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Copycat CNN: Are Random Non-Labeled Data Enough to Steal Knowledge from
  Black-box Models?

Copycat CNN: Are Random Non-Labeled Data Enough to Steal Knowledge from Black-box Models?

21 January 2021
Jacson Rodrigues Correia-Silva
Rodrigo Berriel
C. Badue
Alberto F. de Souza
Thiago Oliveira-Santos
    MLAU
ArXivPDFHTML

Papers citing "Copycat CNN: Are Random Non-Labeled Data Enough to Steal Knowledge from Black-box Models?"

3 / 3 papers shown
Title
Beyond Slow Signs in High-fidelity Model Extraction
Beyond Slow Signs in High-fidelity Model Extraction
Hanna Foerster
Robert D. Mullins
Ilia Shumailov
Jamie Hayes
AAML
42
1
0
14 Jun 2024
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems
Guangjing Wang
Ce Zhou
Yuanda Wang
Bocheng Chen
Hanqing Guo
Qiben Yan
AAML
SILM
68
3
0
20 Nov 2023
EZClone: Improving DNN Model Extraction Attack via Shape Distillation
  from GPU Execution Profiles
EZClone: Improving DNN Model Extraction Attack via Shape Distillation from GPU Execution Profiles
Jonah O'Brien Weiss
Tiago A. O. Alves
S. Kundu
MIACV
AAML
FedML
30
8
0
06 Apr 2023
1