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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 / 2,298 papers shown
Title
Active Subspace of Neural Networks: Structural Analysis and Universal
  Attacks
Active Subspace of Neural Networks: Structural Analysis and Universal Attacks
Chunfeng Cui
Kaiqi Zhang
Talgat Daulbaev
Julia Gusak
Ivan Oseledets
Zheng Zhang
AAML
61
25
0
29 Oct 2019
EdgeFool: An Adversarial Image Enhancement Filter
EdgeFool: An Adversarial Image Enhancement Filter
Ali Shahin Shamsabadi
Changjae Oh
Andrea Cavallaro
AAML
54
23
0
27 Oct 2019
Adversarial Defense via Local Flatness Regularization
Adversarial Defense via Local Flatness Regularization
Jia Xu
Yiming Li
Yong Jiang
Shutao Xia
AAML
103
18
0
27 Oct 2019
Understanding and Quantifying Adversarial Examples Existence in Linear
  Classification
Understanding and Quantifying Adversarial Examples Existence in Linear Classification
Xupeng Shi
A. Ding
AAML
48
3
0
27 Oct 2019
ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries
ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries
Xingxing Zhang
Shupeng Gui
Zhenfeng Zhu
Yao Zhao
Ji Liu
VLM
56
6
0
24 Oct 2019
Adversarial Example Detection by Classification for Deep Speech
  Recognition
Adversarial Example Detection by Classification for Deep Speech Recognition
Saeid Samizade
Zheng-Hua Tan
Chao Shen
X. Guan
AAML
79
35
0
22 Oct 2019
An Alternative Surrogate Loss for PGD-based Adversarial Testing
An Alternative Surrogate Loss for PGD-based Adversarial Testing
Sven Gowal
J. Uesato
Chongli Qin
Po-Sen Huang
Timothy A. Mann
Pushmeet Kohli
AAML
107
90
0
21 Oct 2019
Good, Better, Best: Textual Distractors Generation for Multiple-Choice
  Visual Question Answering via Reinforcement Learning
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning
Jiaying Lu
Xin Ye
Yi Ren
Yezhou Yang
78
10
0
21 Oct 2019
Adversarial Attacks on Spoofing Countermeasures of automatic speaker
  verification
Adversarial Attacks on Spoofing Countermeasures of automatic speaker verification
Songxiang Liu
Haibin Wu
Hung-yi Lee
Helen Meng
AAML
68
65
0
19 Oct 2019
SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking
SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking
Qing Guo
Xiaofei Xie
Felix Juefei-Xu
Lei Ma
Zhongguo Li
Wanli Xue
Wei Feng
Yang Liu
AAML
56
4
0
19 Oct 2019
A Fast Saddle-Point Dynamical System Approach to Robust Deep Learning
A Fast Saddle-Point Dynamical System Approach to Robust Deep Learning
Yasaman Esfandiari
Aditya Balu
K. Ebrahimi
Umesh Vaidya
N. Elia
Soumik Sarkar
OOD
59
3
0
18 Oct 2019
Instance adaptive adversarial training: Improved accuracy tradeoffs in
  neural nets
Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets
Yogesh Balaji
Tom Goldstein
Judy Hoffman
AAML
205
103
0
17 Oct 2019
Adversarial Examples for Models of Code
Adversarial Examples for Models of Code
Noam Yefet
Uri Alon
Eran Yahav
SILMAAMLMLAU
132
169
0
15 Oct 2019
How a minimal learning agent can infer the existence of unobserved
  variables in a complex environment
How a minimal learning agent can infer the existence of unobserved variables in a complex environment
K. Ried
B. Eva
Thomas Müller
Hans J. Briegel
70
15
0
15 Oct 2019
Understanding Misclassifications by Attributes
Understanding Misclassifications by Attributes
Sadaf Gulshad
Zeynep Akata
J. H. Metzen
A. Smeulders
AAML
95
0
0
15 Oct 2019
DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural
  Networks
DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks
Fuyuan Zhang
Sankalan Pal Chowdhury
M. Christakis
AAML
58
