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The Adversarial Attack and Detection under the Fisher Information Metric
v1v2 (latest)

The Adversarial Attack and Detection under the Fisher Information Metric

9 October 2018
Chenxiao Zhao
P. T. Fletcher
Mixue Yu
Chaomin Shen
Guixu Zhang
Yaxin Peng
    AAML
ArXiv (abs)PDFHTML

Papers citing "The Adversarial Attack and Detection under the Fisher Information Metric"

13 / 13 papers shown
Title
Robustness of classifiers to universal perturbations: a geometric
  perspective
Robustness of classifiers to universal perturbations: a geometric perspective
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
Omar Fawzi
P. Frossard
Stefano Soatto
AAML
60
118
0
26 May 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection
  Methods
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Nicholas Carlini
D. Wagner
AAML
126
1,864
0
20 May 2017
The Space of Transferable Adversarial Examples
The Space of Transferable Adversarial Examples
Florian Tramèr
Nicolas Papernot
Ian Goodfellow
Dan Boneh
Patrick McDaniel
AAMLSILM
90
559
0
11 Apr 2017
Detecting Adversarial Samples from Artifacts
Detecting Adversarial Samples from Artifacts
Reuben Feinman
Ryan R. Curtin
S. Shintre
Andrew B. Gardner
AAML
93
894
0
01 Mar 2017
On Detecting Adversarial Perturbations
On Detecting Adversarial Perturbations
J. H. Metzen
Tim Genewein
Volker Fischer
Bastian Bischoff
AAML
61
950
0
14 Feb 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
318
1,873
0
03 Feb 2017
Dense Associative Memory is Robust to Adversarial Inputs
Dense Associative Memory is Robust to Adversarial Inputs
Dmitry Krotov
J. Hopfield
AAML
67
112
0
04 Jan 2017
Delving into Transferable Adversarial Examples and Black-box Attacks
Delving into Transferable Adversarial Examples and Black-box Attacks
Yanpei Liu
Xinyun Chen
Chang-rui Liu
Basel Alomair
AAML
140
1,741
0
08 Nov 2016
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
472
3,147
0
04 Nov 2016
Universal adversarial perturbations
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
Omar Fawzi
P. Frossard
AAML
145
2,533
0
26 Oct 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILMAAML
545
5,909
0
08 Jul 2016
Fast and Accurate Deep Network Learning by Exponential Linear Units
  (ELUs)
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Djork-Arné Clevert
Thomas Unterthiner
Sepp Hochreiter
300
5,532
0
23 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
151
4,903
0
14 Nov 2015
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