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  4. Cited By
Copycat CNN: Stealing Knowledge by Persuading Confession with Random
  Non-Labeled Data

Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data

14 June 2018
Jacson Rodrigues Correia-Silva
Rodrigo Berriel
C. Badue
Alberto F. de Souza
Thiago Oliveira-Santos
    MLAU
ArXivPDFHTML

Papers citing "Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data"

43 / 43 papers shown
Title
Attackers Can Do Better: Over- and Understated Factors of Model Stealing Attacks
Daryna Oliynyk
Rudolf Mayer
Andreas Rauber
AAML
51
0
0
08 Mar 2025
Examining the Threat Landscape: Foundation Models and Model Stealing
Examining the Threat Landscape: Foundation Models and Model Stealing
Ankita Raj
Deepankar Varma
Chetan Arora
AAML
83
1
0
25 Feb 2025
Neural Honeytrace: A Robust Plug-and-Play Watermarking Framework against Model Extraction Attacks
Neural Honeytrace: A Robust Plug-and-Play Watermarking Framework against Model Extraction Attacks
Yixiao Xu
Binxing Fang
Rui Wang
Yinghai Zhou
S. Ji
Yuan Liu
Mohan Li
Zhihong Tian
MIACV
AAML
73
0
0
20 Jan 2025
Precise Extraction of Deep Learning Models via Side-Channel Attacks on
  Edge/Endpoint Devices
Precise Extraction of Deep Learning Models via Side-Channel Attacks on Edge/Endpoint Devices
Younghan Lee
Sohee Jun
Yungi Cho
Woorim Han
Hyungon Moon
Y. Paek
AAML
31
2
0
05 Mar 2024
SoK: Unintended Interactions among Machine Learning Defenses and Risks
SoK: Unintended Interactions among Machine Learning Defenses and Risks
Vasisht Duddu
S. Szyller
Nadarajah Asokan
AAML
52
2
0
07 Dec 2023
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based
  sample selection
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selection
Akshit Jindal
Vikram Goyal
Saket Anand
Chetan Arora
FedML
22
2
0
08 Nov 2023
Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation
Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation
Vlad Hondru
Radu Tudor Ionescu
DiffM
52
1
0
29 Sep 2023
Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing
  Inference Serving Systems
Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing Inference Serving Systems
Debopam Sanyal
Jui-Tse Hung
Manavi Agrawal
Prahlad Jasti
Shahab Nikkhoo
S. Jha
Tianhao Wang
Sibin Mohan
Alexey Tumanov
51
0
0
03 Jul 2023
Robust and IP-Protecting Vertical Federated Learning against Unexpected
  Quitting of Parties
Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties
Jingwei Sun
Zhixu Du
Anna Dai
Saleh Baghersalimi
Alireza Amirshahi
David Atienza
Yiran Chen
FedML
21
8
0
28 Mar 2023
Model Extraction Attacks on Split Federated Learning
Model Extraction Attacks on Split Federated Learning
Jingtao Li
Adnan Siraj Rakin
Xing Chen
Li Yang
Zhezhi He
Deliang Fan
C. Chakrabarti
FedML
65
5
0
13 Mar 2023
Threats, Vulnerabilities, and Controls of Machine Learning Based
  Systems: A Survey and Taxonomy
Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy
Yusuke Kawamoto
Kazumasa Miyake
K. Konishi
Y. Oiwa
29
4
0
18 Jan 2023
IronForge: An Open, Secure, Fair, Decentralized Federated Learning
IronForge: An Open, Secure, Fair, Decentralized Federated Learning
Guangsheng Yu
Xu Wang
Caijun Sun
Qin Wang
Ping Yu
Wei Ni
R. Liu
Xiwei Xu
OOD
AI4CE
29
25
0
07 Jan 2023
Model Extraction Attack against Self-supervised Speech Models
Model Extraction Attack against Self-supervised Speech Models
Tsung-Yuan Hsu
Chen-An Li
Tung-Yu Wu
Hung-yi Lee
32
1
0
29 Nov 2022
Careful What You Wish For: on the Extraction of Adversarially Trained
  Models
Careful What You Wish For: on the Extraction of Adversarially Trained Models
Kacem Khaled
Gabriela Nicolescu
F. Magalhães
MIACV
AAML
35
4
0
21 Jul 2022
Threat Assessment in Machine Learning based Systems
Threat Assessment in Machine Learning based Systems
L. Tidjon
Foutse Khomh
27
17
0
30 Jun 2022
I Know What You Trained Last Summer: A Survey on Stealing Machine
  Learning Models and Defences
I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences
Daryna Oliynyk
Rudolf Mayer
Andreas Rauber
57
106
0
16 Jun 2022
Stealing and Evading Malware Classifiers and Antivirus at Low False
  Positive Conditions
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditions
M. Rigaki
Sebastian Garcia
AAML
25
10
0
13 Apr 2022
StolenEncoder: Stealing Pre-trained Encoders in Self-supervised Learning
StolenEncoder: Stealing Pre-trained Encoders in Self-supervised Learning
Yupei Liu
Jinyuan Jia
Hongbin Liu
Neil Zhenqiang Gong
MIACV
16
24
0
15 Jan 2022
Security for Machine Learning-based Software Systems: a survey of
  threats, practices and challenges
Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges
Huaming Chen
Muhammad Ali Babar
AAML
42
22
0
12 Jan 2022
