Built on

The dimension of almost spherical sections of convex bodies
Original
Tadeusz Figiel, Joram Lindenstrauss, and Vitali D Milman · 1977
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Original
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Original
Diederik P. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Foveation-based mechanisms alleviate adversarial examples
Original
Yan Luo, Xavier Boix, Gemma Roig, Tomaso Poggio, and Qi Zhao · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
Earlier work this paper cites.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Earlier work this paper cites.
Synthesizing robust adversarial examples
Original
Anish Athalye and Ilya Sutskever · 2017
Earlier work this paper cites.
Parseval networks: Improving robustness to adversarial examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
Earlier work this paper cites.

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Hyeungill Lee, Sungyeob Han, and Jungwoo Lee · 2017
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Cited alongside, same era.
Detecting adversarial samples from artifacts

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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Later among the works it cites.
Breaking the madry defense model with l1-based adversarial examples
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Yash Sharma and Pin-Yu Chen · 2017
Later among the works it cites.
Reluplex: An efficient smt solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
Later among the works it cites.
Measuring the tendency of cnns to learn surface statistical regularities
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Jason Jo and Yoshua Bengio · 2017
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Defense-gan: Protecting classifiers against adversarial attacks using generative models
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Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
Closest in time.

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2018

Adversarial Spheres

Gilmer, Justin, Metz, Luke, Faghri, Fartash et al.

Understand

State of the art computer vision models have been shown to be vulnerable to small adversarial perturbations of the input.

In other words, most images in the data distribution are both correctly classified by the model and are very close to a visually similar misclassified image.
Despite substantial research interest, the cause of the phenomenon is still poorly understood and remains unsolved.
We hypothesize that this counter intuitive behavior is a naturally occurring result of the high dimensional geometry of the data manifold.

Built on

The dimension of almost spherical sections of convex bodies
Original
Tadeusz Figiel, Joram Lindenstrauss, and Vitali D Milman · 1977
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Original
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Original
Diederik P. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Foveation-based mechanisms alleviate adversarial examples
Original
Yan Luo, Xavier Boix, Gemma Roig, Tomaso Poggio, and Qi Zhao · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
Earlier work this paper cites.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Earlier work this paper cites.
Synthesizing robust adversarial examples
Original
Anish Athalye and Ilya Sutskever · 2017
Earlier work this paper cites.
Parseval networks: Improving robustness to adversarial examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
Earlier work this paper cites.

Similar

Towards deep learning models resistant to adversarial examples
Original
Aleksander Madry, Aleksander Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Cited alongside, same era.
Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Original
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman · 2017
Cited alongside, same era.
Generative adversarial trainer: Defense to adversarial perturbations with gan
Original
Hyeungill Lee, Sungyeob Han, and Jungwoo Lee · 2017
Cited alongside, same era.
Dense associative memory is robust to adversarial inputs
Original
Dmitry Krotov and John J Hopfield · 2017
Cited alongside, same era.
Detecting adversarial samples from artifacts

Then

Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Later among the works it cites.
Breaking the madry defense model with l1-based adversarial examples
Original
Yash Sharma and Pin-Yu Chen · 2017
Later among the works it cites.
Reluplex: An efficient smt solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
Later among the works it cites.
Measuring the tendency of cnns to learn surface statistical regularities
Original
Jason Jo and Yoshua Bengio · 2017
Later among the works it cites.
Defense-gan: Protecting classifiers against adversarial attacks using generative models
Original
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
Closest in time.