Built on

T. G. Dietterich and G. Bakiri, “Solving multiclass learning problems via error-correcting output codes,” Journal of artificial intelligence research , vol. 2, pp. 263–286, 1994
1994
Earlier work this paper cites.
Y. LeCun, “The mnist database of handwritten digits,” http://yann. lecun. com/exdb/mnist/ , 1998
1998
Earlier work this paper cites.
C. P. Pfleeger and S. L. Pfleeger, Security in computing . Prentice Hall Professional Technical Reference, 2002
2002
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,” arXiv preprint arXiv:1412.6572 , 2014
Original
2014
Earlier work this paper cites.
M. Wang, X. Liu, and X. Wu, “Visual classification by l1-hypergraph modeling,” in IEEE Trans. KDE , 2015, pp. 2564–2574
2015
Earlier work this paper cites.

Similar

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
S. M. Moosavi Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , no. EPFL-CONF-218057, 2016
2016
Cited alongside, same era.
Y. Liu, X. Chen, C. Liu, and D. Song, “Delving into transferable adversarial examples and black-box attacks,” arXiv preprint arXiv:1611.02770 , 2016
Original
2016
Cited alongside, same era.
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in Security and Privacy (SP), 2016 IEEE Symposium on . IEEE, 2016, pp. 582–597
2016
Cited alongside, same era.

Then

K. Grosse, P. Manoharan, N. Papernot, M. Backes, and P. McDaniel, “On the (statistical) detection of adversarial examples,” arXiv preprint arXiv:1702.06280 , 2017
Original
2017
Later among the works it 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
Later among the works it cites.
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in Security and Privacy (SP), 2017 IEEE Symposium on . IEEE, 2017, pp. 39–57
2017
Later among the works it cites.
N. Carlini and D. Wagner, “Magnet and ” efficient defenses against adversarial attacks” are not robust to adversarial examples,” arXiv preprint arXiv:1711.08478 , 2017
Original
2017
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…

2018

Detecting Adversarial Examples via Key-based Network

Zhao, Pinlong, Fu, Zhouyu, wu, Ou et al.

Understand

Though deep neural networks have achieved state-of-the-art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversarial examples.

Small and often imperceptible perturbations to the input images are sufficient to fool the most powerful deep neural networks.
Various defense methods have been proposed to address this issue.
However, they either require knowledge on the process of generating adversarial examples, or are not robust against new attacks specifically designed to penetrate the existing defense.

Built on

T. G. Dietterich and G. Bakiri, “Solving multiclass learning problems via error-correcting output codes,” Journal of artificial intelligence research , vol. 2, pp. 263–286, 1994
1994
Earlier work this paper cites.
Y. LeCun, “The mnist database of handwritten digits,” http://yann. lecun. com/exdb/mnist/ , 1998
1998
Earlier work this paper cites.
C. P. Pfleeger and S. L. Pfleeger, Security in computing . Prentice Hall Professional Technical Reference, 2002
2002
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,” arXiv preprint arXiv:1412.6572 , 2014
Original
2014
Earlier work this paper cites.
M. Wang, X. Liu, and X. Wu, “Visual classification by l1-hypergraph modeling,” in IEEE Trans. KDE , 2015, pp. 2564–2574
2015
Earlier work this paper cites.

Similar

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
S. M. Moosavi Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , no. EPFL-CONF-218057, 2016
2016
Cited alongside, same era.
Y. Liu, X. Chen, C. Liu, and D. Song, “Delving into transferable adversarial examples and black-box attacks,” arXiv preprint arXiv:1611.02770 , 2016
Original
2016
Cited alongside, same era.
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in Security and Privacy (SP), 2016 IEEE Symposium on . IEEE, 2016, pp. 582–597
2016
Cited alongside, same era.

Then

K. Grosse, P. Manoharan, N. Papernot, M. Backes, and P. McDaniel, “On the (statistical) detection of adversarial examples,” arXiv preprint arXiv:1702.06280 , 2017
Original
2017
Later among the works it 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
Later among the works it cites.
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in Security and Privacy (SP), 2017 IEEE Symposium on . IEEE, 2017, pp. 39–57
2017
Later among the works it cites.
N. Carlini and D. Wagner, “Magnet and ” efficient defenses against adversarial attacks” are not robust to adversarial examples,” arXiv preprint arXiv:1711.08478 , 2017
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…

J. H. Metzen, T. Genewein, V. Fischer, and B. Bischoff, “On detecting adversarial perturbations,” arXiv preprint arXiv:1702.04267 , 2017

Original

2017

Cited alongside, same era.

N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security . ACM, 2017, pp. 506–519

2017

Later among the works it cites.