Who Cited It

The Limitations of Deep Learning in Adversarial Settings

2016 · 3,980 citations · 12 from inside this corpus

Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Ananthram Swami

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.

The Limitations of Deep Learning in Adversarial Settings (2016)The Limitations of Deep Learn…Learning representations by back-propagating errors (1986)Learning representations by b…Multilayer feedforward networks are universal approximators (1989)Multilayer feedforward networ…A Fast Learning Algorithm for Deep Belief Nets (2006)A Fast Learning Algorithm for…Multilayer feedforward networks are universal approximators (1989)Multilayer feedforward networ…Machine learning a probabilistic perspective (2012)Machine learning a probabilis…Explaining and Harnessing Adversarial Examples (2014)Explaining and Harnessing Adv…Learning Deep Architectures for AI (2009)Learning Deep Architectures f…Intriguing properties of neural networks (2013)Intriguing properties of neur…A unified architecture for natural language processing (2008)A unified architecture for na…Learning Deep Architectures for AI (2009)Learning Deep Architectures f…Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition (2011)Context-Dependent Pre-Trained…Long short-term memory recurrent neural network architectures for large scale acoustic mo… (2014)Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks (2015)Convolutional, Long Short-Ter…Practical Black-Box Attacks against Machine Learning (2017)Practical Black-Box Attacks a…Explainable AI: A Review of Machine Learning Interpretability Methods (2020)Explainable AI: A Review of M…Robust Physical-World Attacks on Deep Learning Visual Classification (2018)Robust Physical-World Attacks…Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey (2018)Threat of Adversarial Attacks…Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks (2018)Feature Squeezing: Detecting …Adversarial Examples: Attacks and Defenses for Deep Learning (2019)Adversarial Examples: Attacks…ZOO (2017)ZOOOne Pixel Attack for Fooling Deep Neural Networks (2019)One Pixel Attack for Fooling …Accessorize to a Crime (2016)Accessorize to a CrimeAdversarial Examples Are Not Easily Detected (2017)Adversarial Examples Are Not …Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria… (2016)Transferability in Machine Le…Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks (2019)Neural Cleanse: Identifying a…
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What this paper cites, inside the corpus

PaperYearCited
Learning representations by back-propagating errors198631,688
Multilayer feedforward networks are universal approximators198921,633
A Fast Learning Algorithm for Deep Belief Nets200616,540
Multilayer feedforward networks are universal approximators19899,357
Machine learning a probabilistic perspective20129,324
Explaining and Harnessing Adversarial Examples20148,146
Learning Deep Architectures for AI20096,951
Intriguing properties of neural networks20135,739
A unified architecture for natural language processing20085,207
Learning Deep Architectures for AI20095,077
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition20113,089
Long short-term memory recurrent neural network architectures for large scale acoustic mo…20142,986
Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks20151,646

What cites it, inside the corpus

PaperYearCited
Practical Black-Box Attacks against Machine Learning20173,540
Explainable AI: A Review of Machine Learning Interpretability Methods20202,907
Robust Physical-World Attacks on Deep Learning Visual Classification20182,154
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey20182,090
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks20181,882
Adversarial Examples: Attacks and Defenses for Deep Learning20191,786
ZOO20171,779
One Pixel Attack for Fooling Deep Neural Networks20191,715
Accessorize to a Crime20161,596
Adversarial Examples Are Not Easily Detected20171,428
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…20161,416
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks20191,404

Links

DOI · OpenAlex record

Topics

Adversarial Robustness in Machine LearningComputer Science
Explainable Artificial Intelligence (XAI)Computer Science
Ethics and Social Impacts of AISocial Sciences

Is this record sound?

complete

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

  • supports5 author record(s) attached.
  • supports57 reference(s) recorded.
  • supportsThe DOI's year agrees with the publication year.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:46+00:00.

sha256 bba2969b3567609a…