Adversarial Vision Challenge
Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar, Veliqi, Marcel Salath\'e, Sharada P. Mohanty, Matthias Bethge

TL;DR
The paper discusses the NIPS 2018 Adversarial Vision Challenge, a competition aimed at advancing robust machine vision models and understanding adversarial attacks through standardized benchmarks.
Contribution
It introduces a structured competition to evaluate and improve the robustness of machine vision models against adversarial attacks.
Findings
Established benchmarks for adversarial robustness
Facilitated progress in developing resilient vision models
Identified vulnerabilities in current machine vision systems
Abstract
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. This document is an updated version of our competition proposal that was accepted in the competition track of 32nd Conference on Neural Information Processing Systems (NIPS 2018).
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Taxonomy
TopicsAdversarial Robustness in Machine Learning
