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Though Convolutional Neural Networks (CNNs) have surpassed human-level performance on tasks such as object classification and face verification, they can easily be fooled by adversarial attacks.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: · 1998
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Maaten, L.v.d., Hinton, G.: · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G.: · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I.J., Shlens, J., Szegedy, C.: · 2014
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Towards deep neural network architectures robust to adversarial examples
Gu, S., Rigazio, L.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Shaham, U., Yamada, Y., Negahban, S.: · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: · 2016
Cited alongside, same era.
Universal adversarial perturbations
Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: · 2016
Cited alongside, same era.
Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., Bengio, S.: · 2016
Cited alongside, same era.
A study of the effect of jpg compression on adversarial images
Dziugaite, G.K., Ghahramani, Z., Roy, D.M.: · 2016
Cited alongside, same era.
Safetynet: Detecting and rejecting adversarial examples robustly
Lu, J., Issaranon, T., Forsyth, D.: · 2017
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On detecting adversarial perturbations
Metzen, J.H., Genewein, T., Fischer, V., Bischoff, B.: · 2017
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On the (statistical) detection of adversarial examples
Grosse, K., Manoharan, P., Papernot, N., Backes, M., McDaniel, P.: · 2017
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Detecting adversarial samples from artifacts
Feinman, R., Curtin, R.R., Shintre, S., Gardner, A.B.: · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
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Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: · 2016
Cited alongside, same era.
Adversarial perturbations of deep neural networks
Warde-Farley, D., Goodfellow, I.: · 2016
Cited alongside, same era.
Regularizing deep networks using efficient layerwise adversarial training
Sankaranarayanan, S., Jain, A., Chellappa, R., Lim, S.N.: · 2017
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Tramèr, F., Kurakin, A., Papernot, N., Boneh, D., McDaniel, P.: · 2017
Cited alongside, same era.
L2-constrained softmax loss for discriminative face verification
Ranjan, R., Castillo, C.D., Chellappa, R.: · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Carlini, N., Wagner, D.: · 2017
Cited alongside, same era.
Carlini, N., Wagner, D.: · 2017
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Miyato, T., Maeda, S.i., Koyama, M., Ishii, S.: · 2017
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Countering adversarial images using input transformations
Guo, C., Rana, M., Cisse, M., van der Maaten, L.: · 2017
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Parseval networks: Improving robustness to adversarial examples
Cisse, M., Bojanowski, P., Grave, E., Dauphin, Y., Usunier, N.: · 2017
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Efficient defenses against adversarial attacks
Zantedeschi, V., Nicolae, M.I., Rawat, A.: · 2017
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Foolbox v0. 8.0: A python toolbox to benchmark the robustness of machine learning models
Rauber, J., Brendel, W., Bethge, M.: · 2017
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Practical black-box attacks against machine learning
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: · 2017
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