Universal Adversarial Perturbations

Seyed-Mohsen Moosavi-Dezfooli; Alhussein Fawzi; Omar Fawzi; Pascal Frossard

doi:https://doi.org/10.1109/cvpr.2017.17

Universal Adversarial Perturbations

2017 · 2,744 citations · 9 from inside this corpus

Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard

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.

12 of 12 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.

Boosting Adversarial Attacks with Momentum→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
Explainable AI: A Review of Machine Learning Interpretability Methods→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
Robust Physical-World Attacks on Deep Learning Visual Classification→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
ZOO→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
One Pixel Attack for Fooling Deep Neural Networks→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
Universal Adversarial Perturbations→Explaining and Harnessing Adversarial ExamplesLegacy edge — source detail unavailable
Universal Adversarial Perturbations→Intriguing properties of neural networksLegacy edge — source detail unavailable
Universal Adversarial Perturbations→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
Explaining and Harnessing Adversarial Examples→Intriguing properties of neural networksLegacy 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
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→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
ZOO→Intriguing properties of neural networksLegacy edge — source detail unavailable
ZOO→Explaining and Harnessing Adversarial ExamplesLegacy edge — source detail unavailable
ZOO→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
ZOO→Feature Squeezing: Detecting Adversarial Examples in Deep Neural NetworksLegacy edge — source detail unavailable
One Pixel Attack for Fooling Deep Neural Networks→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
Boosting Adversarial Attacks with Momentum→Intriguing properties of neural networksLegacy edge — source detail unavailable
Boosting Adversarial Attacks with Momentum→Explaining and Harnessing Adversarial ExamplesLegacy edge — source detail unavailable
Robust Physical-World Attacks on Deep Learning Visual Classification→Intriguing properties of neural networksLegacy edge — source detail unavailable
Robust Physical-World Attacks on Deep Learning Visual Classification→Explaining and Harnessing Adversarial ExamplesLegacy edge — source detail unavailable
Robust Physical-World Attacks on Deep Learning Visual Classification→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks→Intriguing properties of neural networksLegacy edge — source detail unavailable
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks→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→Explaining and Harnessing Adversarial ExamplesLegacy 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→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→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→Intriguing properties of neural networksLegacy 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→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy 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
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→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
Explainable AI: A Review of Machine Learning Interpretability Methods→ZOOLegacy edge — source detail unavailable
Explainable AI: A Review of Machine Learning Interpretability Methods→One Pixel Attack for Fooling Deep Neural NetworksLegacy edge — source detail unavailable
Explainable AI: A Review of Machine Learning Interpretability Methods→Boosting Adversarial Attacks with MomentumLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
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

What cites it, inside the corpus

Paper	Year	Cited
Boosting Adversarial Attacks with Momentum	2018	3,040
Explainable AI: A Review of Machine Learning Interpretability Methods	2020	2,907
Robust Physical-World Attacks on Deep Learning Visual Classification	2018	2,154
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey	2018	2,090
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks	2018	1,882
Adversarial Examples: Attacks and Defenses for Deep Learning	2019	1,786
ZOO	2017	1,779
One Pixel Attack for Fooling Deep Neural Networks	2019	1,715
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks	2019	1,404

Links

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Topics

Adversarial Robustness in Machine Learning	Computer Science
Anomaly Detection Techniques and Applications	Computer Science
Advanced Neural Network Applications	Computer Science

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complete

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supports4 author record(s) attached.
supports37 reference(s) recorded.
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supportsA title is present.

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