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There is a rising interest in studying the robustness of deep neural network classifiers against adversaries, with both advanced attack and defence techniques being actively developed.
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The efficiency of logistic regression compared to normal discriminant analysis
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On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes
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Adversarial classification
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Design of robust classifiers for adversarial environments
Biggio, B., Fumera, G., and Roli, F · 2011
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Adversarial machine learning
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I., and Tygar, J · 2011
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Evasion attacks against machine learning at test time
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., and Roli, F · 2013
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Security evaluation of pattern classifiers under attack
Biggio, B., Fumera, G., and Roli, F · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Towards deep neural network architectures robust to adversarial examples
Gu, S. and Rigazio, L · 2014
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
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Generating images with perceptual similarity metrics based on deep networks
Dosovitskiy, A. and Brox, T · 2016
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Image style transfer using convolutional neural networks
Gatys, L. A., Ecker, A. S., and Bethge, M · 2016
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Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., and Fei-Fei, L · 2016
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Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
Cited alongside, same era.
Autoencoding beyond pixels using a learned similarity metric
Larsen, A. B. L., Sønderby, S. K., Larochelle, H., and Winther, O · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., and Swami, A · 2016
Cited alongside, same era.
Adversarial images for variational autoencoders
Tabacof, P., Tavares, J., and Valle, E · 2016
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Brendel, W., Rauber, J., and Bethge, M · 2018
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Audio adversarial examples: Targeted attacks on speech-to-text
Carlini, N. and Wagner, D · 2018
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Ead: Elastic-net attacks to deep neural networks via adversarial examples, 2018
Chen, P.-Y., Sharma, Y., Zhang, H., Yi, J., and Hsieh, C.-J · 2018
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Boosting adversarial attacks with momentum
Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., and Li, J · 2018
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van den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Deep variational information bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
Cited alongside, same era.
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Chen, P. Y., Zhang, H., Sharma, Y., Yi, J., and Hsieh, C · 2017
Cited alongside, same era.
Latentpoison-adversarial attacks on the latent space
Creswell, A., Bharath, A. A., and Sengupta, B · 2017
Cited alongside, same era.
Detecting adversarial samples from artifacts
Feinman, R., Curtin, R. R., Shintre, S., and Gardner, A. B · 2017
Cited alongside, same era.
Adversarial examples for generative models
Kos, J., Fischer, I., and Song, D · 2017
Cited alongside, same era.
Gilmer, J., Metz, L., Faghri, F., Schoenholz, S. S., Raghu, M., Wattenberg, M., and Goodfellow, I · 2018
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Progressive growing of GANs for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
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Adversarial attacks and defences competition
Kurakin, A., Goodfellow, I., Bengio, S., Dong, Y., Liao, F., Liang, M., Pang, T., Zhu, J., Hu, X., Xie, C., et al · 2018
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Defense-GAN: Protecting classifiers against adversarial attacks using generative models
Samangouei, P., Kabkab, M., and Chellappa, R · 2018
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Understanding measures of uncertainty for adversarial example detection
Smith, L. and Gal, Y · 2018
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Song, Y., Kim, T., Nowozin, S., Ermon, S., and Kushman, N · 2018
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Ensemble adversarial training: Attacks and defenses
Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., and McDaniel, P · 2018
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Adversarial risk and the dangers of evaluating against weak attacks
Uesato, J., O’Donoghue, B., Kohli, P., and Oord, A · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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Do deep generative models know what they don’t know?
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2019
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Towards the first adversarially robust neural network model on MNIST
Schott, L., Rauber, J., Bethge, M., and Brendel, W · 2019
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