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Training deep neural networks on images represented as grids of pixels has brought to light an interesting phenomenon known as adversarial examples.
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Generative adversarial nets
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Distributional smoothing with virtual adversarial training
T. Miyato, S.-i. Maeda, M. Koyama, K. Nakae, and S. Ishii · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
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Defensive distillation is not robust to adversarial examples
N. Carlini and D. Wagner · 2016
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Adversarial machine learning at scale
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
S. M. Moosavi Dezfooli, A. Fawzi, and P. Frossard · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
A. Nguyen, A. Dosovitskiy, T. Yosinski, Jason band Brox, and J. Clune · 2016
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cleverhans v2. 0.0: an adversarial machine learning library
N. Papernot, N. Carlini, I. Goodfellow, R. Feinman, F. Faghri, A. Matyasko, K. Hambardzumyan, Y.-L. Juang, A. Kurakin, R. Sheatsley, et al · 2016
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The limitations of deep learning in adversarial settings
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami · 2016
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Generative visual manipulation on the natural image manifold
J.-Y. Zhu, P. Krähenbühl, E. Shechtman, and A. A. Efros · 2016
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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
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Ape-gan: Adversarial perturbation elimination with gan
S. Shen, G. Jin, K. Gao, and Y. Zhang · 2017
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Y. Song, T. Kim, S. Nowozin, S. Ermon, and N. Kushman · 2017
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One pixel attack for fooling deep neural networks
J. Su, D. V. Vargas, and S. Kouichi · 2017
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Ensemble adversarial training: Attacks and defenses
F. Tramèr, A. Kurakin, N. Papernot, D. Boneh, and P. McDaniel · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
N. Carlini and D. Wagner · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
Cited alongside, same era.
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
P.-Y. Chen, H. Zhang, Y. Sharma, J. Yi, and C.-J. Hsieh · 2017
Cited alongside, same era.
Countering adversarial images using input transformations
C. Guo, M. Rana, M. Cissé, and L. van der Maaten · 2017
Cited alongside, same era.
A neural representation of sketch drawings
D. Ha and D. Eck · 2017
Cited alongside, same era.
Query-efficient black-box adversarial examples
A. Ilyas, L. Engstrom, A. Athalye, and J. Lin · 2017
Cited alongside, same era.
The robust manifold defense: Adversarial training using generative models
A. Ilyas, A. Jalal, E. Asteri, C. Daskalakis, and A. G. Dimakis · 2017
Cited alongside, same era.
Safer classification by synthesis
W. Wang, A. Wang, A. Tamar, X. Chen, and P. Abbeel · 2017
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Neural scene de-rendering
J. Wu, J. B. Tenenbaum, and P. Kohli · 2017
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Feature squeezing: Detecting adversarial examples in deep neural networks
W. Xu, D. Evans, and Y. Qi · 2017
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Adversarial examples: Attacks and defenses for deep learning
X. Yuan, P. He, Q. Zhu, R. R. Bhat, and X. Li · 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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J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow · 2018
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Defense-gan: Protecting classifiers against adversarial attacks using generative models
P. Samangouei, M. Kabkab, and R. Chellappa · 2018
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Understanding measures of uncertainty for adversarial example detection
L. Smith and Y. Gal · 2018
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