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It has been shown that most machine learning algorithms are susceptible to adversarial perturbations.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
Earlier work this paper cites.
Towards deep neural network architectures robust to adversarial examples
S. Gu and L. Rigazio · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Analysis of classifiers’ robustness to adversarial perturbations
A. Fawzi, O. Fawzi, and P. Frossard · 2015
Earlier work this paper cites.
Deepfool: a simple and accurate method to fool deep neural networks
S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2015
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
Cited alongside, same era.
Adversarial manipulation of deep representations
S. Sabour, Y. Cao, F. Faghri, and D. J. Fleet · 2015
Cited alongside, same era.
Robustness of classifiers: from adversarial to random noise
A. Fawzi, S. Moosavi-Dezfooli, and P. Frossard · 2016
Cited alongside, same era.
Robustness of classifiers: from adversarial to random noise
A. Fawzi, S.-M. Moosavi-Dezfooli, and P. Frossard · 2016
Cited alongside, same era.
Universal adversarial perturbations
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2016
Later among the works it cites.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
N. Papernot, P. D. McDaniel, and I. J. Goodfellow · 2016
Later among the works it cites.
You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Later among the works it cites.
Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter · 2016
Later among the works it cites.
Reluplex: An efficient SMT solver for verifying deep neural networks
G. Katz, C. W. Barrett, D. L. Dill, K. Julian, and M. J. Kochenderfer · 2017
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Adversarial examples in the physical world
A. Kurakin, I. J. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
Delving into transferable adversarial examples and black-box attacks
Y. Liu, X. Chen, C. Liu, and D. Song · 2016
Cited alongside, same era.
Defensive distillation is not robust to adversarial examples
N. Carlini and D. Wagner
Cited in the paper.
Fundamental limits on adversarial robustness
A. Fawzi and P. Frossard
Cited in the paper.
Closest in time.
Safetynet: Detecting and rejecting adversarial examples robustly
J. Lu, T. Issaranon, and D. Forsyth · 2017
Closest in time.
On detecting adversarial perturbations
J. H. Metzen, T. Genewein, V. Fischer, and B. Bischoff · 2017
Closest in time.