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AdvCheck: Characterizing Adversarial Examples via Local Gradient
  Checking

AdvCheck: Characterizing Adversarial Examples via Local Gradient Checking

25 March 2023
Ruoxi Chen
Haibo Jin
Jinyin Chen
Haibin Zheng
    AAML
ArXiv (abs)PDFHTML

Papers citing "AdvCheck: Characterizing Adversarial Examples via Local Gradient Checking"

28 / 28 papers shown
Title
Improving Adversarial Robustness via Channel-wise Activation Suppressing
Improving Adversarial Robustness via Channel-wise Activation Suppressing
Yang Bai
Yuyuan Zeng
Yong Jiang
Shutao Xia
Xingjun Ma
Yisen Wang
AAML
84
131
0
11 Mar 2021
GraN: An Efficient Gradient-Norm Based Detector for Adversarial and
  Misclassified Examples
GraN: An Efficient Gradient-Norm Based Detector for Adversarial and Misclassified Examples
Julia Lust
Alexandru Paul Condurache
AAML
30
26
0
20 Apr 2020
Reliable evaluation of adversarial robustness with an ensemble of
  diverse parameter-free attacks
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce
Matthias Hein
AAML
241
1,861
0
03 Mar 2020
Square Attack: a query-efficient black-box adversarial attack via random
  search
Square Attack: a query-efficient black-box adversarial attack via random search
Maksym Andriushchenko
Francesco Croce
Nicolas Flammarion
Matthias Hein
AAML
99
992
0
29 Nov 2019
DLA: Dense-Layer-Analysis for Adversarial Example Detection
DLA: Dense-Layer-Analysis for Adversarial Example Detection
Philip Sperl
Ching-yu Kao
Peng Chen
Konstantin Böttinger
AAML
41
34
0
05 Nov 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
59
128
0
15 Sep 2019
ML-LOO: Detecting Adversarial Examples with Feature Attribution
ML-LOO: Detecting Adversarial Examples with Feature Attribution
Puyudi Yang
Jianbo Chen
Cho-Jui Hsieh
Jane-ling Wang
Michael I. Jordan
AAML
77
101
0
08 Jun 2019
Adversarial Sample Detection for Deep Neural Network through Model
  Mutation Testing
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
Jingyi Wang
Guoliang Dong
Jun Sun
Xinyu Wang
Peixin Zhang
AAML
59
191
0
14 Dec 2018
SparseFool: a few pixels make a big difference
SparseFool: a few pixels make a big difference
Apostolos Modas
Seyed-Mohsen Moosavi-Dezfooli
P. Frossard
AAML
72
199
0
06 Nov 2018
Towards the first adversarially robust neural network model on MNIST
Towards the first adversarially robust neural network model on MNIST
Lukas Schott
Jonas Rauber
Matthias Bethge
Wieland Brendel
AAMLOOD
70
370
0
23 May 2018
On the Limitation of Local Intrinsic Dimensionality for Characterizing
  the Subspaces of Adversarial Examples
On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples
Pei-Hsuan Lu
Pin-Yu Chen
Chia-Mu Yu
AAML
52
26
0
26 Mar 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust
  Deep Learning
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
Nicolas Papernot
Patrick McDaniel
OODAAML
154
508
0
13 Mar 2018
Characterizing Adversarial Subspaces Using Local Intrinsic
  Dimensionality
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Xingjun Ma
Yue Liu
Yisen Wang
S. Erfani
S. Wijewickrema
Grant Schoenebeck
Basel Alomair
Michael E. Houle
James Bailey
AAML
116
742
0
08 Jan 2018
Facial Attributes: Accuracy and Adversarial Robustness
Facial Attributes: Accuracy and Adversarial Robustness
Andras Rozsa
Manuel Günther
Ethan M. Rudd
Terrance E. Boult
AAMLCVBM
76
65
0
04 Jan 2018
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
73
1,351
0
12 Dec 2017
One pixel attack for fooling deep neural networks
One pixel attack for fooling deep neural networks
Jiawei Su
Danilo Vasconcellos Vargas
Kouichi Sakurai
AAML
158
2,327
0
24 Oct 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
SILMOOD
319
12,151
0
19 Jun 2017
MagNet: a Two-Pronged Defense against Adversarial Examples
MagNet: a Two-Pronged Defense against Adversarial Examples
Dongyu Meng
Hao Chen
AAML
56
1,209
0
25 May 2017
Detecting Adversarial Image Examples in Deep Networks with Adaptive
  Noise Reduction
Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction
Bin Liang
Hongcheng Li
Miaoqiang Su
Xirong Li
Wenchang Shi
Xiaofeng Wang
AAML
104
218
0
23 May 2017
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
Kexin Pei
Yinzhi Cao
Junfeng Yang
Suman Jana
AAML
109
1,375
0
18 May 2017
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
  Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard
Menglong Zhu
Bo Chen
Dmitry Kalenichenko
Weijun Wang
Tobias Weyand
M. Andreetto
Hartwig Adam
3DH
1.2K
20,900
0
17 Apr 2017
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILMAAML
547
5,912
0
08 Jul 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.3K
194,641
0
10 Dec 2015
The Limitations of Deep Learning in Adversarial Settings
The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot
Patrick McDaniel
S. Jha
Matt Fredrikson
Z. Berkay Celik
A. Swami
AAML
120
3,968
0
24 Nov 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
154
4,910
0
14 Nov 2015
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAMLGAN
282
19,129
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
FAttMDE
1.7K
100,575
0
04 Sep 2014
ImageNet Large Scale Visual Recognition Challenge
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky
Jia Deng
Hao Su
J. Krause
S. Satheesh
...
A. Karpathy
A. Khosla
Michael S. Bernstein
Alexander C. Berg
Li Fei-Fei
VLMObjD
1.7K
39,637
0
01 Sep 2014
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