Built on

A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, and K.-R. MÃžller, “How to explain individual classification decisions,” Journal of Machine Learning Research , vol. 11, no. Jun, pp. 1803–1831, 2010
2010
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.
K. Simonyan, A. Vedaldi, and A. Zisserman, “Deep inside convolutional networks: Visualising image classification models and saliency maps,” arXiv preprint arXiv:1312.6034 , 2013
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.
S. Gu and L. Rigazio, “Towards deep neural network architectures robust to adversarial examples,” Computer Science , 2014
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.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European conference on computer vision . Springer, 2014, pp. 818–833
2014
Earlier work this paper cites.
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller, “Striving for simplicity: The all convolutional net,” arXiv preprint arXiv:1412.6806 , 2014
Original
2014
Earlier work this paper cites.
D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1–9
2015
Earlier work this paper cites.
R. Huang, B. Xu, D. Schuurmans, and C. Szepesvari, “Learning with a strong adversary,” Computer Science , 2015
2015
Earlier work this paper cites.

Similar

J. Jin, A. Dundar, and E. Culurciello, “Robust convolutional neural networks under adversarial noise,” Computer Science , 2015
2015
Cited alongside, same era.
U. Shaham, Y. Yamada, and S. Negahban, “Understanding adversarial training: Increasing local stability of neural nets through robust optimization,” Computer Science , 2015
2015
Cited alongside, same era.
N. Papernot, P. Mcdaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” pp. 372–387, 2015
2015
Cited alongside, same era.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision , vol. 115, no. 3, pp. 211–252, 2015
2015
Cited alongside, same era.

Then

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin et al. , “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467 , 2016
Original
2016
Later among the works it cites.
R. Feinman, R. R. Curtin, S. Shintre, and A. B. Gardner, “Detecting adversarial samples from artifacts,” arXiv preprint arXiv:1703.00410 , 2017
Original
2017
Later among the works it cites.
A. N. Bhagoji, D. Cullina, and P. Mittal, “Dimensionality reduction as a defense against evasion attacks on machine learning classifiers,” 2017
2017
Later among the works it cites.
Z. Gong, W. Wang, and W.-S. Ku, “Adversarial and clean data are not twins,” arXiv preprint arXiv:1704.04960 , 2017
Original
2017
Later among the works it cites.
K. Grosse, P. Manoharan, N. Papernot, M. Backes, and P. McDaniel, “On the (statistical) detection of adversarial examples,” arXiv preprint arXiv:1702.06280 , 2017

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2018

Detecting Adversarial Perturbations with Saliency

Zhang, Chiliang, Yang, Zhimou, Ye, Zuochang

Understand

In this paper we propose a novel method for detecting adversarial examples by training a binary classifier with both origin data and saliency data.

In the case of image classification model, saliency simply explain how the model make decisions by identifying significant pixels for prediction.
A model shows wrong classification output always learns wrong features and shows wrong saliency as well.
Our approach shows good performance on detecting adversarial perturbations.

Built on

A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, and K.-R. MÃžller, “How to explain individual classification decisions,” Journal of Machine Learning Research , vol. 11, no. Jun, pp. 1803–1831, 2010
2010
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.
K. Simonyan, A. Vedaldi, and A. Zisserman, “Deep inside convolutional networks: Visualising image classification models and saliency maps,” arXiv preprint arXiv:1312.6034 , 2013
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.
S. Gu and L. Rigazio, “Towards deep neural network architectures robust to adversarial examples,” Computer Science , 2014
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.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European conference on computer vision . Springer, 2014, pp. 818–833
2014
Earlier work this paper cites.
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller, “Striving for simplicity: The all convolutional net,” arXiv preprint arXiv:1412.6806 , 2014
Original
2014
Earlier work this paper cites.
D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1–9
2015
Earlier work this paper cites.
R. Huang, B. Xu, D. Schuurmans, and C. Szepesvari, “Learning with a strong adversary,” Computer Science , 2015
2015
Earlier work this paper cites.

Similar

J. Jin, A. Dundar, and E. Culurciello, “Robust convolutional neural networks under adversarial noise,” Computer Science , 2015
2015
Cited alongside, same era.
U. Shaham, Y. Yamada, and S. Negahban, “Understanding adversarial training: Increasing local stability of neural nets through robust optimization,” Computer Science , 2015
2015
Cited alongside, same era.
N. Papernot, P. Mcdaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” pp. 372–387, 2015
2015
Cited alongside, same era.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision , vol. 115, no. 3, pp. 211–252, 2015
2015
Cited alongside, same era.

Then

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin et al. , “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467 , 2016
Original
2016
Later among the works it cites.
R. Feinman, R. R. Curtin, S. Shintre, and A. B. Gardner, “Detecting adversarial samples from artifacts,” arXiv preprint arXiv:1703.00410 , 2017
Original
2017
Later among the works it cites.
A. N. Bhagoji, D. Cullina, and P. Mittal, “Dimensionality reduction as a defense against evasion attacks on machine learning classifiers,” 2017
2017
Later among the works it cites.
Z. Gong, W. Wang, and W.-S. Ku, “Adversarial and clean data are not twins,” arXiv preprint arXiv:1704.04960 , 2017
Original
2017
Later among the works it cites.
K. Grosse, P. Manoharan, N. Papernot, M. Backes, and P. McDaniel, “On the (statistical) detection of adversarial examples,” arXiv preprint arXiv:1702.06280 , 2017

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

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.

O. Bastani, Y. Ioannou, L. Lampropoulos, D. Vytiniotis, A. Nori, and A. Criminisi, “Measuring neural net robustness with constraints,” 2016

2016

Cited alongside, same era.

A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” arXiv preprint arXiv:1607.02533 , 2016

Original

2016

Cited alongside, same era.

S. Zheng, Y. Song, T. Leung, and I. Goodfellow, “Improving the robustness of deep neural networks via stability training,” in Computer Vision and Pattern Recognition , 2016, pp. 4480–4488

2016

Cited alongside, same era.

X. Li and F. Li, “Adversarial examples detection in deep networks with convolutional filter statistics,” 2016

2016

Cited alongside, same era.

B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, “Learning deep features for discriminative localization,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2921–2929

2016

Cited alongside, same era.

R. R. Selvaraju, A. Das, R. Vedantam, M. Cogswell, D. Parikh, and D. Batra, “Grad-cam: Why did you say that?” arXiv preprint arXiv:1611.07450 , 2016

Original

2016

Cited alongside, same era.

L. M. Zintgraf, T. S. Cohen, and M. Welling, “A new method to visualize deep neural networks,” arXiv preprint arXiv:1603.02518 , 2016

Original

2016

Cited alongside, same era.

Original

2017