Built on

The mnist database of handwritten digits
Yann LeCun · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Robustness and regularization of support vector machines
Huan Xu, Constantine Caramanis, and Shie Mannor · 2009
Earlier work this paper cites.
Intriguing properties of neural networks
Original
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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.
Towards evaluating the robustness of neural networks
Original
Nicholas Carlini and David Wagner · 2016
Earlier work this paper cites.
The robust manifold defense: Adversarial training using generative models
Original
Andrew Ilyas, Ajil Jalal, Eirini Asteri, Constantinos Daskalakis, and Alexandros G Dimakis · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Original
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Earlier work this paper cites.

Similar

Synthesizing robust adversarial examples
Original
Anish Athalye and Ilya Sutskever · 2017
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Original
J. Zico Kolter and Eric Wong · 2017
Cited alongside, same era.
Certifiable distributional robustness with principled adversarial training
Original
Aman Sinha, Hongseok Namkoong, and John Duchi · 2017
Cited alongside, same era.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
A. Athalye, N. Carlini, and D. Wagner · 2018
Cited alongside, same era.

Adversarially robust generalization requires more data
Original
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Mądry · 2018
Closest in time.
Adversarial examples from computational constraints
Original
Sébastien Bubeck, Eric Price, and Ilya Razenshteyn · 2018
Closest in time.
Analysis of classifiers’ robustness to adversarial perturbations
Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2018
Closest in time.
Adversarial vulnerability for any classifier
A. Fawzi, H. Fawzi, and O. Fawzi · 2018
Closest in time.
Robustness of classifiers to uniform l and gaussian noise
Original
Jean-Yves Franceschi, Alhussein Fawzi, and Omar Fawzi · 2018
Closest in time.

alphaXiv searches the wider corpus for related work and actual follow-ups.

alphaXiv is searching for related work…

Reading the bibliography…

2018

Suggala, Arun Sai, Prasad, Adarsh, Nagarajan, Vaishnavh et al.

Recent works on adversarial perturbations show that there is an inherent trade-off between standard test accuracy and adversarial accuracy.

Specifically, they show that no classifier can simultaneously be robust to adversarial perturbations and achieve high standard test accuracy.
However, this is contrary to the standard notion that on tasks such as image classification, humans are robust classifiers with low error rate.
In this work, we show that the main reason behind this confusion is the inexact definition of adversarial perturbation that is used in the literature.

The mnist database of handwritten digits
Yann LeCun · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Robustness and regularization of support vector machines
Huan Xu, Constantine Caramanis, and Shie Mannor · 2009
Earlier work this paper cites.
Intriguing properties of neural networks
Original
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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.
Towards evaluating the robustness of neural networks
Original
Nicholas Carlini and David Wagner · 2016
Earlier work this paper cites.
The robust manifold defense: Adversarial training using generative models
Original
Andrew Ilyas, Ajil Jalal, Eirini Asteri, Constantinos Daskalakis, and Alexandros G Dimakis · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Original
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Earlier work this paper cites.

Synthesizing robust adversarial examples
Original
Anish Athalye and Ilya Sutskever · 2017
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Original
J. Zico Kolter and Eric Wong · 2017
Cited alongside, same era.
Certifiable distributional robustness with principled adversarial training
Original
Aman Sinha, Hongseok Namkoong, and John Duchi · 2017
Cited alongside, same era.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
A. Athalye, N. Carlini, and D. Wagner · 2018
Cited alongside, same era.

Adversarially robust generalization requires more data
Original
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Mądry · 2018
Closest in time.
Adversarial examples from computational constraints
Original
Sébastien Bubeck, Eric Price, and Ilya Razenshteyn · 2018
Closest in time.
Analysis of classifiers’ robustness to adversarial perturbations
Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2018
Closest in time.
Adversarial vulnerability for any classifier
A. Fawzi, H. Fawzi, and O. Fawzi · 2018
Closest in time.
Robustness of classifiers to uniform l and gaussian noise
Original
Jean-Yves Franceschi, Alhussein Fawzi, and Omar Fawzi · 2018
Closest in time.