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Robustness of classifiers to universal perturbations: a geometric
  perspective
v1v2 (latest)

Robustness of classifiers to universal perturbations: a geometric perspective

26 May 2017
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
Omar Fawzi
P. Frossard
Stefano Soatto
    AAML
ArXiv (abs)PDFHTML

Papers citing "Robustness of classifiers to universal perturbations: a geometric perspective"

50 / 63 papers shown
Origins of Low-dimensional Adversarial Perturbations
Origins of Low-dimensional Adversarial PerturbationsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Elvis Dohmatob
Chuan Guo
Morgane Goibert
AAML
274
4
0
25 Mar 2022
On the (Non-)Robustness of Two-Layer Neural Networks in Different
  Learning Regimes
On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes
Elvis Dohmatob
A. Bietti
AAML
425
15
0
22 Mar 2022
On Distinctive Properties of Universal Perturbations
On Distinctive Properties of Universal Perturbations
Sung Min Park
K. Wei
Kai Y. Xiao
Jungshian Li
Aleksander Madry
AAML
269
2
0
31 Dec 2021
On the Security & Privacy in Federated Learning
On the Security & Privacy in Federated Learning
Gorka Abad
S. Picek
Víctor Julio Ramírez-Durán
A. Urbieta
378
13
0
10 Dec 2021
Improving Local Effectiveness for Global robust training
Improving Local Effectiveness for Global robust training
Jingyue Lu
M. P. Kumar
AAML
157
0
0
26 Oct 2021
Advances in adversarial attacks and defenses in computer vision: A
  survey
Advances in adversarial attacks and defenses in computer vision: A survey
Naveed Akhtar
Lin Wang
Navid Kardan
M. Shah
AAML
545
315
0
01 Aug 2021
Attack to Fool and Explain Deep Networks
Attack to Fool and Explain Deep NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
Naveed Akhtar
M. Jalwana
Bennamoun
Lin Wang
AAML
280
36
0
20 Jun 2021
Group-Structured Adversarial Training
Group-Structured Adversarial Training
Farzan Farnia
Amirali Aghazadeh
James Zou
David Tse
AAML
365
0
0
18 Jun 2021
Universal Adversarial Training with Class-Wise Perturbations
Universal Adversarial Training with Class-Wise PerturbationsIEEE International Conference on Multimedia and Expo (ICME), 2021
Philipp Benz
Chaoning Zhang
Adil Karjauv
In So Kweon
AAML
258
29
0
07 Apr 2021
A Survey On Universal Adversarial Attack
A Survey On Universal Adversarial AttackInternational Joint Conference on Artificial Intelligence (IJCAI), 2021
Chaoning Zhang
Philipp Benz
Chenguo Lin
Adil Karjauv
Jing Wu
In So Kweon
AAML
381
107
0
02 Mar 2021
Universal Adversarial Perturbations Through the Lens of Deep
  Steganography: Towards A Fourier Perspective
Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier PerspectiveAAAI Conference on Artificial Intelligence (AAAI), 2021
Chaoning Zhang
Philipp Benz
Adil Karjauv
In So Kweon
AAML
250
50
0
12 Feb 2021
Analysis of Dominant Classes in Universal Adversarial Perturbations
Analysis of Dominant Classes in Universal Adversarial PerturbationsKnowledge-Based Systems (KBS), 2020
Jon Vadillo
Roberto Santana
Jose A. Lozano
AAML
261
9
0
28 Dec 2020
TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural
  Backdoors
TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural BackdoorsEuropean Symposium on Security and Privacy (EuroS&P), 2020
Ren Pang
Zheng Zhang
Xiangshan Gao
Zhaohan Xi
S. Ji
Peng Cheng
Xiapu Luo
Ting Wang
AAML
489
46
0
16 Dec 2020
Achieving Adversarial Robustness Requires An Active Teacher
Achieving Adversarial Robustness Requires An Active TeacherJournal of Computational Mathematics (JCM), 2020
Chao Ma
Lexing Ying
201
1
0
14 Dec 2020
A Targeted Universal Attack on Graph Convolutional Network
A Targeted Universal Attack on Graph Convolutional NetworkNeural Processing Letters (NPL), 2020
Jiazhu Dai
Weifeng Zhu
Xiangfeng Luo
AAMLGNN
204
25
0
29 Nov 2020
Adversarial Machine Learning in Image Classification: A Survey Towards
