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Fingerprinting Deep Neural Networks Globally via Universal Adversarial
  Perturbations
v1v2v3 (latest)

Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations

17 February 2022
Zirui Peng
Shaofeng Li
Guoxing Chen
Cheng Zhang
Haojin Zhu
Minhui Xue
    AAMLFedML
ArXiv (abs)PDFHTML

Papers citing "Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations"

38 / 38 papers shown
Title
Sample Correlation for Fingerprinting Deep Face Recognition
Sample Correlation for Fingerprinting Deep Face Recognition
Jiyang Guan
Jian Liang
Yanbo Wang
Ran He
AAML
147
0
0
31 Dec 2024
MOVE: Effective and Harmless Ownership Verification via Embedded External Features
MOVE: Effective and Harmless Ownership Verification via Embedded External Features
Yiming Li
Linghui Zhu
Xiaojun Jia
Yang Bai
Yong Jiang
Shutao Xia
Xiaochun Cao
Kui Ren
AAML
91
14
0
04 Aug 2022
SoK: Machine Learning Governance
SoK: Machine Learning Governance
Varun Chandrasekaran
Hengrui Jia
Anvith Thudi
Adelin Travers
Mohammad Yaghini
Nicolas Papernot
101
16
0
20 Sep 2021
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial
  Attacks
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks
Nezihe Merve Gürel
Xiangyu Qi
Luka Rimanic
Ce Zhang
Yue Liu
AAML
50
39
0
11 Jun 2021
Progressive-Scale Boundary Blackbox Attack via Projective Gradient
  Estimation
Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation
Jiawei Zhang
Linyi Li
Huichen Li
Xiaolu Zhang
Shuang Yang
Yangqiu Song
AAML
46
17
0
10 Jun 2021
Proof-of-Learning: Definitions and Practice
Proof-of-Learning: Definitions and Practice
Hengrui Jia
Mohammad Yaghini
Christopher A. Choquette-Choo
Natalie Dullerud
Anvith Thudi
Varun Chandrasekaran
Nicolas Papernot
AAML
76
106
0
09 Mar 2021
Protecting Intellectual Property of Generative Adversarial Networks from
  Ambiguity Attack
Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack
Ding Sheng Ong
Chee Seng Chan
KamWoh Ng
Lixin Fan
Qiang Yang
AAML
41
72
0
08 Feb 2021
Hermes Attack: Steal DNN Models with Lossless Inference Accuracy
Hermes Attack: Steal DNN Models with Lossless Inference Accuracy
Yuankun Zhu
Yueqiang Cheng
Husheng Zhou
Yantao Lu
MIACVAAML
89
103
0
23 Jun 2020
Supervised Contrastive Learning
Supervised Contrastive Learning
Prannay Khosla
Piotr Teterwak
Chen Wang
Aaron Sarna
Yonglong Tian
Phillip Isola
Aaron Maschinot
Ce Liu
Dilip Krishnan
SSL
180
4,580
0
23 Apr 2020
Cryptanalytic Extraction of Neural Network Models
Cryptanalytic Extraction of Neural Network Models
Nicholas Carlini
Matthew Jagielski
Ilya Mironov
FedMLMLAUMIACVAAML
138
137
0
10 Mar 2020
Entangled Watermarks as a Defense against Model Extraction
Entangled Watermarks as a Defense against Model Extraction
Hengrui Jia
Christopher A. Choquette-Choo
Varun Chandrasekaran
Nicolas Papernot
WaLMAAML
80
220
0
27 Feb 2020
A Simple Framework for Contrastive Learning of Visual Representations
A Simple Framework for Contrastive Learning of Visual Representations
Ting-Li Chen
Simon Kornblith
Mohammad Norouzi
Geoffrey E. Hinton
SSL
390
18,897
0
13 Feb 2020
AI-GAN: Attack-Inspired Generation of Adversarial Examples
AI-GAN: Attack-Inspired Generation of Adversarial Examples
Tao Bai
Jun Zhao
Jinlin Zhu
Shoudong Han
Jiefeng Chen
Yue Liu
Alex C. Kot
GAN
76
50
0
06 Feb 2020
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples
Nils Lukas
Yuxuan Zhang
Florian Kerschbaum
MLAUFedMLAAML
90
145
0
02 Dec 2019
IPGuard: Protecting Intellectual Property of Deep Neural Networks via
  Fingerprinting the Classification Boundary
IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary
Xiaoyu Cao
Jinyuan Jia
Neil Zhenqiang Gong
76
106
0
28 Oct 2019
Reverse-Engineering Deep ReLU Networks
Reverse-Engineering Deep ReLU Networks
David Rolnick
Konrad Paul Kording
74
105
0
02 Oct 2019
High Accuracy and High Fidelity Extraction of Neural Networks
High Accuracy and High Fidelity Extraction of Neural Networks
