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2107.05166
Cited By
Stateful Detection of Model Extraction Attacks
12 July 2021
Soham Pal
Yash Gupta
Aditya Kanade
S. Shevade
MLAU
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Papers citing
"Stateful Detection of Model Extraction Attacks"
11 / 11 papers shown
Title
On Evaluating Adversarial Robustness
Nicholas Carlini
Anish Athalye
Nicolas Papernot
Wieland Brendel
Jonas Rauber
Dimitris Tsipras
Ian Goodfellow
Aleksander Madry
Alexey Kurakin
ELM
AAML
109
905
0
18 Feb 2019
Deep Learning for Anomaly Detection: A Survey
Raghavendra Chalapathy
Sanjay Chawla
AI4TS
172
1,495
0
10 Jan 2019
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
Pouya Samangouei
Maya Kabkab
Rama Chellappa
AAML
GAN
86
1,179
0
17 May 2018
Adversarial Active Learning for Deep Networks: a Margin Based Approach
Mélanie Ducoffe
F. Precioso
GAN
AAML
143
276
0
27 Feb 2018
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao
Kashif Rasul
Roland Vollgraf
285
8,928
0
25 Aug 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Nicholas Carlini
D. Wagner
AAML
131
1,867
0
20 May 2017
Stealing Machine Learning Models via Prediction APIs
Florian Tramèr
Fan Zhang
Ari Juels
Michael K. Reiter
Thomas Ristenpart
SILM
MLAU
109
1,811
0
09 Sep 2016
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
547
5,912
0
08 Jul 2016
Practical Black-Box Attacks against Machine Learning
Nicolas Papernot
Patrick McDaniel
Ian Goodfellow
S. Jha
Z. Berkay Celik
A. Swami
MLAU
AAML
85
3,685
0
08 Feb 2016
DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
P. Frossard
AAML
154
4,905
0
14 Nov 2015
Auto-Encoding Variational Bayes
Diederik P. Kingma
Max Welling
BDL
458
16,922
0
20 Dec 2013
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