Who Cited It

Adversarial Examples Are Not Easily Detected

2017 · 1,428 citations · 3 from inside this corpus

Nicholas Carlini, David Wagner

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.

Adversarial Examples Are Not Easily Detected (2017)Adversarial Examples Are Not …Dropout: a simple way to prevent neural networks from overfitting (2014)Dropout: a simple way to prev…Mastering the game of Go with deep neural networks and tree search (2016)Mastering the game of Go with…Explaining and Harnessing Adversarial Examples (2014)Explaining and Harnessing Adv…Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Tr… (2016)Google's Neural Machine Trans…DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks (2016)DeepFool: A Simple and Accura…The Limitations of Deep Learning in Adversarial Settings (2016)The Limitations of Deep Learn…A kernel two-sample test (2012)A kernel two-sample testTransferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria… (2016)Transferability in Machine Le…Adversarial Examples: Attacks and Defenses for Deep Learning (2019)Adversarial Examples: Attacks…One Pixel Attack for Fooling Deep Neural Networks (2019)One Pixel Attack for Fooling …Towards Deep Learning Models Resistant to Adversarial Attacks (2017)Towards Deep Learning Models …
11 of 11 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.
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What this paper cites, inside the corpus

PaperYearCited
Dropout: a simple way to prevent neural networks from overfitting201434,236
Mastering the game of Go with deep neural networks and tree search201616,014
Explaining and Harnessing Adversarial Examples20148,146
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Tr…20165,668
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks20165,392
The Limitations of Deep Learning in Adversarial Settings20163,980
A kernel two-sample test20122,224
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…20161,416

What cites it, inside the corpus

PaperYearCited
Adversarial Examples: Attacks and Defenses for Deep Learning20191,786
One Pixel Attack for Fooling Deep Neural Networks20191,715
Towards Deep Learning Models Resistant to Adversarial Attacks20171,536

Links

DOI · OpenAlex record

Topics

Adversarial Robustness in Machine LearningComputer Science
Explainable Artificial Intelligence (XAI)Computer Science
Advanced Malware Detection TechniquesComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports2 author record(s) attached.
  • supports37 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:59+00:00.

sha256 59007f057c7dae86…