8
0
14 Oct 2019
Man-in-the-Middle Attacks against Machine Learning Classifiers via
  Malicious Generative Models
Man-in-the-Middle Attacks against Machine Learning Classifiers via Malicious Generative Models
Derui Wang
Wang
Chaoran Li
S. Wen
Surya Nepal
Yang Xiang
AAML
34
35
0
14 Oct 2019
Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box
  Attacks on Speech Recognition and Voice Identification Systems
Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systems
H. Abdullah
Muhammad Sajidur Rahman
Washington Garcia
Logan Blue
Kevin Warren
Anurag Swarnim Yadav
T. Shrimpton
Patrick Traynor
AAML
75
88
0
11 Oct 2019
Noise as a Resource for Learning in Knowledge Distillation
Noise as a Resource for Learning in Knowledge Distillation
Elahe Arani
F. Sarfraz
Bahram Zonooz
57
6
0
11 Oct 2019
Universal Adversarial Perturbation for Text Classification
Universal Adversarial Perturbation for Text Classification
Hang Gao
Tim Oates
AAML
108
15
0
10 Oct 2019
SmoothFool: An Efficient Framework for Computing Smooth Adversarial
  Perturbations
SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations
Ali Dabouei
Sobhan Soleymani
Fariborz Taherkhani
J. Dawson
Nasser M. Nasrabadi
AAML
144
19
0
08 Oct 2019
Yet another but more efficient black-box adversarial attack: tiling and
  evolution strategies
Yet another but more efficient black-box adversarial attack: tiling and evolution strategies
Laurent Meunier
Cen Chen
Li Wang
MLAUAAML
133
40
0
05 Oct 2019
Perturbations are not Enough: Generating Adversarial Examples with
  Spatial Distortions
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions
He Zhao
Trung Le
Paul Montague
O. Vel
Tamas Abraham
Dinh Q. Phung
AAML
62
8
0
03 Oct 2019
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
Micah Goldblum
Liam H. Fowl
Tom Goldstein
83
13
0
02 Oct 2019
An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack
An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack
Yang Zhang
Shiyu Chang
Mo Yu
Kaizhi Qian
AAML
29
2
0
01 Oct 2019
Role of Spatial Context in Adversarial Robustness for Object Detection
Role of Spatial Context in Adversarial Robustness for Object Detection
Aniruddha Saha
Akshayvarun Subramanya
Koninika Patil
Hamed Pirsiavash
ObjDAAML
112
54
0
30 Sep 2019
Impact of Low-bitwidth Quantization on the Adversarial Robustness for
  Embedded Neural Networks
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks
Rémi Bernhard
Pierre-Alain Moëllic
J. Dutertre
AAMLMQ
96
18
0
27 Sep 2019
Lower Bounds on Adversarial Robustness from Optimal Transport
Lower Bounds on Adversarial Robustness from Optimal Transport
A. Bhagoji
Daniel Cullina
Prateek Mittal
OODOTAAML
72
94
0
26 Sep 2019
MemGuard: Defending against Black-Box Membership Inference Attacks via
  Adversarial Examples
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
Jinyuan Jia
Ahmed Salem
Michael Backes
Yang Zhang
Neil Zhenqiang Gong
108
398
0
23 Sep 2019
HAWKEYE: Adversarial Example Detector for Deep Neural Networks
HAWKEYE: Adversarial Example Detector for Deep Neural Networks
Jinkyu Koo
Michael A. Roth
S. Bagchi
AAML
232
3
0
22 Sep 2019
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware
  Detection
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection
Aminollah Khormali
Ahmed A. Abusnaina
Songqing Chen
Daehun Nyang
Aziz Mohaisen
AAML
58
27
0
20 Sep 2019
Adversarial Learning with Margin-based Triplet Embedding Regularization
Adversarial Learning with Margin-based Triplet Embedding Regularization
Yaoyao Zhong
Weihong Deng
AAML
91
50
0
20 Sep 2019
