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Recent work has shown that state-of-the-art models are highly vulnerable to adversarial perturbations of the input.
On the momentum term in gradient descent learning algorithms
Qian, Ning · 1999
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Understanding the difficulty of training deep feedforward neural networks
Glorot, Xavier and Bengio, Yoshua · 2010
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Towards Deep Neural Network Architectures Robust to Adversarial Examples
Gu, S. and Rigazio, L · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
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Distributional Smoothing with Virtual Adversarial Training
Miyato, T., Maeda, S.-i., Koyama, M., Nakae, K., and Ishii, S · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, Alec, Metz, Luke, and Chintala, Soumith · 2015
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2016
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Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, Nicolas, McDaniel, Patrick, Wu, Xi, Jha, Somesh, and Swami, Ananthram · 2016
Cited alongside, same era.
Pixel Recurrent Neural Networks
van den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
Cited alongside, same era.
11 adversarial perturbations of deep neural networks
Warde-Farley, David and Goodfellow, Ian · 2016
Cited alongside, same era.
Began: Boundary equilibrium generative adversarial networks
Berthelot, David, Schumm, Tom, and Metz, Luke · 2017
Cited alongside, same era.
Boosting Adversarial Attacks with Momentum
Dong, Y., Liao, F., Pang, T., Su, H., Hu, X., Li, J., and Zhu, J · 2017
Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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Magnet: a two-pronged defense against adversarial examples
Meng, Dongyu and Chen, Hao · 2017
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cleverhans v2.0.0: an adversarial machine learning library
Papernot, Nicolas, Carlini, Nicholas, Goodfellow, Ian, Feinman, Reuben, Faghri, Fartash, Matyasko, Alexander, Hambardzumyan, Karen, Juang, Yi-Lin, Kurakin, Alexey, Sheatsley, Ryan, Garg, Abhibhav, and Lin, Yen-Chen · 2017
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Stabilizing training of generative adversarial networks through regularization
Roth, Kevin, Lucchi, Aurelien, Nowozin, Sebastian, and Hofmann, Thomas · 2017
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APE-GAN: Adversarial Perturbation Elimination with GAN
Shen, S., Jin, G., Gao, K., and Zhang, Y · 2017
Later among the works it cites.
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Cited alongside, same era.
Adversarial and Clean Data Are Not Twins
Gong, Z., Wang, W., and Ku, W.-S · 2017
Cited alongside, same era.
On the (Statistical) Detection of Adversarial Examples
Grosse, K., Manoharan, P., Papernot, N., Backes, M., and McDaniel, P · 2017
Cited alongside, same era.
Generative Adversarial Networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y
Cited in the paper.
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., and Szegedy, C
Cited in the paper.
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
Song, Y., Kim, T., Nowozin, S., Ermon, S., and Kushman, N · 2017
Later among the works it cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, Han, Rasul, Kashif, and Vollgraf, Roland · 2017
Later among the works it cites.
Defense-GAN: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, Rama Chellappa · 2018
Closest in time.