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Backdoor Smoothing: Demystifying Backdoor Attacks on Deep Neural
  Networks
v1v2v3v4 (latest)

Backdoor Smoothing: Demystifying Backdoor Attacks on Deep Neural Networks

Computers & security (CS), 2020
11 June 2020
Kathrin Grosse
Taesung Lee
Battista Biggio
Youngja Park
Michael Backes
Ian Molloy
    AAML
ArXiv (abs)PDFHTML

Papers citing "Backdoor Smoothing: Demystifying Backdoor Attacks on Deep Neural Networks"

3 / 3 papers shown
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping
  Attacks
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks
Orson Mengara
AAML
333
8
0
29 Mar 2024
Algorithmic Collective Action in Machine Learning
Algorithmic Collective Action in Machine LearningInternational Conference on Machine Learning (ICML), 2023
Moritz Hardt
Eric Mazumdar
Celestine Mendler-Dünner
Tijana Zrnic
189
30
0
08 Feb 2023
Wild Patterns Reloaded: A Survey of Machine Learning Security against
  Training Data Poisoning
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data PoisoningACM Computing Surveys (ACM CSUR), 2022
Antonio Emanuele Cinà
Kathrin Grosse
Ambra Demontis
Sebastiano Vascon
Werner Zellinger
Bernhard A. Moser
Alina Oprea
Battista Biggio
Marcello Pelillo
Fabio Roli
AAML
401
170
0
04 May 2022
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