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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
P. Frossard
    AAML
ArXiv (abs)PDFHTML

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

50 / 2,353 papers shown
Retrieval-Augmented Convolutional Neural Networks for Improved
  Robustness against Adversarial Examples
Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples
Jake Zhao
Dong Wang
AAML
242
20
0
26 Feb 2018
Max-Mahalanobis Linear Discriminant Analysis Networks
Max-Mahalanobis Linear Discriminant Analysis NetworksInternational Conference on Machine Learning (ICML), 2018
Tianyu Pang
Chao Du
Jun Zhu
189
56
0
26 Feb 2018
Adversarial vulnerability for any classifier
Adversarial vulnerability for any classifier
Alhussein Fawzi
Hamza Fawzi
Omar Fawzi
AAML
278
259
0
23 Feb 2018
Deep Defense: Training DNNs with Improved Adversarial Robustness
Deep Defense: Training DNNs with Improved Adversarial Robustness
Ziang Yan
Yiwen Guo
Changshui Zhang
AAML
337
121
0
23 Feb 2018
Robustness of classifiers to uniform $\ell\_p$ and Gaussian noise
Robustness of classifiers to uniform ℓ_p\ell\_pℓ_p and Gaussian noise
Jean-Yves Franceschi
Alhussein Fawzi
Omar Fawzi
148
21
0
22 Feb 2018
Unravelling Robustness of Deep Learning based Face Recognition Against
  Adversarial Attacks
Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks
Gaurav Goswami
Nalini Ratha
Akshay Agarwal
Richa Singh
Mayank Vatsa
AAML
234
174
0
22 Feb 2018
Generalizable Adversarial Examples Detection Based on Bi-model Decision
  Mismatch
Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch
João Monteiro
Isabela Albuquerque
Zahid Akhtar
T. Falk
AAML
218
31
0
21 Feb 2018
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic
  Corrections
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections
Xin Zhang
Armando Solar-Lezama
Rishabh Singh
FAtt
215
65
0
21 Feb 2018
On Lyapunov exponents and adversarial perturbation
On Lyapunov exponents and adversarial perturbation
Vinay Uday Prabhu
Nishant Desai
John Whaley
AAML
94
7
0
20 Feb 2018
Shield: Fast, Practical Defense and Vaccination for Deep Learning using
  JPEG Compression
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
Nilaksh Das
Madhuri Shanbhogue
Shang-Tse Chen
Fred Hohman
Siwei Li
Li-Wei Chen
Michael E. Kounavis
Duen Horng Chau
FedMLAAML
206
245
0
19 Feb 2018
Divide, Denoise, and Defend against Adversarial Attacks
Divide, Denoise, and Defend against Adversarial Attacks
Seyed-Mohsen Moosavi-Dezfooli
A. Shrivastava
Oncel Tuzel
AAML
163
46
0
19 Feb 2018
Robustness of Rotation-Equivariant Networks to Adversarial Perturbations
Robustness of Rotation-Equivariant Networks to Adversarial Perturbations
Beranger Dumont
Simona Maggio
Pablo Montalvo
AAML
165
25
0
19 Feb 2018
DARTS: Deceiving Autonomous Cars with Toxic Signs
DARTS: Deceiving Autonomous Cars with Toxic Signs
Chawin Sitawarin
A. Bhagoji
Arsalan Mosenia
M. Chiang
Prateek Mittal
AAML
327
246
0
18 Feb 2018
ASP:A Fast Adversarial Attack Example Generation Framework based on
  Adversarial Saliency Prediction
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction
Fuxun Yu
Qide Dong
Xiang Chen
AAML
107
6
0
15 Feb 2018
Learning Privacy Preserving Encodings through Adversarial Training
Learning Privacy Preserving Encodings through Adversarial Training
Francesco Pittaluga
S. Koppal
Ayan Chakrabarti
PICV
331
78
0
14 Feb 2018
Identify Susceptible Locations in Medical Records via Adversarial
  Attacks on Deep Predictive Models
Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models
Mengying Sun
Fengyi Tang
Jinfeng Yi
Fei Wang
Jiayu Zhou
AAMLOODMedIm
156
66
0
13 Feb 2018
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial
  Examples
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
Felix Kreuk
A. Barak
Shir Aviv-Reuven
Moran Baruch
Benny Pinkas
Joseph Keshet
AAML
252
121
0
13 Feb 2018
Lipschitz-Margin Training: Scalable Certification of Perturbation
  Invariance for Deep Neural Networks
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
Yusuke Tsuzuku
Issei Sato
Masashi Sugiyama
AAML
493
345
0
12 Feb 2018
Fibres of Failure: Classifying errors in predictive processes
Fibres of Failure: Classifying errors in predictive processes
L. Carlsson
Gunnar Carlsson
Mikael Vejdemo-Johansson
AI4CE
212
4
0
09 Feb 2018
Blind Pre-Processing: A Robust Defense Method Against Adversarial
  Examples
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples
Adnan Siraj Rakin
Zhezhi He
Boqing Gong
Deliang Fan
AAML
169
4
0
05 Feb 2018
First-order Adversarial Vulnerability of Neural Networks and Input
  Dimension
First-order Adversarial Vulnerability of Neural Networks and Input Dimension
Carl-Johann Simon-Gabriel
Yann Ollivier
Léon Bottou
Bernhard Schölkopf
David Lopez-Paz
AAML
430
49
0
05 Feb 2018
Towards an Understanding of Neural Networks in Natural-Image Spaces
Towards an Understanding of Neural Networks in Natural-Image Spaces
Yifei Fan
A. Yezzi
AAMLGAN
100
2
0
27 Jan 2018
Deflecting Adversarial Attacks with Pixel Deflection
Deflecting Adversarial Attacks with Pixel Deflection
Aaditya (Adi) Prakash
N. Moran
Solomon Garber
Antonella DiLillo
J. Storer
AAML
244
325
0
26 Jan 2018
Generalizable Data-free Objective for Crafting Universal Adversarial
  Perturbations
Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations
Konda Reddy Mopuri
Aditya Ganeshan
R. Venkatesh Babu
AAML
486
222
0
24 Jan 2018
Adversarial Texts with Gradient Methods
Zhitao Gong
Wenlu Wang
Yangqiu Song
Basel Alomair
Wei-Shinn Ku
AAML
191
79
0
22 Jan 2018
Sparsity-based Defense against Adversarial Attacks on Linear Classifiers
Sparsity-based Defense against Adversarial Attacks on Linear Classifiers
Zhinus Marzi
S. Gopalakrishnan
Upamanyu Madhow
Ramtin Pedarsani
AAML
176
32
0
15 Jan 2018
A3T: Adversarially Augmented Adversarial Training
A3T: Adversarially Augmented Adversarial Training
Akram Erraqabi
A. Baratin
Yoshua Bengio
Damien Scieur
AAML
139
9
0
12 Jan 2018
Fooling End-to-end Speaker Verification by Adversarial Examples
Fooling End-to-end Speaker Verification by Adversarial Examples
Felix Kreuk
Yossi Adi
Moustapha Cissé
Joseph Keshet
AAML
202
218
0
10 Jan 2018
Less is More: Culling the Training Set to Improve Robustness of Deep
  Neural Networks
Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks
Yongshuai Liu
Jiyu Chen
Hao Chen
AAML
235
14
0
09 Jan 2018
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and
  Logos
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos
Chawin Sitawarin
A. Bhagoji
Arsalan Mosenia
Prateek Mittal
M. Chiang
AAML
236
70
0
09 Jan 2018
Spatially Transformed Adversarial Examples
Spatially Transformed Adversarial Examples
Chaowei Xiao
Jun-Yan Zhu
Yue Liu
Warren He
M. Liu
Basel Alomair
AAML
403
557
0
08 Jan 2018
Generating Adversarial Examples with Adversarial Networks
Generating Adversarial Examples with Adversarial Networks
Chaowei Xiao
Yue Liu
Jun-Yan Zhu
Warren He
M. Liu
Basel Alomair
GANAAML
365
990
0
08 Jan 2018
Denoising Dictionary Learning Against Adversarial Perturbations
