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Model extraction from counterfactual explanations

Model extraction from counterfactual explanations

3 September 2020
Ulrich Aivodji
Alexandre Bolot
Sébastien Gambs
    MIACV
    MLAU
ArXivPDFHTML

Papers citing "Model extraction from counterfactual explanations"

10 / 10 papers shown
Title
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review
Sonal Allana
Mohan Kankanhalli
Rozita Dara
27
0
0
05 May 2025
Feature Inference Attack on Shapley Values
Feature Inference Attack on Shapley Values
Xinjian Luo
Yangfan Jiang
X. Xiao
AAML
FAtt
24
19
0
16 Jul 2024
Tensions Between the Proxies of Human Values in AI
Tensions Between the Proxies of Human Values in AI
Teresa Datta
D. Nissani
Max Cembalest
Akash Khanna
Haley Massa
John P. Dickerson
16
2
0
14 Dec 2022
Differentially Private Counterfactuals via Functional Mechanism
Differentially Private Counterfactuals via Functional Mechanism
Fan Yang
Qizhang Feng
Kaixiong Zhou
Jiahao Chen
Xia Hu
13
8
0
04 Aug 2022
I Know What You Trained Last Summer: A Survey on Stealing Machine
  Learning Models and Defences
I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences
Daryna Oliynyk
Rudolf Mayer
Andreas Rauber
16
106
0
16 Jun 2022
MEGEX: Data-Free Model Extraction Attack against Gradient-Based
  Explainable AI
MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI
T. Miura
Satoshi Hasegawa
Toshiki Shibahara
SILM
MIACV
11
37
0
19 Jul 2021
Characterizing the risk of fairwashing
Characterizing the risk of fairwashing
Ulrich Aivodji
Hiromi Arai
Sébastien Gambs
Satoshi Hara
18
26
0
14 Jun 2021
Exploiting Explanations for Model Inversion Attacks
Exploiting Explanations for Model Inversion Attacks
Xu Zhao
Wencan Zhang
Xiao Xiao
Brian Y. Lim
MIACV
13
82
0
26 Apr 2021
Methods for Interpreting and Understanding Deep Neural Networks
Methods for Interpreting and Understanding Deep Neural Networks
G. Montavon
Wojciech Samek
K. Müller
FaML
234
2,233
0
24 Jun 2017
Learning Certifiably Optimal Rule Lists for Categorical Data
Learning Certifiably Optimal Rule Lists for Categorical Data
E. Angelino
Nicholas Larus-Stone
Daniel Alabi
Margo Seltzer
Cynthia Rudin
46
196
0
06 Apr 2017
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