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Reducing audio membership inference attack accuracy to chance: 4
  defenses

Reducing audio membership inference attack accuracy to chance: 4 defenses

31 October 2019
M. Lomnitz
Nina Lopatina
Paul Gamble
Z. Hampel-Arias
Lucas Tindall
Felipe A. Mejia
M. Barrios
    AAML
ArXivPDFHTML

Papers citing "Reducing audio membership inference attack accuracy to chance: 4 defenses"

11 / 11 papers shown
Title
Machine Learning with Membership Privacy using Adversarial
  Regularization
Machine Learning with Membership Privacy using Adversarial Regularization
Milad Nasr
Reza Shokri
Amir Houmansadr
FedML
MIACV
45
470
0
16 Jul 2018
ML-Leaks: Model and Data Independent Membership Inference Attacks and
  Defenses on Machine Learning Models
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models
A. Salem
Yang Zhang
Mathias Humbert
Pascal Berrang
Mario Fritz
Michael Backes
MIACV
MIALM
91
948
0
04 Jun 2018
Voices Obscured in Complex Environmental Settings (VOICES) corpus
Voices Obscured in Complex Environmental Settings (VOICES) corpus
Colleen Richey
Maria Artigas
Zeb Armstrong
C. Bartels
H. Franco
...
Julien van Hout
Paul Gamble
Jeff Hetherly
Cory Stephenson
Karl S. Ni
56
127
0
13 Apr 2018
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
Nicholas Carlini
D. Wagner
AAML
94
1,079
0
05 Jan 2018
Houdini: Fooling Deep Structured Prediction Models
Houdini: Fooling Deep Structured Prediction Models
Moustapha Cissé
Yossi Adi
Natalia Neverova
Joseph Keshet
AAML
48
272
0
17 Jul 2017
Attacking Machine Learning models as part of a cyber kill chain
Attacking Machine Learning models as part of a cyber kill chain
Tam n. Nguyen
AAML
18
9
0
01 May 2017
Membership Inference Attacks against Machine Learning Models
Membership Inference Attacks against Machine Learning Models
Reza Shokri
M. Stronati
Congzheng Song
Vitaly Shmatikov
SLR
MIALM
MIACV
238
4,120
0
18 Oct 2016
Adversarial Perturbations Against Deep Neural Networks for Malware
  Classification
Adversarial Perturbations Against Deep Neural Networks for Malware Classification
Kathrin Grosse
Nicolas Papernot
Praveen Manoharan
Michael Backes
Patrick McDaniel
AAML
64
418
0
14 Jun 2016
Distillation as a Defense to Adversarial Perturbations against Deep
  Neural Networks
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
Nicolas Papernot
Patrick McDaniel
Xi Wu
S. Jha
A. Swami
AAML
92
3,072
0
14 Nov 2015
Distilling the Knowledge in a Neural Network
Distilling the Knowledge in a Neural Network
Geoffrey E. Hinton
Oriol Vinyals
J. Dean
FedML
342
19,634
0
09 Mar 2015
Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data
  from Machine Learning Classifiers
Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers
G. Ateniese
G. Felici
L. Mancini
A. Spognardi
Antonio Villani
Domenico Vitali
72
460
0
19 Jun 2013
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