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RobustBench: a standardized adversarial robustness benchmark

Oct 19, 2020
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein

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Fast-Convergent Federated Learning

Jul 26, 2020
Hung T. Nguyen, Vikash Sehwag, Seyyedali Hosseinalipour, Christopher G. Brinton, Mung Chiang, H. Vincent Poor

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A Critical Evaluation of Open-World Machine Learning

Jul 08, 2020
Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji, Prateek Mittal

* Presented at the ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning 

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Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks

Jun 24, 2020
Vikash Sehwag, Rajvardhan Oak, Mung Chiang, Prateek Mittal

* 6 pages, 5 figures 

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PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields

Jun 08, 2020
Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag, Prateek Mittal

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On Pruning Adversarially Robust Neural Networks

Feb 24, 2020
Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana

* 19 pages, 14 figures, 8 tables 

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Towards Compact and Robust Deep Neural Networks

Jun 14, 2019
Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana

* 14 pages, 9 figures, 7 tables 

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Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples

May 05, 2019
Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal

* 18 pages, 5 figures, 9 tables 

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