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DeepFool: a simple and accurate method to fool deep neural networks

v1v2v3 (latest)

DeepFool: a simple and accurate method to fool deep neural networks

14 November 2015

Seyed-Mohsen Moosavi-Dezfooli

Alhussein Fawzi

ArXiv (abs)PDF HTML

Papers citing "DeepFool: a simple and accurate method to fool deep neural networks"

50 / 2,353 papers shown

Interpreting Adversarial Examples with Attributes

Interpreting Adversarial Examples with Attributes

196

6

0

17 Apr 2019

Adversarial Defense Through Network Profiling Based Path Extraction

Adversarial Defense Through Network Profiling Based Path Extraction

Jingwen Leng

Cong Guo

Yuhao Zhu

115

56

0

17 Apr 2019

Detecting the Unexpected via Image Resynthesis

Detecting the Unexpected via Image Resynthesis

Krishna Kanth Nakka

Mathieu Salzmann

279

205

0

16 Apr 2019

Generating Minimal Adversarial Perturbations with Integrated Adaptive Gradients

Chi-Man Pun

38

0

0

12 Apr 2019

Deep learning as optimal control problems: models and numerical methods

Deep learning as optimal control problems: models and numerical methods

Matthias Joachim Ehrhardt

Carola-Bibiane Schönlieb

272

88

0

11 Apr 2019

Black-Box Decision based Adversarial Attack with Symmetric
$α$-stable Distribution

Black-Box Decision based Adversarial Attack with Symmetric

α

-stable Distribution

Vignesh Srinivasan

Shinichi Nakajima

141

7

0

11 Apr 2019

Black-box Adversarial Attacks on Video Recognition Models

Black-box Adversarial Attacks on Video Recognition Models

247

162

0

10 Apr 2019

Towards Analyzing Semantic Robustness of Deep Neural Networks

Towards Analyzing Semantic Robustness of Deep Neural Networks

280

17

0

09 Apr 2019

Adversarial Audio: A New Information Hiding Method and Backdoor for
DNN-based Speech Recognition Models

Adversarial Audio: A New Information Hiding Method and Backdoor for DNN-based Speech Recognition Models

101

31

0

08 Apr 2019

JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks

JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks

N. Benjamin Erichson

Michael W. Mahoney

131

27

0

07 Apr 2019

HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

Martin J. Wainwright

518

758

0

03 Apr 2019

Adversarial Defense by Restricting the Hidden Space of Deep Neural
Networks

Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks

Salman Khan

281

157

0

01 Apr 2019

On the Vulnerability of CNN Classifiers in EEG-Based BCIs

On the Vulnerability of CNN Classifiers in EEG-Based BCIs

161

91

0

31 Mar 2019

Scaling up the randomized gradient-free adversarial attack reveals
overestimation of robustness using established attacks

Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks

Francesco Croce

Matthias Hein

133

32

0

27 Mar 2019

A geometry-inspired decision-based attack

A geometry-inspired decision-based attack

Seyed-Mohsen Moosavi-Dezfooli

141

54

0

26 Mar 2019

Defending against Whitebox Adversarial Attacks via Randomized
Discretization

Defending against Whitebox Adversarial Attacks via Randomized Discretization

Abigail Z. Jacobs

208

77

0

25 Mar 2019

Learning from Adversarial Features for Few-Shot Classification

Learning from Adversarial Features for Few-Shot Classification

Ziqiang Shi

91

10

0

25 Mar 2019

Robust Neural Networks using Randomized Adversarial Training

Robust Neural Networks using Randomized Adversarial Training

Alexandre Araujo

Laurent Meunier

Benjamin Négrevergne

258

36

0

25 Mar 2019

A Formalization of Robustness for Deep Neural Networks

A Formalization of Robustness for Deep Neural Networks

Alberto L. Sangiovanni-Vincentelli

Sanjit A. Seshia

132

33

0

24 Mar 2019

Variational Inference with Latent Space Quantization for Adversarial
Resilience

Variational Inference with Latent Space Quantization for Adversarial Resilience

Tarun Kumar Yadav

Dheeraj Mundhra

208

3

0

24 Mar 2019

Adversarial camera stickers: A physical camera-based attack on deep
learning systems

Adversarial camera stickers: A physical camera-based attack on deep learning systemsInternational Conference on Machine Learning (ICML), 2019

Juncheng Billy Li

Frank R. Schmidt

J. Zico Kolter

511

189

0

21 Mar 2019

On the Robustness of Deep K-Nearest Neighbors

On the Robustness of Deep K-Nearest Neighbors

Chawin Sitawarin

179

62

0

20 Mar 2019

Practical Hidden Voice Attacks against Speech and Speaker Recognition
Systems

Practical Hidden Voice Attacks against Speech and Speaker Recognition SystemsNetwork and Distributed System Security Symposium (NDSS), 2019

Washington Garcia

Christian Peeters

Patrick Traynor

Kevin R. B. Butler

Joseph N. Wilson

167

178

0

18 Mar 2019

Generating Adversarial Examples With Conditional Generative Adversarial
Net

Generating Adversarial Examples With Conditional Generative Adversarial NetInternational Conference on Pattern Recognition (ICPR), 2018