Protecting Intellectual Property of Language Generation APIs with
  Lexical Watermark
Protecting Intellectual Property of Language Generation APIs with Lexical Watermark
Xuanli He
Qiongkai Xu
Lingjuan Lyu
Fangzhao Wu
Chenguang Wang
WaLM
180
95
0
05 Dec 2021
DeepSteal: Advanced Model Extractions Leveraging Efficient Weight
  Stealing in Memories
DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories
Adnan Siraj Rakin
Md Hafizul Islam Chowdhuryy
Fan Yao
Deliang Fan
AAML
MIACV
42
110
0
08 Nov 2021
Morphence: Moving Target Defense Against Adversarial Examples
Morphence: Moving Target Defense Against Adversarial Examples
Abderrahmen Amich
Birhanu Eshete
AAML
37
24
0
31 Aug 2021
Survey: Leakage and Privacy at Inference Time
Survey: Leakage and Privacy at Inference Time
Marija Jegorova
Chaitanya Kaul
Charlie Mayor
Alison Q. OÑeil
Alexander Weir
Roderick Murray-Smith
Sotirios A. Tsaftaris
PILM
MIACV
30
71
0
04 Jul 2021
HODA: Hardness-Oriented Detection of Model Extraction Attacks
HODA: Hardness-Oriented Detection of Model Extraction Attacks
A. M. Sadeghzadeh
Amir Mohammad Sobhanian
F. Dehghan
R. Jalili
MIACV
25
7
0
21 Jun 2021
Single-Layer Vision Transformers for More Accurate Early Exits with Less
  Overhead
Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead
Arian Bakhtiarnia
Qi Zhang
Alexandros Iosifidis
29
35
0
19 May 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
25
99
0
09 Mar 2021
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?
Jacson Rodrigues Correia-Silva
Rodrigo Berriel
C. Badue
Alberto F. de Souza
Thiago Oliveira-Santos
MLAU
21
14
0
21 Jan 2021
Developing Future Human-Centered Smart Cities: Critical Analysis of
  Smart City Security, Interpretability, and Ethical Challenges
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges
Kashif Ahmad
Majdi Maabreh
M. Ghaly
Khalil Khan
Junaid Qadir
Ala I. Al-Fuqaha
32
142
0
14 Dec 2020
Data-Free Model Extraction
Data-Free Model Extraction
Jean-Baptiste Truong
Pratyush Maini
R. Walls
Nicolas Papernot
MIACV
15
181
0
30 Nov 2020
Knowledge-Enriched Distributional Model Inversion Attacks
Knowledge-Enriched Distributional Model Inversion Attacks
Si-An Chen
Mostafa Kahla
R. Jia
Guo-Jun Qi
24
93
0
08 Oct 2020
Model extraction from counterfactual explanations
Model extraction from counterfactual explanations
Ulrich Aïvodji
Alexandre Bolot
Sébastien Gambs
MIACV
MLAU
33
51
0
03 Sep 2020
Simulating Unknown Target Models for Query-Efficient Black-box Attacks
Simulating Unknown Target Models for Query-Efficient Black-box Attacks
Chen Ma
L. Chen
Junhai Yong
MLAU
OOD
41
17
0
02 Sep 2020
A Survey of Privacy Attacks in Machine Learning
A Survey of Privacy Attacks in Machine Learning
M. Rigaki
Sebastian Garcia
PILM
AAML
39
213
0
15 Jul 2020
Stealing Deep Reinforcement Learning Models for Fun and Profit
Stealing Deep Reinforcement Learning Models for Fun and Profit
Kangjie Chen
Shangwei Guo
Tianwei Zhang
Xiaofei Xie
Yang Liu
MLAU
MIACV
OffRL
24
45
0
09 Jun 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
WaLM
AAML
15
218
0
27 Feb 2020
Model Weight Theft With Just Noise Inputs: The Curious Case of the
  Petulant Attacker
Model Weight Theft With Just Noise Inputs: The Curious Case of the Petulant Attacker
Nicholas Roberts
Vinay Uday Prabhu
Matthew McAteer
13
18
0
19 Dec 2019
DeepMimic: Mentor-Student Unlabeled Data Based Training
DeepMimic: Mentor-Student Unlabeled Data Based Training
Itay Mosafi
E. David
N. Netanyahu
24
6
0
24 Nov 2019
The Threat of Adversarial Attacks on Machine Learning in Network
  Security -- A Survey
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey
Olakunle Ibitoye
Rana Abou-Khamis
Mohamed el Shehaby
Ashraf Matrawy
M. O. Shafiq
AAML
39
68
0
06 Nov 2019
Extraction of Complex DNN Models: Real Threat or Boogeyman?
Extraction of Complex DNN Models: Real Threat or Boogeyman?
B. Atli
S. Szyller
Mika Juuti
Samuel Marchal
Nadarajah Asokan
MLAU
MIACV
33
45
0
11 Oct 2019
Prediction Poisoning: Towards Defenses Against DNN Model Stealing
  Attacks
Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks
Tribhuvanesh Orekondy
Bernt Schiele
Mario Fritz
AAML
19
164
0
26 Jun 2019
A framework for the extraction of Deep Neural Networks by leveraging
  public data
A framework for the extraction of Deep Neural Networks by leveraging public data
Soham Pal
Yash Gupta
Aditya Shukla
Aditya Kanade
S. Shevade
V. Ganapathy
FedML
MLAU
MIACV
36
56
0
22 May 2019
Stealing Neural Networks via Timing Side Channels
Stealing Neural Networks via Timing Side Channels
Vasisht Duddu
D. Samanta
D. V. Rao
V. Balas
AAML
MLAU
FedML
33
133
0
31 Dec 2018
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
309
3,115
0
04 Nov 2016
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