  the Defender's Perspective
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's PerspectiveACM Computing Surveys (ACM CSUR), 2020
G. R. Machado
Eugênio Silva
R. Goldschmidt
AAML
327
189
0
08 Sep 2020
Adversarial Examples on Object Recognition: A Comprehensive Survey
Adversarial Examples on Object Recognition: A Comprehensive SurveyACM Computing Surveys (ACM CSUR), 2020
A. Serban
E. Poll
Joost Visser
AAML
580
83
0
07 Aug 2020
Black-box Adversarial Sample Generation Based on Differential Evolution
Black-box Adversarial Sample Generation Based on Differential EvolutionJournal of Systems and Software (JSS), 2020
Junyu Lin
Lei Xu
Yingqi Liu
Xinming Zhang
AAML
156
36
0
30 Jul 2020
Understanding Adversarial Examples from the Mutual Influence of Images
  and Perturbations
Understanding Adversarial Examples from the Mutual Influence of Images and PerturbationsComputer Vision and Pattern Recognition (CVPR), 2020
Chaoning Zhang
Philipp Benz
Tooba Imtiaz
In-So Kweon
SSLAAML
424
139
0
13 Jul 2020
Classifier-independent Lower-Bounds for Adversarial Robustness
Classifier-independent Lower-Bounds for Adversarial Robustness
Elvis Dohmatob
526
1
0
17 Jun 2020
On Universalized Adversarial and Invariant Perturbations
On Universalized Adversarial and Invariant Perturbations
Sandesh Kamath
Amit Deshpande
K. Subrahmanyam
AAML
88
0
0
08 Jun 2020
Universalization of any adversarial attack using very few test examples
Universalization of any adversarial attack using very few test examples
Sandesh Kamath
Amit Deshpande
K. Subrahmanyam
Vineeth N. Balasubramanian
FedMLAAML
128
1
0
18 May 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve
  Adversarial Robustness
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial RobustnessComputer Vision and Pattern Recognition (CVPR), 2020
Ahmadreza Jeddi
M. Shafiee
Michelle Karg
C. Scharfenberger
A. Wong
OODAAML
444
74
0
02 Mar 2020
Challenges and Countermeasures for Adversarial Attacks on Deep
  Reinforcement Learning
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement LearningIEEE Transactions on Artificial Intelligence (IEEE TAI), 2020
Inaam Ilahi
Muhammad Usama
Junaid Qadir
M. Janjua
Ala I. Al-Fuqaha
D. Hoang
Dusit Niyato
AAML
378
189
0
27 Jan 2020
Practical Fast Gradient Sign Attack against Mammographic Image
  Classifier
Practical Fast Gradient Sign Attack against Mammographic Image Classifier
Ibrahim Yilmaz
AAML
250
11
0
27 Jan 2020
A Method for Computing Class-wise Universal Adversarial Perturbations
A Method for Computing Class-wise Universal Adversarial Perturbations
Tejus Gupta
Abhishek Sinha
Nupur Kumari
M. Singh
Balaji Krishnamurthy
AAML
123
11
0
01 Dec 2019
Indirect Local Attacks for Context-aware Semantic Segmentation Networks
Indirect Local Attacks for Context-aware Semantic Segmentation NetworksEuropean Conference on Computer Vision (ECCV), 2019
Krishna Kanth Nakka
Mathieu Salzmann
SSegAAML
249
36
0
29 Nov 2019
Universal adversarial examples in speech command classification
Universal adversarial examples in speech command classification
Jon Vadillo
Roberto Santana
AAML
285
30
0
22 Nov 2019
A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models
A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models
Ren Pang
Hua Shen
Xinyang Zhang
S. Ji
Yevgeniy Vorobeychik
Xiaopu Luo
Alex Liu
Ting Wang
AAML
283
2
0
05 Nov 2019
Universal Adversarial Perturbation for Text Classification
Universal Adversarial Perturbation for Text Classification
Hang Gao
Tim Oates
AAML
257
16
0
10 Oct 2019
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Adversarial Attacks and Defenses in Images, Graphs and Text: A ReviewInternational Journal of Automation and Computing (IJAC), 2019
Han Xu
Yao Ma
Haochen Liu
Debayan Deb
Hui Liu
Shucheng Zhou
Anil K. Jain
AAML
464
742
0
17 Sep 2019
Detecting Adversarial Samples Using Influence Functions and Nearest
  Neighbors
