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Under this metric, we provide a tractable upper bound serving as a robustness certificate by exploiting the duality. Table 5. Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, Zico Kolter. Patch adversarial attacks on images, in which the attacker can distort pixels within a region of bounded size, are an important threat model since they provide a quantitative model for physical adversarial attacks. Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, Zico Kolter [virtual poster/presentation] Certified Adversarial Robustness via Randomized Smoothing ICML 2019 . Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. In ICLR, 2020. In ICML. Certified robustness for top-k predictions against adversarial perturbations via randomized smoothing. In ICML. However, existing guarantees for such classifiers are unnecessarily loose. Certified adversarial robustness via randomized smoothing. We derive a variant which provides a deterministic, analytical bound, sidestepping the probabilistic certificates that traditionally result from the sampling subprocedure. Certified Robustness to Label-Flipping Attacks via Randomized Smoothing Elan Rosenfeld , E. Winston , P. Ravikumar , J. Title: Certified Robustness to Label-Flipping Attacks via Randomized Smoothing Authors: Elan Rosenfeld , Ezra Winston , Pradeep Ravikumar , J. Zico Kolter (Submitted on 7 Feb 2020 ( v1 ), revised 13 Jun 2020 (this version, v2), latest version 11 Aug 2020 ( v4 )) This paper presents a new understanding of randomized smoothing. ∙ 5 ∙ share . Below you find a number of papers presented at international conferences and published in renowned journals sorted by date, topics and conferences. This repository contains code and trained models for the paper Certified Adversarial Robustness via Randomized Smoothing by Jeremy Cohen, Elan Rosenfeld, and Zico Kolter.. Randomized smoothing is a provable adversarial defense in L2 norm which scales to ImageNet. In ICLR, 2020. Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing Jinyuan Jia, Binghui Wang, Xiaoyu Cao, Neil Zhenqiang Gong Duke University {jinyuan.jia,binghui.wang,xiaoyu.cao,neil.gong}@duke.edu ABSTRACT Community detection plays a key role in understanding graph structure. Adversarial attack on graph structured data. Elan Rosenfeld's 4 research works with 220 reads, including: The Risks of Invariant Risk Minimization ∙ 6 ∙ share . Recent work has shown that any classifier which classifies well under Gaussian noise can be leveraged to create a new classifier that is provably robust to adversarial perturbations in L2 norm. J. Zico Kolter. However, most existing methods only leverage Gaussian smoothing noise and only work for $\\ell_2$ perturbation. Randomized classifiers have been shown to provide a promising approach for achieving certified robustness against adversarial attacks in deep learning. Xiaoyu Cao, and Neil Zhenqiang Gong. Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing. Certified Robustness to Label-Flipping Attacks via Randomized Smoothing Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, J. Zico Kolter In International Conference on Machine Learning, 2020. Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #64 in Posters Wed » We show how to turn any classifier that classifies well under Gaussian noise into a new classifier that is certifiably robust to adversarial perturbations under the L2 norm. Certified Robustness to adversarial Label-Flipping Attacks via Randomized Smoothing sidestepping the probabilistic certificates that result! 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