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High sensitivity of neural networks against malicious perturbations on inputs causes security concerns.
A Method for Unconstrained Convex Minimization Problem with the Rate of Convergence o ( 1 / k 2 ) o(1/k^{2})
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Error bounds on the power method for determining the largest eigenvalue of a symmetric, positive definite matrix
J. Friedman · 1998
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Incorporating Second-Order Functional Knowledge for Better Option Pricing
F. Bélisle, Y. Bengio, C. Dugas, R. Garcia, and C. Nadeau · 2001
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PAC-Bayes & Margins
J. Langford and J. Shawe-Taylor · 2002
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Rectified Linear Units Improve Restricted Boltzmann Machines
V. Nair and G. E. Hinton · 2010
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Reading Digits in Natural Images with Unsupervised Feature Learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
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Rectifier Nonlinearities Improve Neural Network Acoustic Models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
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Intriguing Properties of Neural Networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2014
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Measuring Neural Net Robustness with Constraints
O. Bastani, Y. Ioannou, L. Lampropoulos, D. Vytiniotis, A. V. Nori, and A. Criminisi · 2015
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Explaining and Harnessing Adversarial Examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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ADAM: A Method for Stochastic Optimization
D. P. Kingma and J. L. Ba · 2015
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Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
D. Clevert, T. Unterthiner, and S. Hochreiter · 2016
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DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
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Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
N. Papernot, P. D. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
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Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
T. Salimans and D. P. Kingma · 2016
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Wide Residual Networks
S. Zagoruyko and N. Komodakis · 2016
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Spectrally-normalized Margin Bounds for Neural Networks
P. L. Bartlett, D. J. Foster, and M. Telgarsky · 2017
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Towards Evaluating the Robustness of Neural Networks
N. Carlini and D. A. Wagner · 2017
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Parseval Networks: Improving Robustness to Adversarial Examples
M. Cisse, P. Bojanowski, E. Grave, Y. Dauphin, and N. Usunier · 2017
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Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
N. Akhtar and A. Mian · 2018
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Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
A. Athalye, N. Carlini, and D. Wagner · 2018
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Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size
I. J. Goodfellow · 2018
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Countering Adversarial Images using Input Transformations
C. Guo, M. Rana, M. Cisse, and L. v. d. Maaten · 2018
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Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope
J. Z. Kolter and E. Wong · 2018
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Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
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Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
M. Hein and M. Andriushchenko · 2017
Cited alongside, same era.
Adversarial Machine Learning at Scale
A. Kurakin, I. J. Goodfellow, and S. Bengio · 2017
Cited alongside, same era.
Lower Bounds on the Robustness to Adversarial Perturbations
J. Peck, J. Roels, B. Goossens, and Y. Saeys · 2017
Cited alongside, same era.
One pixel attack for fooling deep neural networks
J. Su, D. V. Vargas, and S. Kouichi · 2017
Cited alongside, same era.
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Examples
B. Wang, J. Gao, and Y. Qi · 2017
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Towards Deep Learning Models Resistant to Adversarial Attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
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A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
B. Neyshabur, S. Bhojanapalli, and N. Srebro · 2018
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Certified Defenses against Adversarial Examples
A. Raghunathan, J. Steinhardt, and P. Liang · 2018
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Reachability Analysis of Deep Neural Networks with Provable Guarantees
W. Ruan, X. Huang, and M. Kwiatkowska · 2018
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Certifiable Distributional Robustness with Principled Adversarial Training
A. Sinha, H. Namkoong, and J. Duchi · 2018
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Ensemble Adversarial Training: Attacks and Defenses
F. Tramèr, A. Kurakin, N. Papernot, D. Boneh, and P. D. McDaniel · 2018
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Evaluating the robustness of neural networks: An extreme value theory approach
T. Weng, H. Zhang, P. Chen, J. Yi, D. Su, Y. Gao, C. Hsieh, and L. Daniel · 2018
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Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
W. Xu, D. Evans, and Y. Qi · 2018
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