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Recent advances in Deep Learning show the existence of image-agnostic quasi-imperceptible perturbations that when applied to `any' image can fool a state-of-the-art network classifier to change its prediction about the image label.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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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. Kingma and J. Ba · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Analysis of classifiers’ robustness to adversarial perturbations
A. Fawzi, O. Fawzi, and P. Frossard · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 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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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Adversarial manipulation of deep representations
S. Sabour, Y. Cao, F. Faghri, and D. J. Fleet · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 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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Robustness of classifiers: from adversarial to random noise
A. Fawzi, S.-M. Moosavi-Dezfooli, and P. Frossard · 2016
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Deep learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2016
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A boundary tilting persepective on the phenomenon of adversarial examples
T. Tanay and L. Griffin · 2016
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Adversarial transformation networks: Learning to generate adversarial examples
S. Baluja and I. Fischer · 2017
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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
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Adversarial examples for semantic image segmentation
V. Fischer, M. C. Kumar, J. H. Metzen, and T. Brox · 2017
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Safetynet: Detecting and rejecting adversarial examples robustly
J. Lu, T. Issaranon, and D. Forsyth · 2017
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A. Kurakin, I. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
Adversarial machine learning at scale
A. Kurakin, I. 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.
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
Cited alongside, same era.
Adversarial diversity and hard positive generation
A. Rozsa, E. M. Rudd, and T. E. Boult · 2016
Cited alongside, same era.
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
Cited alongside, same era.
No need to worry about adversarial examples in object detection in autonomous vehicles
J. Lu, H. Sibai, E. Fabry, and D. Forsyth · 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 against semantic image segmentation
J. H. Metzen, M. C. Kumar, T. Brox, and V. Fischer · 2017
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Universal adversarial perturbations
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
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Analysis of universal adversarial perturbations
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, P. Frossard, and S. Soatto · 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 for semantic segmentation and object detection
C. Xie, J. Wang, Z. Zhang, Y. Zhou, L. Xie, and A. Yuille · 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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Deflecting adversarial attacks with pixel deflection
A. Prakash, N. Moran, S. Garber, A. DiLillo, and J. Storer · 2018
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