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Pin‐Yu Chen; Huan Zhang; Yash Sharma; Jinfeng Yi; Cho‐Jui Hsieh

doi:https://doi.org/10.1145/3128572.3140448

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2017 · 1,779 citations · 3 from inside this corpus

Pin‐Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, Cho‐Jui Hsieh

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.

16 of 16 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.

Explainable AI: A Review of Machine Learning Interpretability Methods→ZOOLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→ZOOLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→ZOOLegacy edge — source detail unavailable
ZOO→Adam: A Method for Stochastic OptimizationLegacy edge — source detail unavailable
ZOO→Distilling the Knowledge in a Neural NetworkLegacy edge — source detail unavailable
ZOO→Explaining and Harnessing Adversarial ExamplesLegacy edge — source detail unavailable
ZOO→Intriguing properties of neural networksLegacy edge — source detail unavailable
ZOO→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
ZOO→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
ZOO→Practical Black-Box Attacks against Machine LearningLegacy edge — source detail unavailable
ZOO→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
ZOO→Feature Squeezing: Detecting Adversarial Examples in Deep Neural NetworksLegacy edge — source detail unavailable
ZOO→Ensemble Adversarial Training: Attacks and DefensesLegacy edge — source detail unavailable
ZOO→Adversarial Examples in the Physical WorldLegacy edge — source detail unavailable
ZOO→Towards Deep Learning Models Resistant to Adversarial AttacksLegacy edge — source detail unavailable
ZOO→Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…Legacy edge — source detail unavailable
Explaining and Harnessing Adversarial Examples→Intriguing properties of neural networksLegacy edge — source detail unavailable
The Limitations of Deep Learning in Adversarial Settings→Intriguing properties of neural networksLegacy edge — source detail unavailable
The Limitations of Deep Learning in Adversarial Settings→Explaining and Harnessing Adversarial ExamplesLegacy edge — source detail unavailable
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks→Intriguing properties of neural networksLegacy edge — source detail unavailable
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks→Explaining and Harnessing Adversarial ExamplesLegacy edge — source detail unavailable
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…→Distilling the Knowledge in a Neural NetworkLegacy edge — source detail unavailable
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
Adversarial Examples in the Physical World→Explaining and Harnessing Adversarial ExamplesLegacy edge — source detail unavailable
Universal Adversarial Perturbations→Intriguing properties of neural networksLegacy edge — source detail unavailable
Universal Adversarial Perturbations→Explaining and Harnessing Adversarial ExamplesLegacy edge — source detail unavailable
Universal Adversarial Perturbations→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
Practical Black-Box Attacks against Machine Learning→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks→Intriguing properties of neural networksLegacy edge — source detail unavailable
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks→Explaining and Harnessing Adversarial ExamplesLegacy edge — source detail unavailable
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks→Adversarial Examples in the Physical WorldLegacy edge — source detail unavailable
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
Towards Deep Learning Models Resistant to Adversarial Attacks→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
Towards Deep Learning Models Resistant to Adversarial Attacks→Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…Legacy edge — source detail unavailable
Towards Deep Learning Models Resistant to Adversarial Attacks→Feature Squeezing: Detecting Adversarial Examples in Deep Neural NetworksLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Intriguing properties of neural networksLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Distilling the Knowledge in a Neural NetworkLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Explaining and Harnessing Adversarial ExamplesLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…Legacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Adversarial Examples in the Physical WorldLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Practical Black-Box Attacks against Machine LearningLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Feature Squeezing: Detecting Adversarial Examples in Deep Neural NetworksLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Towards Deep Learning Models Resistant to Adversarial AttacksLegacy 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→Intriguing properties of neural networksLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→Distilling the Knowledge in a Neural NetworkLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→Explaining and Harnessing Adversarial ExamplesLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…Legacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→Adversarial Examples in the Physical WorldLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→Ensemble Adversarial Training: Attacks and DefensesLegacy edge — source detail unavailable
Explainable AI: A Review of Machine Learning Interpretability Methods→Explaining and Harnessing Adversarial ExamplesLegacy edge — source detail unavailable
Explainable AI: A Review of Machine Learning Interpretability Methods→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
Explainable AI: A Review of Machine Learning Interpretability Methods→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
Explainable AI: A Review of Machine Learning Interpretability Methods→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
Explainable AI: A Review of Machine Learning Interpretability Methods→Practical Black-Box Attacks against Machine LearningLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Adam: A Method for Stochastic Optimization	2014	84,698
Distilling the Knowledge in a Neural Network	2015	14,099
Explaining and Harnessing Adversarial Examples	2014	8,146
Intriguing properties of neural networks	2013	5,739
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks	2016	5,392
The Limitations of Deep Learning in Adversarial Settings	2016	3,980
Practical Black-Box Attacks against Machine Learning	2017	3,540
Universal Adversarial Perturbations	2017	2,744
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks	2018	1,882
Ensemble Adversarial Training: Attacks and Defenses	2017	1,861
Adversarial Examples in the Physical World	2018	1,842
Towards Deep Learning Models Resistant to Adversarial Attacks	2017	1,536
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…	2016	1,416

What cites it, inside the corpus

Paper	Year	Cited
Explainable AI: A Review of Machine Learning Interpretability Methods	2020	2,907
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

Links

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Topics

Adversarial Robustness in Machine Learning	Computer Science
Rabies epidemiology and control	Immunology and Microbiology
Bacillus and Francisella bacterial research	Biochemistry, Genetics and Molecular Biology

Is this record sound?

complete

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

supports5 author record(s) attached.
supports47 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 2026-09-04T03:58:53+00:00.

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