DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks

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

doi:https://doi.org/10.1109/cvpr.2016.282

DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks

2016 · 5,392 citations · 11 from inside this corpus

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

The source holds an abstract for this work, but its best open-access copy is under cc-by-nc-nd, which does not permit us to republish the text. Read it at the source below.

14 of 14 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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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→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
Universal Adversarial Perturbations→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy 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
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
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy 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
ZOO→DeepFool: A Simple and Accurate Method to Fool 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
Towards Deep Learning Models Resistant to Adversarial Attacks→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
Adversarial Examples Are Not Easily Detected→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
Explaining nonlinear classification decisions with deep Taylor decomposition→DeepFool: A Simple and Accurate Method to Fool Deep Neural NetworksLegacy edge — source detail unavailable
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks→Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…Legacy edge — source detail unavailable
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks→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
Intriguing properties of neural networks→Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…Legacy edge — source detail unavailable
Explaining and Harnessing Adversarial Examples→Intriguing properties of neural networksLegacy edge — source detail unavailable
Explaining nonlinear classification decisions with deep Taylor decomposition→Intriguing properties of neural networksLegacy 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
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→Universal Adversarial PerturbationsLegacy 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
Towards Deep Learning Models Resistant to Adversarial Attacks→Adversarial Examples Are Not Easily DetectedLegacy 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→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
ZOO→Feature Squeezing: Detecting Adversarial Examples in Deep Neural NetworksLegacy edge — source detail unavailable
ZOO→Towards Deep Learning Models Resistant to Adversarial AttacksLegacy edge — source detail unavailable
One Pixel Attack for Fooling Deep Neural Networks→Universal Adversarial PerturbationsLegacy edge — source detail unavailable
One Pixel Attack for Fooling Deep Neural Networks→Adversarial Examples Are Not Easily DetectedLegacy 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→Universal Adversarial PerturbationsLegacy 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→Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…Legacy 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→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→ZOOLegacy 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→Universal Adversarial PerturbationsLegacy 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→Adversarial Examples Are Not Easily DetectedLegacy edge — source detail unavailable
Adversarial Examples Are Not Easily Detected→Explaining and Harnessing Adversarial ExamplesLegacy 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→Explaining nonlinear classification decisions with deep Taylor decompositionLegacy 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→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

What this paper cites, inside the corpus

Paper	Year	Cited
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…	2012	10,399
Explaining and Harnessing Adversarial Examples	2014	8,146
Intriguing properties of neural networks	2013	5,739

What cites it, inside the corpus

Paper	Year	Cited
Explainable AI: A Review of Machine Learning Interpretability Methods	2020	2,907
Universal Adversarial Perturbations	2017	2,744
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
Towards Deep Learning Models Resistant to Adversarial Attacks	2017	1,536
Adversarial Examples Are Not Easily Detected	2017	1,428
Explaining nonlinear classification decisions with deep Taylor decomposition	2016	1,413

Links

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Topics

Adversarial Robustness in Machine Learning	Computer Science
Advanced Neural Network Applications	Computer Science
Explainable Artificial Intelligence (XAI)	Computer Science

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complete

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supports3 author record(s) attached.
supports25 reference(s) recorded.
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supportsA title is present.

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