Denoising Dictionary Learning Against Adversarial Perturbations
John Mitro
D. Bridge
Steven D. Prestwich
AAML
136
5
0
07 Jan 2018
Adversarial Perturbation Intensity Achieving Chosen Intra-Technique
  Transferability Level for Logistic Regression
Adversarial Perturbation Intensity Achieving Chosen Intra-Technique Transferability Level for Logistic Regression
Martin Gubri
AAML
49
0
0
06 Jan 2018
Neural Networks in Adversarial Setting and Ill-Conditioned Weight Space
Neural Networks in Adversarial Setting and Ill-Conditioned Weight Space
M. Singh
Abhishek Sinha
Balaji Krishnamurthy
AAML
81
8
0
03 Jan 2018
Did you hear that? Adversarial Examples Against Automatic Speech
  Recognition
Did you hear that? Adversarial Examples Against Automatic Speech Recognition
M. Alzantot
Bharathan Balaji
Mani B. Srivastava
AAML
133
266
0
02 Jan 2018
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A
  Survey
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Naveed Akhtar
Lin Wang
AAML
495
1,993
0
02 Jan 2018
A General Framework for Adversarial Examples with Objectives
A General Framework for Adversarial Examples with Objectives
Mahmood Sharif
Sruti Bhagavatula
Lujo Bauer
Michael K. Reiter
AAMLGAN
261
216
0
31 Dec 2017
Adversarial Patch
Adversarial Patch
Tom B. Brown
Dandelion Mané
Aurko Roy
Martín Abadi
Justin Gilmer
AAML
357
1,214
0
27 Dec 2017
Exploring the Space of Black-box Attacks on Deep Neural Networks
Exploring the Space of Black-box Attacks on Deep Neural Networks
A. Bhagoji
Warren He
Yue Liu
Basel Alomair
AAML
67
0
0
27 Dec 2017
Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks
Using LIP to Gloss Over Faces in Single-Stage Face Detection NetworksEuropean Conference on Computer Vision (ECCV), 2017
Siqi Yang
Arnold Wiliem
Shaokang Chen
Brian C. Lovell
CVBMAAML
144
3
0
22 Dec 2017
ReabsNet: Detecting and Revising Adversarial Examples
ReabsNet: Detecting and Revising Adversarial Examples
Jiefeng Chen
Zihang Meng
Changtian Sun
Weiliang Tang
Yinglun Zhu
AAMLGAN
143
4
0
21 Dec 2017
Adversarial Examples: Attacks and Defenses for Deep Learning
Adversarial Examples: Attacks and Defenses for Deep LearningIEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2017
Xiaoyong Yuan
Pan He
Qile Zhu
Xiaolin Li
SILMAAML
592
1,746
0
19 Dec 2017
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box
  Machine Learning Models
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
Wieland Brendel
Jonas Rauber
Matthias Bethge
AAML
313
1,472
0
12 Dec 2017
Training Ensembles to Detect Adversarial Examples
Training Ensembles to Detect Adversarial Examples
Alexander Bagnall
Razvan Bunescu
Gordon Stewart
AAML
98
40
0
11 Dec 2017
NAG: Network for Adversary Generation
NAG: Network for Adversary Generation
Konda Reddy Mopuri
Utkarsh Ojha
Utsav Garg
R. Venkatesh Babu
AAML
232
153
0
09 Dec 2017
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Battista Biggio
Fabio Roli
AAML
368
1,541
0
08 Dec 2017
Exploring the Landscape of Spatial Robustness
Exploring the Landscape of Spatial Robustness
Logan Engstrom
Brandon Tran
Dimitris Tsipras
Ludwig Schmidt
Aleksander Madry
AAML
397
381
0
07 Dec 2017
Adversarial Examples that Fool Detectors
Adversarial Examples that Fool Detectors
Jiajun Lu
Hussein Sibai
Evan Fabry
AAML
141
156
0
07 Dec 2017
Generative Adversarial Perturbations
Generative Adversarial Perturbations
Omid Poursaeed
Isay Katsman
Bicheng Gao
Serge J. Belongie
AAMLGANWIGM
481
385
0
06 Dec 2017
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