Detecting Adversarial Samples Using Influence Functions and Nearest NeighborsComputer Vision and Pattern Recognition (CVPR), 2019
Gilad Cohen
Guillermo Sapiro
Raja Giryes
TDI
294
142
0
15 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
228
13
0
13 Sep 2019
Global Adversarial Attacks for Assessing Deep Learning Robustness
Global Adversarial Attacks for Assessing Deep Learning Robustness
Hanbin Hu
Mitt Shah
Jianhua Z. Huang
Peng Li
AAML
217
4
0
19 Jun 2019
Defending Against Universal Attacks Through Selective Feature
  Regeneration
Defending Against Universal Attacks Through Selective Feature Regeneration
Tejas S. Borkar
Felix Heide
Lina Karam
AAML
288
1
0
08 Jun 2019
Label Universal Targeted Attack
Label Universal Targeted Attack
Naveed Akhtar
M. Jalwana
Bennamoun
Lin Wang
AAML
163
5
0
27 May 2019
Adaptive Gradient for Adversarial Perturbations Generation
Yatie Xiao
Chi-Man Pun
ODL
272
11
0
01 Feb 2019
A Black-box Attack on Neural Networks Based on Swarm Evolutionary
  Algorithm
A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm
Xiaolei Liu
Yuheng Luo
Xiaosong Zhang
Qingxin Zhu
AAML
140
17
0
26 Jan 2019
Dissociable neural representations of adversarially perturbed images in
  convolutional neural networks and the human brain
Dissociable neural representations of adversarially perturbed images in convolutional neural networks and the human brain
Chi Zhang
Xiaohan Duan
Linyuan Wang
Yongli Li
Bin Yan
Guoen Hu
Ruyuan Zhang
Li Tong
AAML
281
1
0
22 Dec 2018
Universal Adversarial Training
Universal Adversarial Training
A. Mendrik
Mahyar Najibi
Zheng Xu
John P. Dickerson
L. Davis
Tom Goldstein
AAMLOOD
422
209
0
27 Nov 2018
Deep Neural Network Concepts for Background Subtraction: A Systematic
  Review and Comparative Evaluation
Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation
T. Bouwmans
S. Javed
M. Sultana
Soon Ki Jung
291
336
0
13 Nov 2018
A Geometric Perspective on the Transferability of Adversarial Directions
A Geometric Perspective on the Transferability of Adversarial DirectionsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2018
Duncan C. McElfresh
H. Bidkhori
Dimitris Papailiopoulos
AAML
134
17
0
08 Nov 2018
The Adversarial Attack and Detection under the Fisher Information Metric
The Adversarial Attack and Detection under the Fisher Information Metric
Chenxiao Zhao
P. T. Fletcher
Mixue Yu
Chaomin Shen
Guixu Zhang
Yaxin Peng
AAML
269
51
0
09 Oct 2018
Efficient Two-Step Adversarial Defense for Deep Neural Networks
Efficient Two-Step Adversarial Defense for Deep Neural Networks
Ting-Jui Chang
Yukun He
Peng Li
AAML
223
13
0
08 Oct 2018
Adversarial Examples - A Complete Characterisation of the Phenomenon
Adversarial Examples - A Complete Characterisation of the Phenomenon
A. Serban
E. Poll
Joost Visser
SILMAAML
329
51
0
02 Oct 2018
Bridging machine learning and cryptography in defence against
  adversarial attacks
Bridging machine learning and cryptography in defence against adversarial attacks
O. Taran
Shideh Rezaeifar
Svyatoslav Voloshynovskiy
AAML
174
24
0
05 Sep 2018
Are You Tampering With My Data?
Are You Tampering With My Data?
Michele Alberti
Vinaychandran Pondenkandath
Marcel Würsch
Manuel Bouillon
Mathias Seuret
Rolf Ingold
Marcus Liwicki
AAML
238
20
0
21 Aug 2018
With Friends Like These, Who Needs Adversaries?
With Friends Like These, Who Needs Adversaries?
Saumya Jetley
Nicholas A. Lord
Juil Sock
AAML
379
72
0
11 Jul 2018
Built-in Vulnerabilities to Imperceptible Adversarial Perturbations
Built-in Vulnerabilities to Imperceptible Adversarial Perturbations
T. Tanay
Jerone T. A. Andrews
Lewis D. Griffin
214
7
0
19 Jun 2018
An Explainable Adversarial Robustness Metric for Deep Learning Neural
  Networks
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks
Chirag Agarwal
Bo Dong
Dan Schonfeld
A. Hoogs
228
2
0
05 Jun 2018
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