Matthew Jagielski
Nicholas Carlini
David Berthelot
Alexey Kurakin
Nicolas Papernot
MLAUMIACV
81
381
0
03 Sep 2019
DAWN: Dynamic Adversarial Watermarking of Neural Networks
DAWN: Dynamic Adversarial Watermarking of Neural Networks
S. Szyller
B. Atli
Samuel Marchal
Nadarajah Asokan
MLAUAAML
70
180
0
03 Jun 2019
Exploring Connections Between Active Learning and Model Extraction
Exploring Connections Between Active Learning and Model Extraction
Varun Chandrasekaran
Kamalika Chaudhuri
Irene Giacomelli
Shane Walker
Songbai Yan
MIACV
205
159
0
05 Nov 2018
Have You Stolen My Model? Evasion Attacks Against Deep Neural Network
  Watermarking Techniques
Have You Stolen My Model? Evasion Attacks Against Deep Neural Network Watermarking Techniques
Dorjan Hitaj
L. Mancini
AAML
75
53
0
03 Sep 2018
PRADA: Protecting against DNN Model Stealing Attacks
PRADA: Protecting against DNN Model Stealing Attacks
Mika Juuti
S. Szyller
Samuel Marchal
Nadarajah Asokan
SILMAAML
74
443
0
07 May 2018
Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks
  by Backdooring
Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring
Yossi Adi
Carsten Baum
Moustapha Cissé
Benny Pinkas
Joseph Keshet
68
682
0
13 Feb 2018
Adversarial Frontier Stitching for Remote Neural Network Watermarking
Adversarial Frontier Stitching for Remote Neural Network Watermarking
Erwan Le Merrer
P. Pérez
Gilles Trédan
MLAUAAML
78
339
0
06 Nov 2017
To prune, or not to prune: exploring the efficacy of pruning for model
  compression
To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu
Suyog Gupta
197
1,282
0
05 Oct 2017
Art of singular vectors and universal adversarial perturbations
Art of singular vectors and universal adversarial perturbations
Valentin Khrulkov
Ivan Oseledets
AAML
64
132
0
11 Sep 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
  Algorithms
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao
Kashif Rasul
Roland Vollgraf
285
8,928
0
25 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILMOOD
319
12,138
0
19 Jun 2017
The Space of Transferable Adversarial Examples
The Space of Transferable Adversarial Examples
Florian Tramèr
Nicolas Papernot
Ian Goodfellow
Dan Boneh
Patrick McDaniel
AAMLSILM
102
558
0
11 Apr 2017
Delving into Transferable Adversarial Examples and Black-box Attacks
Delving into Transferable Adversarial Examples and Black-box Attacks
Yanpei Liu
Xinyun Chen
Chang-rui Liu
Basel Alomair
AAML
143
1,741
0
08 Nov 2016
Universal adversarial perturbations
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
Omar Fawzi
P. Frossard
AAML
152
2,533
0
26 Oct 2016
Stealing Machine Learning Models via Prediction APIs
Stealing Machine Learning Models via Prediction APIs
Florian Tramèr
Fan Zhang
Ari Juels
Michael K. Reiter
Thomas Ristenpart
SILMMLAU
109
1,811
0
09 Sep 2016
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
Laurens van der Maaten
Kilian Q. Weinberger
PINN3DV
813
36,892
0
25 Aug 2016
Practical Black-Box Attacks against Machine Learning
Practical Black-Box Attacks against Machine Learning
Nicolas Papernot
Patrick McDaniel
Ian Goodfellow
S. Jha
Z. Berkay Celik
A. Swami
MLAUAAML
76
3,685
0
08 Feb 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,510
0
10 Dec 2015
DeepFool: a simple and accurate method to fool deep neural networks
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
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained
  Quantization and Huffman Coding
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Song Han
Huizi Mao
W. Dally
3DGS
263
8,862
0
01 Oct 2015
Going Deeper with Convolutions
Going Deeper with Convolutions
Christian Szegedy
Wei Liu
Yangqing Jia
P. Sermanet
Scott E. Reed
Dragomir Anguelov
D. Erhan
Vincent Vanhoucke
Andrew Rabinovich
494
43,698
0
17 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAttMDE
1.7K
100,529
0
04 Sep 2014
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