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Understanding and characterizing the subspaces of adversarial examples aid in studying the robustness of deep neural networks (DNNs) to adversarial perturbations.
Finding nearest neighbors in growth-restricted metrics
David R Karger and Matthias Ruhl · 2002
Earlier work this paper cites.
Generalized expansion dimension
Michael E Houle, Hisashi Kashima, and Michael Nett · 2012
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 · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2016
Cited alongside, same era.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Ead: elastic-net attacks to deep neural networks via adversarial examples
Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh · 2017
Cited alongside, same era.
Magnet: a two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
Later among the works it cites.
Attacking the Madry defense model with L 1 L_{1} -based adversarial examples
Yash Sharma and Pin-Yu Chen · 2017
Later among the works it cites.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Closest in time.
Characterizing adversarial subspaces using local intrinsic dimensionality
Xingjun Ma, Bo Li, Yisen Wang, Sarah M Erfani, Sudanthi Wijewickrema, Michael E Houle, Grant Schoenebeck, Dawn Song, and James Bailey · 2018
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