130

25

0

18 Mar 2019

On Evaluation of Adversarial Perturbations for Sequence-to-Sequence
Models

On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models

Xian Li

Graham Neubig

183

143

0

15 Mar 2019

Attribution-driven Causal Analysis for Detection of Adversarial Examples

Attribution-driven Causal Analysis for Detection of Adversarial Examples

Sumit Kumar Jha

173

17

0

14 Mar 2019

Paradox in Deep Neural Networks: Similar yet Different while Different
yet Similar

Paradox in Deep Neural Networks: Similar yet Different while Different yet Similar

K. Gegenfurtner

85

5

0

12 Mar 2019

Fisher-Bures Adversary Graph Convolutional Networks

Fisher-Bures Adversary Graph Convolutional Networks

198

35

0

11 Mar 2019

Neural Network Model Extraction Attacks in Edge Devices by Hearing
Architectural Hints

Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints

Lei Deng

169

40

0

10 Mar 2019

Defense Against Adversarial Images using Web-Scale Nearest-Neighbor
Search

Defense Against Adversarial Images using Web-Scale Nearest-Neighbor SearchComputer Vision and Pattern Recognition (CVPR), 2019

Abhimanyu Dubey

Laurens van der Maaten

245

66

0

05 Mar 2019

Safety Verification and Robustness Analysis of Neural Networks via
Quadratic Constraints and Semidefinite Programming

Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite ProgrammingIEEE Transactions on Automatic Control (IEEE TAC), 2019

George J. Pappas

312

256

0

04 Mar 2019

A Fundamental Performance Limitation for Adversarial Classification

A Fundamental Performance Limitation for Adversarial ClassificationIEEE Control Systems Letters (L-CSS), 2019

Abed AlRahman Al Makdah

Fabio Pasqualetti

158

9

0

04 Mar 2019

PuVAE: A Variational Autoencoder to Purify Adversarial Examples

PuVAE: A Variational Autoencoder to Purify Adversarial ExamplesIEEE Access (IEEE Access), 2019

184

90

0

02 Mar 2019

Towards Understanding Adversarial Examples Systematically: Exploring
Data Size, Task and Model Factors

Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors

150

19

0

28 Feb 2019

Adversarial Attack and Defense on Point Sets

Adversarial Attack and Defense on Point Sets

209

145

0

28 Feb 2019

Verification of Non-Linear Specifications for Neural Networks

Verification of Non-Linear Specifications for Neural Networks

Krishnamurthy Dvijotham

Brendan O'Donoghue

Robert Stanforth

181

45

0

25 Feb 2019

Adversarial attacks hidden in plain sight

Adversarial attacks hidden in plain sight

Jan Philip Göpfert

Barbara Hammer

111

20

0

25 Feb 2019

Visualization, Discriminability and Applications of Interpretable Saak
Features

Visualization, Discriminability and Applications of Interpretable Saak Features

Abinaya Manimaran

231

8

0

25 Feb 2019

Adversarial Reinforcement Learning under Partial Observability in
Autonomous Computer Network Defence

Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence

David Hubczenko

Benjamin I. P. Rubinstein

233

6

0

25 Feb 2019

A Deep, Information-theoretic Framework for Robust Biometric Recognition

A Deep, Information-theoretic Framework for Robust Biometric Recognition

75

1

0

23 Feb 2019

On the Sensitivity of Adversarial Robustness to Input Data Distributions

On the Sensitivity of Adversarial Robustness to Input Data Distributions

Kry Yik-Chau Lui

121

64

0

22 Feb 2019

Perceptual Quality-preserving Black-Box Attack against Deep Learning
Image Classifiers

Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers

Diego Gragnaniello

Francesco Marra

154

32

0

20 Feb 2019

There are No Bit Parts for Sign Bits in Black-Box Attacks

There are No Bit Parts for Sign Bits in Black-Box Attacks

Abdullah Al-Dujaili

Una-May O’Reilly

345

22

0

19 Feb 2019

On Evaluating Adversarial Robustness

On Evaluating Adversarial Robustness

Nicholas Carlini

Nicolas Papernot

Wieland Brendel

Dimitris Tsipras

Aleksander Madry

476

961

0

18 Feb 2019

Mockingbird: Defending Against Deep-Learning-Based Website
Fingerprinting Attacks with Adversarial Traces

Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces

Mohammad Saidur Rahman

Mohsen Imani

319

94

0

18 Feb 2019

AuxBlocks: Defense Adversarial Example via Auxiliary Blocks

AuxBlocks: Defense Adversarial Example via Auxiliary Blocks

76

8

0

18 Feb 2019

DeepFault: Fault Localization for Deep Neural Networks

DeepFault: Fault Localization for Deep Neural Networks

Hasan Ferit Eniser

Simos Gerasimou

141

95

0

15 Feb 2019

Can Intelligent Hyperparameter Selection Improve Resistance to
Adversarial Examples?

Can Intelligent Hyperparameter Selection Improve Resistance to Adversarial Examples?

87

1

0

14 Feb 2019

On instabilities of deep learning in image reconstruction - Does AI come
at a cost?

On instabilities of deep learning in image reconstruction - Does AI come at a cost?

265

666

0

14 Feb 2019

The Odds are Odd: A Statistical Test for Detecting Adversarial Examples

The Odds are Odd: A Statistical Test for Detecting Adversarial ExamplesInternational Conference on Machine Learning (ICML), 2019

221

182

0

13 Feb 2019

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