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Learning Universal Adversarial Perturbations with Generative Models

Learning Universal Adversarial Perturbations with Generative Models

17 August 2017
Jamie Hayes
G. Danezis
    AAML
ArXivPDFHTML

Papers citing "Learning Universal Adversarial Perturbations with Generative Models"

14 / 14 papers shown
Title
Mitigating Black-Box Adversarial Attacks via Output Noise Perturbation
Mitigating Black-Box Adversarial Attacks via Output Noise Perturbation
Manjushree B. Aithal
Xiaohua Li
AAML
60
6
0
30 Sep 2021
Targeted Physical-World Attention Attack on Deep Learning Models in Road
  Sign Recognition
Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognition
Xinghao Yang
Weifeng Liu
Shengli Zhang
Wei Liu
Dacheng Tao
AAML
27
28
0
09 Oct 2020
Query complexity of adversarial attacks
Query complexity of adversarial attacks
Grzegorz Gluch
R. Urbanke
AAML
27
5
0
02 Oct 2020
A simple way to make neural networks robust against diverse image
  corruptions
A simple way to make neural networks robust against diverse image corruptions
E. Rusak
Lukas Schott
Roland S. Zimmermann
Julian Bitterwolf
Oliver Bringmann
Matthias Bethge
Wieland Brendel
21
64
0
16 Jan 2020
POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via
  Genetic Algorithm
POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm
Jinyin Chen
Mengmeng Su
Shijing Shen
Hui Xiong
Haibin Zheng
AAML
22
67
0
01 May 2019
A geometry-inspired decision-based attack
A geometry-inspired decision-based attack
Yujia Liu
Seyed-Mohsen Moosavi-Dezfooli
P. Frossard
AAML
24
52
0
26 Mar 2019
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical
  Study
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study
Xurong Li
S. Ji
Men Han
Juntao Ji
Zhenyu Ren
Yushan Liu
Chunming Wu
AAML
26
31
0
04 Jan 2019
Query-Efficient Black-Box Attack by Active Learning
Query-Efficient Black-Box Attack by Active Learning
Pengcheng Li
Jinfeng Yi
Lijun Zhang
AAML
MLAU
21
54
0
13 Sep 2018
Black-box Adversarial Attacks with Limited Queries and Information
Black-box Adversarial Attacks with Limited Queries and Information
Andrew Ilyas
Logan Engstrom
Anish Athalye
Jessy Lin
MLAU
AAML
70
1,191
0
23 Apr 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
OOD
AAML
13
504
0
13 Mar 2018
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with
  Adversarial Examples
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples
Minhao Cheng
Jinfeng Yi
Pin-Yu Chen
Huan Zhang
Cho-Jui Hsieh
SILM
AAML
54
242
0
03 Mar 2018
Query-limited Black-box Attacks to Classifiers
Query-limited Black-box Attacks to Classifiers
Fnu Suya
Yuan Tian
David Evans
Paolo Papotti
AAML
20
24
0
23 Dec 2017
Towards Reverse-Engineering Black-Box Neural Networks
Towards Reverse-Engineering Black-Box Neural Networks
Seong Joon Oh
Maximilian Augustin
Bernt Schiele
Mario Fritz
AAML
292
3
0
06 Nov 2017
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
332
5,849
0
08 Jul 2016
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