Ensemble Adversarial Training: Attacks and Defenses

Alexey Kurakin; Nicolas Papernot; Ian Goodfellow; Dan Boneh; Patrick McDaniel

Ensemble Adversarial Training: Attacks and Defenses

2017 · arXiv (Cornell University) · 1,861 citations · 6 from inside this corpus

Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

6 of 6 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

this paper works it cites works citing it node size = global citations · hover for the full title

Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

Advances and Open Problems in Federated Learning→Ensemble Adversarial Training: Attacks and DefensesLegacy edge — source detail unavailable
Boosting Adversarial Attacks with Momentum→Ensemble Adversarial Training: Attacks and DefensesLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Ensemble Adversarial Training: Attacks and DefensesLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→Ensemble Adversarial Training: Attacks and DefensesLegacy edge — source detail unavailable
ZOO→Ensemble Adversarial Training: Attacks and DefensesLegacy edge — source detail unavailable
A survey on security and privacy of federated learning→Ensemble Adversarial Training: Attacks and DefensesLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→ZOOLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Boosting Adversarial Attacks with MomentumLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→ZOOLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→Boosting Adversarial Attacks with MomentumLegacy edge — source detail unavailable

What cites it, inside the corpus

Paper	Year	Cited
Advances and Open Problems in Federated Learning	2020	5,383
Boosting Adversarial Attacks with Momentum	2018	3,040
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey	2018	2,090
Adversarial Examples: Attacks and Defenses for Deep Learning	2019	1,786
ZOO	2017	1,779
A survey on security and privacy of federated learning	2020	1,393

Links

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Topics

Adversarial Robustness in Machine Learning	Computer Science
COVID-19 diagnosis using AI	Medicine
Anomaly Detection Techniques and Applications	Computer Science

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partial

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supports5 author record(s) attached.
weakensNo references are recorded despite 1,861 citations. A paper this heavily cited did not cite nothing, so the record is incomplete.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:52+00:00.

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