arxiv.org/abs/1511.05122
Original
2015
Cited alongside, same era.
A. Andoni, P. Indyk, T. Laarhoven, I. Razenshteyn, and L. Schmidt, “Practical and optimal lsh for angular distance,” in Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1 , ser. NIPS’15. Cambridge, MA, USA: MIT Press, 2015, pp. 1225–1233. [Online]. Available: http://dl.acm.org/citation.cfm?id=2969239.2969376
2015
Cited alongside, same era.
M. Bojarski, D. D. Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” CoRR , vol. abs/1604.07316, 2016. [Online]. Available: http://arxiv.org/abs/1604.07316
Original
2016
Cited alongside, same era.
N. Carlini and D. Wagner, “Defensive distillation is not robust to adversarial examples,” arXiv preprint arXiv:1607.04311 , 2016

Then

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” CoRR , vol. abs/1706.06083, 2017. [Online]. Available: http://arxiv.org/abs/1706.06083
Original
2017
Later among the works it cites.
W. Xu, D. Evans, and Y. Qi, “Feature squeezing: Detecting adversarial examples in deep neural networks,” CoRR , vol. abs/1704.01155, 2017. [Online]. Available: http://arxiv.org/abs/1704.01155
Original
2017
Later among the works it cites.
J. Kim and J. F. Canny, “Interpretable learning for self-driving cars by visualizing causal attention,” CoRR , vol. abs/1703.10631, 2017. [Online]. Available: http://arxiv.org/abs/1703.10631
Original
2017
Later among the works it cites.
Q. Zhang, Y. N. Wu, and S. Zhu, “Interpretable convolutional neural networks,” CoRR , vol. abs/1710.00935, 2017. [Online]. Available: http://arxiv.org/abs/1710.00935
Original
2017
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…

Original

2016

Cited alongside, same era.

M. T. Ribeiro, S. Singh, and C. Guestrin, “”why should I trust you?”: Explaining the predictions of any classifier,” CoRR , vol. abs/1602.04938, 2016. [Online]. Available: http://arxiv.org/abs/1602.04938

Original

2016

Cited alongside, same era.

D. Hendrycks and K. Gimpel, “Visible progress on adversarial images and a new saliency map,” CoRR , vol. abs/1608.00530, 2016. [Online]. Available: http://arxiv.org/abs/1608.00530

Original

2016

Cited alongside, same era.

X. Li and F. Li, “Adversarial examples detection in deep networks with convolutional filter statistics,” CoRR , vol. abs/1612.07767, 2016. [Online]. Available: http://arxiv.org/abs/1612.07767

Original

2016

Cited alongside, same era.

D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton et al. , “Mastering the game of go without human knowledge,” Nature , vol. 550, no. 7676, p. 354, 2017

2017

Cited alongside, same era.

D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. P. Lillicrap, K. Simonyan, and D. Hassabis, “Mastering chess and shogi by self-play with a general reinforcement learning algorithm,” CoRR , vol. abs/1712.01815, 2017. [Online]. Available: http://arxiv.org/abs/1712.01815

Original

2017

Cited alongside, same era.

G. J. S. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” CoRR , vol. abs/1702.05747, 2017. [Online]. Available: http://arxiv.org/abs/1702.05747

Original

2017

Cited alongside, same era.

L. Engstrom, D. Tsipras, L. Schmidt, and A. Madry, “A rotation and a translation suffice: Fooling cnns with simple transformations,” CoRR , vol. abs/1712.02779, 2017. [Online]. Available: http://arxiv.org/abs/1712.02779

Original

2017

Cited alongside, same era.

——, “Adversarial examples are not easily detected: Bypassing ten detection methods,” in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security , ser. AISec ’17. New York, NY, USA: ACM, 2017, pp. 3–14. [Online]. Available: http://doi.acm.org/10.1145/3128572.3140444

2017

Cited alongside, same era.

R. Feinman, R. R. Curtin, S. Shintre, and A. B. Gardner, “Detecting adversarial samples from artifacts,” CoRR , vol. abs/1703.00410, 2017

Original

2017