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CD-UAP: Class Discriminative Universal Adversarial Perturbation

CD-UAP: Class Discriminative Universal Adversarial Perturbation

7 October 2020
Chaoning Zhang
Philipp Benz
Tooba Imtiaz
In So Kweon
    AAML
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Papers citing "CD-UAP: Class Discriminative Universal Adversarial Perturbation"

23 / 23 papers shown
Title
Improving Generalization of Universal Adversarial Perturbation via Dynamic Maximin Optimization
Improving Generalization of Universal Adversarial Perturbation via Dynamic Maximin Optimization
Yize Zhang
Yingzhe Xu
Junyu Shi
L. Zhang
Shengshan Hu
Minghui Li
Yanjun Zhang
AAML
122
1
0
17 Mar 2025
Universal Adversarial Perturbations for Speech Recognition Systems
Universal Adversarial Perturbations for Speech Recognition Systems
Paarth Neekhara
Shehzeen Samarah Hussain
Prakhar Pandey
Shlomo Dubnov
Julian McAuley
F. Koushanfar
AAML
58
117
0
09 May 2019
Ask, Acquire, and Attack: Data-free UAP Generation using Class
  Impressions
Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions
Konda Reddy Mopuri
P. Uppala
R. Venkatesh Babu
AAML
62
85
0
03 Aug 2018
Understanding and Enhancing the Transferability of Adversarial Examples
Understanding and Enhancing the Transferability of Adversarial Examples
Lei Wu
Zhanxing Zhu
Cheng Tai
E. Weinan
AAML
SILM
67
99
0
27 Feb 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
102
206
0
24 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
Ajmal Mian
AAML
91
1,867
0
02 Jan 2018
Adversarial Patch
Adversarial Patch
Tom B. Brown
Dandelion Mané
Aurko Roy
Martín Abadi
Justin Gilmer
AAML
70
1,095
0
27 Dec 2017
Adversarial Examples: Attacks and Defenses for Deep Learning
Adversarial Examples: Attacks and Defenses for Deep Learning
Xiaoyong Yuan
Pan He
Qile Zhu
Xiaolin Li
SILM
AAML
86
1,622
0
19 Dec 2017
Generative Adversarial Perturbations
Generative Adversarial Perturbations
Omid Poursaeed
Isay Katsman
Bicheng Gao
Serge J. Belongie
AAML
GAN
WIGM
67
355
0
06 Dec 2017
Art of singular vectors and universal adversarial perturbations
Art of singular vectors and universal adversarial perturbations
Valentin Khrulkov
Ivan Oseledets
AAML
51
132
0
11 Sep 2017
Fast Feature Fool: A data independent approach to universal adversarial
  perturbations
Fast Feature Fool: A data independent approach to universal adversarial perturbations
Konda Reddy Mopuri
Utsav Garg
R. Venkatesh Babu
AAML
80
206
0
18 Jul 2017
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILM
OOD
285
12,060
0
19 Jun 2017
Universal Adversarial Perturbations Against Semantic Image Segmentation
Universal Adversarial Perturbations Against Semantic Image Segmentation
J. H. Metzen
Mummadi Chaithanya Kumar
Thomas Brox
Volker Fischer
AAML
119
287
0
19 Apr 2017
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
461
3,140
0
04 Nov 2016
Universal adversarial perturbations
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
Omar Fawzi
P. Frossard
AAML
130
2,527
0
26 Oct 2016
A Boundary Tilting Persepective on the Phenomenon of Adversarial
  Examples
A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples
T. Tanay
Lewis D. Griffin
AAML
81
271
0
27 Aug 2016
Towards Evaluating the Robustness of Neural Networks
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini
D. Wagner
OOD
AAML
245
8,548
0
16 Aug 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.1K
193,814
0
10 Dec 2015
DeepFool: a simple and accurate method to fool deep neural networks
DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
P. Frossard
AAML
142
4,895
0
14 Nov 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.7K
150,006
0
22 Dec 2014
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAML
GAN
252
19,045
0
20 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAtt
MDE
1.6K
100,330
0
04 Sep 2014
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
255
14,912
1
21 Dec 2013
1