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On the Geometry of Adversarial Examples

On the Geometry of Adversarial Examples

1 November 2018
Marc Khoury
Dylan Hadfield-Menell
    AAML
ArXivPDFHTML

Papers citing "On the Geometry of Adversarial Examples"

19 / 19 papers shown
Title
On The Relationship Between Universal Adversarial Attacks And Sparse
  Representations
On The Relationship Between Universal Adversarial Attacks And Sparse Representations
Dana Weitzner
Raja Giryes
AAML
32
0
0
14 Nov 2023
Adversarial Examples Might be Avoidable: The Role of Data Concentration
  in Adversarial Robustness
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness
Ambar Pal
Huaijin Hao
Rene Vidal
28
8
0
28 Sep 2023
Why Does Little Robustness Help? Understanding and Improving Adversarial
  Transferability from Surrogate Training
Why Does Little Robustness Help? Understanding and Improving Adversarial Transferability from Surrogate Training
Yechao Zhang
Shengshan Hu
Leo Yu Zhang
Junyu Shi
Minghui Li
Xiaogeng Liu
Wei Wan
Hai Jin
AAML
27
21
0
15 Jul 2023
Boosting Adversarial Attacks by Leveraging Decision Boundary Information
Boosting Adversarial Attacks by Leveraging Decision Boundary Information
Boheng Zeng
LianLi Gao
Qilong Zhang
Chaoqun Li
JingKuan Song
Shuaiqi Jing
AAML
25
2
0
10 Mar 2023
Adversarial Rademacher Complexity of Deep Neural Networks
Adversarial Rademacher Complexity of Deep Neural Networks
Jiancong Xiao
Yanbo Fan
Ruoyu Sun
Zhimin Luo
AAML
17
22
0
27 Nov 2022
Evolution of Neural Tangent Kernels under Benign and Adversarial
  Training
Evolution of Neural Tangent Kernels under Benign and Adversarial Training
Noel Loo
Ramin Hasani
Alexander Amini
Daniela Rus
AAML
38
13
0
21 Oct 2022
Towards Out-of-Distribution Adversarial Robustness
Towards Out-of-Distribution Adversarial Robustness
Adam Ibrahim
Charles Guille-Escuret
Ioannis Mitliagkas
Irina Rish
David M. Krueger
P. Bashivan
OOD
31
6
0
06 Oct 2022
How many perturbations break this model? Evaluating robustness beyond
  adversarial accuracy
How many perturbations break this model? Evaluating robustness beyond adversarial accuracy
R. Olivier
Bhiksha Raj
AAML
34
5
0
08 Jul 2022
Adversarial Examples Make Strong Poisons
Adversarial Examples Make Strong Poisons
Liam H. Fowl
Micah Goldblum
Ping Yeh-Chiang
Jonas Geiping
Wojtek Czaja
Tom Goldstein
SILM
32
132
0
21 Jun 2021
Localized Uncertainty Attacks
Localized Uncertainty Attacks
Ousmane Amadou Dia
Theofanis Karaletsos
C. Hazirbas
Cristian Canton Ferrer
I. Kabul
E. Meijer
AAML
24
2
0
17 Jun 2021
Relating Adversarially Robust Generalization to Flat Minima
Relating Adversarially Robust Generalization to Flat Minima
David Stutz
Matthias Hein
Bernt Schiele
OOD
38
65
0
09 Apr 2021
Understanding Robustness in Teacher-Student Setting: A New Perspective
Understanding Robustness in Teacher-Student Setting: A New Perspective
Zhuolin Yang
Zhaoxi Chen
Tiffany Cai
Xinyun Chen
Bo-wen Li
Yuandong Tian
AAML
35
2
0
25 Feb 2021
Adversarial Classification: Necessary conditions and geometric flows
Adversarial Classification: Necessary conditions and geometric flows
Nicolas García Trillos
Ryan W. Murray
AAML
37
19
0
21 Nov 2020
On the Decision Boundaries of Neural Networks: A Tropical Geometry
  Perspective
On the Decision Boundaries of Neural Networks: A Tropical Geometry Perspective
Motasem Alfarra
Adel Bibi
Hasan Hammoud
M. Gaafar
Guohao Li
19
26
0
20 Feb 2020
Robustness of Bayesian Neural Networks to Gradient-Based Attacks
Robustness of Bayesian Neural Networks to Gradient-Based Attacks
Ginevra Carbone
Matthew Wicker
Luca Laurenti
A. Patané
Luca Bortolussi
G. Sanguinetti
AAML
38
77
0
11 Feb 2020
Adversarial Policies: Attacking Deep Reinforcement Learning
Adversarial Policies: Attacking Deep Reinforcement Learning
Adam Gleave
Michael Dennis
Cody Wild
Neel Kant
Sergey Levine
Stuart J. Russell
AAML
27
349
0
25 May 2019
Adversarial Training and Robustness for Multiple Perturbations
Adversarial Training and Robustness for Multiple Perturbations
Florian Tramèr
Dan Boneh
AAML
SILM
28
375
0
30 Apr 2019
Google's Neural Machine Translation System: Bridging the Gap between
  Human and Machine Translation
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Yonghui Wu
M. Schuster
Zhehuai Chen
Quoc V. Le
Mohammad Norouzi
...
Alex Rudnick
Oriol Vinyals
G. Corrado
Macduff Hughes
J. Dean
AIMat
718
6,748
0
26 Sep 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
317
5,847
0
08 Jul 2016
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