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Stealing Black-Box Functionality Using The Deep Neural Tree Architecture

Stealing Black-Box Functionality Using The Deep Neural Tree Architecture

23 February 2020
Daniel Teitelman
I. Naeh
Shie Mannor
ArXiv (abs)PDFHTML

Papers citing "Stealing Black-Box Functionality Using The Deep Neural Tree Architecture"

4 / 4 papers shown
Models That Are Interpretable But Not Transparent
Models That Are Interpretable But Not TransparentInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Chudi Zhong
Panyu Chen
Cynthia Rudin
AAML
352
1
0
26 Feb 2025
Decompiling x86 Deep Neural Network Executables
Decompiling x86 Deep Neural Network ExecutablesUSENIX Security Symposium (USENIX Security), 2022
Zhibo Liu
Yuanyuan Yuan
Shuai Wang
Xiaofei Xie
Lei Ma
AAML
376
29
0
03 Oct 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 DefencesACM Computing Surveys (ACM CSUR), 2022
Daryna Oliynyk
Rudolf Mayer
Andreas Rauber
389
167
0
16 Jun 2022
A Primer for Neural Arithmetic Logic Modules
A Primer for Neural Arithmetic Logic ModulesJournal of machine learning research (JMLR), 2021
Bhumika Mistry
K. Farrahi
Jonathon S. Hare
355
10
0
23 Jan 2021
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