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We explore adversarial robustness in the setting in which it is acceptable for a classifier to abstain---that is, output no class---on adversarial examples.
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Eric Wong, Frank R. Schmidt, and J. Zico Kolter · 1902
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Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, and Yaron Lipman · 1905
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Functional Adversarial Attacks
Cassidy Laidlaw and Soheil Feizi · 1906
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Adversarial Robustness through Local Linearization
Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, and Pushmeet Kohli · 1907
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A Finite Algorithm for the Minimum L
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Classification with a Reject Option Using a Hinge Loss
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Learning Multiple Layers of Features from Tiny Images
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MNIST Handwritten Digit Database
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Adam: A Method for Stochastic Optimization
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Intriguing Properties of Neural Networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Explaining and Harnessing Adversarial Examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Deepfool: a Simple and Accurate Method to Fool Deep Neural Networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Wide Residual Networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Adversarial Examples are Not Easily Detected: Bypassing Ten Detection Methods
Nicholas Carlini and David Wagner · 2017
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Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Towards Robust Detection of Adversarial Examples
Tianyu Pang, Chao Du, Yinpeng Dong, and Jun Zhu · 2018
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Spatially Transformed Adversarial Examples
Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, and Dawn Song · 2018
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Adversarial Example Detection and Classification with Asymmetrical Adversarial Training
Anonymous · 2019
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Big but Imperceptible Adversarial Perturbations via Semantic Manipulation
Anand Bhattad, Min Jin Chong, Kaizhao Liang, Bo Li, and David A. Forsyth · 2019
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A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2017
Cited alongside, same era.
Automatic Differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Robust Physical-World Attacks on Deep Learning Models
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song · 2018
Cited alongside, same era.
Detecting Adversarial Image Examples in Deep Neural Networks with Adaptive Noise Reduction
Bin Liang, Hongcheng Li, Miaoqiang Su, Xirong Li, Wenchang Shi, and XiaoFeng Wang · 2018
Cited alongside, same era.
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
Matthias Hein, Maksym Andriushchenko, and Julian Bitterwolf · 2019
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Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization
Daniel Jakubovitz and Raja Giryes · 2019
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Are Adversarial Examples Inevitable?
Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, and Tom Goldstein · 2019
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Theoretically Principled Trade-off Between Robustness and Accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, and Michael I. Jordan · 2019
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