Adversarial Attacks and Defences Competition

03/31/2018

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To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the structure and organization of the competition and the solutions developed by several of the top-placing teams.

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Adversarial Attacks and Defences Competition

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Adversarial Vision Challenge

CAAD 2018: Generating Transferable Adversarial Examples

Adversarial Attacks on ML Defense Models Competition

Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise

amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth

Technologies and Workflow of Creative Coding Projects: Examples from the Google DevArt Competition

Adversarial Attacks and Defences Competition

Related Research

Imperceptible Adversarial Attacks on Tabular Data

Adversarial Vision Challenge

CAAD 2018: Generating Transferable Adversarial Examples

Adversarial Attacks on ML Defense Models Competition

Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise

amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth

Technologies and Workflow of Creative Coding Projects: Examples from the Google DevArt Competition