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Multiple different approaches of generating adversarial examples have been proposed to attack deep neural networks.
Inversion of multilayer nets
Linden, Alexander and Kindermann, J · 1989
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, Ronald J · 1992
Earlier work this paper cites.
The mnist database of handwritten digits, 1998
LeCun, Yann, Cortes, Corinna, and Burges, Christopher JC · 1998
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, Jia, Dong, Wei, Socher, Richard, Li, Li-Jia, Li, Kai, and Fei-Fei, Li · 2009
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian, and Fergus, Rob · 2013
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, Anh Mai, Yosinski, Jason, and Clune, Jeff · 2014
Earlier work this paper cites.
The virtues of peer pressure: A simple method for discovering high-value mistakes
Baluja, Shumeet, Covell, Michele, and Sukthankar, Rahul · 2015
Earlier work this paper cites.
A neural algorithm of artistic style
Gatys, Leon A., Ecker, Alexander S., and Bethge, Matthias · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2015
Cited alongside, same era.
Inceptionism: Going deeper into neural networks
Mordvintsev, A., Olah, C., and Tyka, M · 2015
Cited alongside, same era.
The limitations of deep learning in adversarial settings
Papernot, Nicolas, McDaniel, Patrick, Jha, Somesh, Fredrikson, Matt, Celik, Z Berkay, and Swami, Ananthram · 2015
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Carlini, Nicholas and Wagner, David · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
Distributional smoothing with virtual adversarial training
Miyato, Takeru, Maeda, Shin-ichi, Koyama, Masanori, Nakae, Ken, and Ishii, Shin · 2016
Later among the works it cites.
Deconvolution and checkerboard artifacts
Odena, Augustus, Dumoulin, Vincent, and Olah, Chris · 2016
Later among the works it cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, Christian, Ioffe, Sergey, Vanhoucke, Vincent, and Alemi, Alex · 2016
Later among the works it cites.
Stealing machine learning models via prediction apis
Tramèr, Florian, Zhang, Fan, Juels, Ari, Reiter, Michael K, and Ristenpart, Thomas · 2016
Later among the works it cites.
Texture networks: Feed-forward synthesis of textures and stylized images
Ulyanov, Dmitry, Lebedev, Vadim, Vedaldi, Andrea, and Lempitsky, Victor S · 2016
Later among the works it cites.
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Johnson, Justin, Alahi, Alexandre, and Fei-Fei, Li · 2016
Cited alongside, same era.
Delving into transferable adversarial examples and black-box attacks
Liu, Yanpei, Chen, Xinyun, Liu, Chang, and Song, Dawn · 2016
Cited alongside, same era.
Generative adversarial nets
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua
Cited in the paper.
Explaining and harnessing adversarial examples
Goodfellow, Ian J, Shlens, Jonathon, and Szegedy, Christian
Cited in the paper.
Adversarial examples in the physical world
Kurakin, Alexey, Goodfellow, Ian J., and Bengio, Samy
Cited in the paper.
Adversarial machine learning at scale
Kurakin, Alexey, Goodfellow, Ian J., and Bengio, Samy
Cited in the paper.
Universal adversarial perturbations
Moosavi-Dezfooli, Seyed-Mohsen, Fawzi, Alhussein, Fawzi, Omar, and Frossard, Pascal
Cited in the paper.
On Detecting Adversarial Perturbations
Hendrik Metzen, J., Genewein, T., Fischer, V., and Bischoff, B · 2017
Closest in time.
Adversarial examples for generative models
Kos, Jernej, Fischer, Ian, and Song, Dawn · 2017
Closest in time.