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Deep neural networks (DNNs) have achieved great success in solving a variety of machine learning (ML) problems, especially in the domain of image recognition.
Learning Multiple Layers of Features from Tiny Images
A. Krizhevsky · 2009
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The German Traffic Sign Recognition Benchmark: A multi-class classification competition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Towards deep neural network architectures robust to adversarial examples
S. Gu and L. Rigazio · 2014
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Intriguing properties of neural networks
C. Szegedy, G. Inc, W. Zaremba, I. Sutskever, G. Inc, J. Bruna, D. Erhan, G. Inc, I. Goodfellow, and R. Fergus · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Foveation-based mechanisms alleviate adversarial examples
Y. Luo, X. Boix, G. Roig, T. Poggio, and Q. Zhao · 2015
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A study of the effect of jpg compression on adversarial images
G. K. Dziugaite, Z. Ghahramani, and D. M. Roy · 2016
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Adversarial perturbations against deep neural networks for malware classification
K. Grosse, N. Papernot, P. Manoharan, M. Backes, and P. McDaniel · 2016
Cited alongside, same era.
Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
Deepfool: A simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
Detecting adversarial samples from artifacts
R. Feinman, R. R. Curtin, S. Shintre, and A. B. Gardner · 2017
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Generating adversarial malware examples for black-box attacks based on gan
W. Hu and Y. Tan · 2017
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Adversarial attacks on neural network policies
S. Huang, N. Papernot, I. Goodfellow, Y. Duan, and P. Abbeel · 2017
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Dense associative memory is robust to adversarial inputs
D. Krotov and J. J. Hopfield · 2017
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Tactics of adversarial attack on deep reinforcement learning agents
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Cited alongside, same era.
The limitations of deep learning in adversarial settings
N. Papernot, P. D. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami · 2016
Cited alongside, same era.
Crafting adversarial input sequences for recurrent neural networks
N. Papernot, P. D. McDaniel, A. Swami, and R. E. Harang · 2016
Cited alongside, same era.
Dimensionality reduction as a defense against evasion attacks on machine learning classifiers
A. N. Bhagoji, D. Cullina, and P. Mittal · 2017
Cited alongside, same era.
Y.-C. Lin, Z.-W. Hong, Y.-H. Liao, M.-L. Shih, M.-Y. Liu, and M. Sun · 2017
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On detecting adversarial perturbations
J. H. Metzen, T. Genewein, V. Fischer, and B. Bischoff · 2017
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Universal adversarial perturbations
S. M. Moosavi Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
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Practical black-box attacks against machine learning
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2017
Closest in time.