Absum: Simple Regularization Method for Reducing Structural Sensitivity
  of Convolutional Neural Networks
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks
Sekitoshi Kanai
Yasutoshi Ida
Yasuhiro Fujiwara
Masanori Yamada
S. Adachi
AAML
44
1
0
19 Sep 2019
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Han Xu
Yao Ma
Haochen Liu
Debayan Deb
Hui Liu
Jiliang Tang
Anil K. Jain
AAML
79
680
0
17 Sep 2019
Generating Black-Box Adversarial Examples for Text Classifiers Using a
  Deep Reinforced Model
Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model
Prashanth Vijayaraghavan
D. Roy
AAML
49
36
0
17 Sep 2019
HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial
  Examples
HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples
Wanting Yu
Hongyi Yu
Lingyun Jiang
Mengli Zhang
Kai Qiao
GANAAML
27
0
0
17 Sep 2019
Detecting Adversarial Samples Using Influence Functions and Nearest
  Neighbors
Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors
Gilad Cohen
Guillermo Sapiro
Raja Giryes
TDI
73
128
0
15 Sep 2019
Wasserstein Diffusion Tikhonov Regularization
Wasserstein Diffusion Tikhonov Regularization
A. Lin
Yonatan Dukler
Wuchen Li
Guido Montúfar
38
2
0
15 Sep 2019
White-Box Adversarial Defense via Self-Supervised Data Estimation
White-Box Adversarial Defense via Self-Supervised Data Estimation
Zudi Lin
Hanspeter Pfister
Ziming Zhang
AAML
23
2
0
13 Sep 2019
Defending Against Adversarial Attacks by Suppressing the Largest
  Eigenvalue of Fisher Information Matrix
Defending Against Adversarial Attacks by Suppressing the Largest Eigenvalue of Fisher Information Matrix
Yaxin Peng
Chaomin Shen
Guixu Zhang
Jinsong Fan
AAML
41
13
0
13 Sep 2019
On educating machines
On educating machines
George Leu
Jiangjun Tang
AI4CE
27
0
0
13 Sep 2019
An Empirical Investigation of Randomized Defenses against Adversarial
  Attacks
An Empirical Investigation of Randomized Defenses against Adversarial Attacks
Yannik Potdevin
Dirk Nowotka
Vijay Ganesh
AAML
49
4
0
12 Sep 2019
Inspecting adversarial examples using the Fisher information
Inspecting adversarial examples using the Fisher information
Jörg Martin
Clemens Elster
AAML
50
15
0
12 Sep 2019
Feedback Learning for Improving the Robustness of Neural Networks
Feedback Learning for Improving the Robustness of Neural Networks
Chang Song
Zuoguan Wang
H. Li
AAML
65
7
0
12 Sep 2019
Sparse and Imperceivable Adversarial Attacks
Sparse and Imperceivable Adversarial Attacks
Francesco Croce
Matthias Hein
AAML
110
199
0
11 Sep 2019
Effectiveness of Adversarial Examples and Defenses for Malware
  Classification
Effectiveness of Adversarial Examples and Defenses for Malware Classification
Robert Podschwadt
Hassan Takabi
AAML
52
11
0
10 Sep 2019
FDA: Feature Disruptive Attack
FDA: Feature Disruptive Attack
Aditya Ganeshan
S. VivekB.
R. Venkatesh Babu
AAML
118
105
0
10 Sep 2019
Universal Physical Camouflage Attacks on Object Detectors
Universal Physical Camouflage Attacks on Object Detectors
Lifeng Huang
Chengying Gao
Yuyin Zhou
Cihang Xie
Alan Yuille
C. Zou
Ning Liu
AAML
182
168
0
10 Sep 2019
Learning to Disentangle Robust and Vulnerable Features for Adversarial
  Detection
Learning to Disentangle Robust and Vulnerable Features for Adversarial Detection
Byunggill Joe
Sung Ju Hwang
I. Shin
AAML
35
1
0
10 Sep 2019
BOSH: An Efficient Meta Algorithm for Decision-based Attacks
BOSH: An Efficient Meta Algorithm for Decision-based Attacks
Zhenxin Xiao
Puyudi Yang
Yuchen Eleanor Jiang
Kai-Wei Chang
Cho-Jui Hsieh
AAML
35
1
0
10 Sep 2019
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