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Adversarial Example Detection in Deployed Tree Ensembles

Adversarial Example Detection in Deployed Tree Ensembles

27 June 2022
Laurens Devos
Wannes Meert
Jesse Davis
    AAML
ArXiv (abs)PDFHTML

Papers citing "Adversarial Example Detection in Deployed Tree Ensembles"

29 / 29 papers shown
Title
Machine Learning with a Reject Option: A survey
Machine Learning with a Reject Option: A survey
Kilian Hendrickx
Lorenzo Perini
Dries Van der Plas
Wannes Meert
Jesse Davis
MU
82
129
0
23 Jul 2021
Genetic Adversarial Training of Decision Trees
Genetic Adversarial Training of Decision Trees
Francesco Ranzato
Marco Zanella
39
15
0
21 Dec 2020
Efficient Training of Robust Decision Trees Against Adversarial Examples
Efficient Training of Robust Decision Trees Against Adversarial Examples
D. Vos
S. Verwer
AAML
61
36
0
18 Dec 2020
Versatile Verification of Tree Ensembles
Versatile Verification of Tree Ensembles
Laurens Devos
Wannes Meert
Jesse Davis
AAML
42
12
0
26 Oct 2020
An Efficient Adversarial Attack for Tree Ensembles
An Efficient Adversarial Attack for Tree Ensembles
Chong Zhang
Huan Zhang
Cho-Jui Hsieh
AAML
33
23
0
22 Oct 2020
On $\ell_p$-norm Robustness of Ensemble Stumps and Trees
On ℓp\ell_pℓp​-norm Robustness of Ensemble Stumps and Trees
Yihan Wang
Huan Zhang
Hongge Chen
Duane S. Boning
Cho-Jui Hsieh
AAML
35
7
0
20 Aug 2020
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
Verifying Robustness of Gradient Boosted Models
Verifying Robustness of Gradient Boosted Models
Gil Einziger
M. Goldstein
Yaniv Saár
Itai Segall
70
41
0
26 Jun 2019
Robustness Verification of Tree-based Models
Robustness Verification of Tree-based Models
Hongge Chen
Huan Zhang
Si Si
Yang Li
Duane S. Boning
Cho-Jui Hsieh
AAML
80
77
0
10 Jun 2019
Provably Robust Boosted Decision Stumps and Trees against Adversarial
  Attacks
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks
Maksym Andriushchenko
Matthias Hein
78
62
0
08 Jun 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
68
101
0
08 Jun 2019
Formal Verification of Input-Output Mappings of Tree Ensembles
Formal Verification of Input-Output Mappings of Tree Ensembles
John Törnblom
Simin Nadjm-Tehrani
63
36
0
10 May 2019
Robust Decision Trees Against Adversarial Examples
Robust Decision Trees Against Adversarial Examples
Hongge Chen
Huan Zhang
Duane S. Boning
Cho-Jui Hsieh
AAML
125
117
0
27 Feb 2019
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
Kevin Roth
Yannic Kilcher
Thomas Hofmann
AAML
66
175
0
13 Feb 2019
The Limitations of Model Uncertainty in Adversarial Settings
The Limitations of Model Uncertainty in Adversarial Settings
Kathrin Grosse
David Pfaff
M. Smith
Michael Backes
AAML
56
34
0
06 Dec 2018
Detecting Adversarial Perturbations Through Spatial Behavior in
  Activation Spaces
Detecting Adversarial Perturbations Through Spatial Behavior in Activation Spaces
Ziv Katzir
Yuval Elovici
AAML
52
26
0
22 Nov 2018
Adversarial Risk and Robustness: General Definitions and Implications
  for the Uniform Distribution
Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution
Dimitrios I. Diochnos
Saeed Mahloujifar
Mohammad Mahmoody
AAML
43
72
0
29 Oct 2018
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural
  Network
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
Xuanqing Liu
Yao Li
Chongruo Wu
Cho-Jui Hsieh
AAMLOOD
74
171
0
01 Oct 2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and
  Adversarial Attacks
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee
Kibok Lee
Honglak Lee
Jinwoo Shin
OODD
197
2,063
0
10 Jul 2018
Adversarial vulnerability for any classifier
Adversarial vulnerability for any classifier
Alhussein Fawzi
Hamza Fawzi
Omar Fawzi
AAML
102
251
0
23 Feb 2018
Evasion Attacks against Machine Learning at Test Time
Evasion Attacks against Machine Learning at Test Time
Battista Biggio
Igino Corona
Davide Maiorca
B. Nelson
Nedim Srndic
Pavel Laskov
Giorgio Giacinto
Fabio Roli
AAML
163
2,160
0
21 Aug 2017
Adversarial and Clean Data Are Not Twins
Adversarial and Clean Data Are Not Twins
Zhitao Gong
Wenlu Wang
Wei-Shinn Ku
AAML
59
157
0
17 Apr 2017
Detecting Adversarial Samples from Artifacts
Detecting Adversarial Samples from Artifacts
Reuben Feinman
Ryan R. Curtin
S. Shintre
Andrew B. Gardner
AAML
105
894
0
01 Mar 2017
On the (Statistical) Detection of Adversarial Examples
On the (Statistical) Detection of Adversarial Examples
Kathrin Grosse
Praveen Manoharan
Nicolas Papernot
Michael Backes
Patrick McDaniel
AAML
86
714
0
21 Feb 2017
On Detecting Adversarial Perturbations
On Detecting Adversarial Perturbations
J. H. Metzen
Tim Genewein
Volker Fischer
Bastian Bischoff
AAML
80
950
0
14 Feb 2017
XGBoost: A Scalable Tree Boosting System
XGBoost: A Scalable Tree Boosting System
Tianqi Chen
Carlos Guestrin
817
39,255
0
09 Mar 2016
Evasion and Hardening of Tree Ensemble Classifiers
Evasion and Hardening of Tree Ensemble Classifiers
Alex Kantchelian
J. D. Tygar
A. Joseph
AAML
135
205
0
25 Sep 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
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
289
14,968
1
21 Dec 2013
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