Adversarially Robust Learning Could Leverage Computational Hardness

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Adversarially Robust Learning Could Leverage Computational Hardness

28 May 2019

Saeed Mahloujifar

Mohammad Mahmoody

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Papers citing "Adversarially Robust Learning Could Leverage Computational Hardness"

8 / 8 papers shown

Title
Hindering Adversarial Attacks with Implicit Neural Representations Andrei A. Rusu D. A. Calian Sven Gowal R. Hadsell AAML 165 4 0 22 Oct 2022
When are Local Queries Useful for Robust Learning? Pascale Gourdeau Varun Kanade Marta Z. Kwiatkowska J. Worrell OOD 77 1 0 12 Oct 2022
Planting Undetectable Backdoors in Machine Learning Models S. Goldwasser Michael P. Kim Vinod Vaikuntanathan Or Zamir AAML 54 73 0 14 Apr 2022
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them Florian Tramèr AAML 89 71 0 24 Jul 2021
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries A. Bhagoji Daniel Cullina Vikash Sehwag Prateek Mittal AAML OOD 73 3 0 16 Apr 2021
Black-box Certification and Learning under Adversarial Perturbations H. Ashtiani Vinayak Pathak Ruth Urner AAML 64 20 0 30 Jun 2020
Towards Deep Learning Models Resistant to Large Perturbations Amirreza Shaeiri Rozhin Nobahari M. Rohban OOD AAML 81 12 0 30 Mar 2020
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models Lin Chen Yifei Min Mingrui Zhang Amin Karbasi OOD 82 64 0 11 Feb 2020

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