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Increasing the Cost of Model Extraction with Calibrated Proof of Work
v1v2v3 (latest)

Increasing the Cost of Model Extraction with Calibrated Proof of Work

International Conference on Learning Representations (ICLR), 2022
23 January 2022
Adam Dziedzic
Muhammad Ahmad Kaleem
Y. Lu
Nicolas Papernot
    FedMLMIACVAAMLMLAU
ArXiv (abs)PDFHTML

Papers citing "Increasing the Cost of Model Extraction with Calibrated Proof of Work"

14 / 14 papers shown
LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems
LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems
Vitor Hugo Galhardo Moia
Igor Jochem Sanz
Gabriel Antonio Fontes Rebello
Rodrigo Duarte de Meneses
Briland Hitaj
Ulf Lindqvist
338
1
0
12 Sep 2025
Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs
Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs
Xun Wang
Jing Xu
Franziska Boenisch
Michael Backes
Christopher A. Choquette-Choo
Adam Dziedzic
AAML
282
1
0
19 Jun 2025
A Comprehensive Survey of Attack Techniques, Implementation, and
  Mitigation Strategies in Large Language Models
A Comprehensive Survey of Attack Techniques, Implementation, and Mitigation Strategies in Large Language Models
Aysan Esmradi
Daniel Wankit Yip
C. Chan
AAML
297
29
0
18 Dec 2023
Beyond Labeling Oracles: What does it mean to steal ML models?
Beyond Labeling Oracles: What does it mean to steal ML models?
Avital Shafran
Ilia Shumailov
Murat A. Erdogdu
Nicolas Papernot
AAML
423
5
0
03 Oct 2023
Extracting Cloud-based Model with Prior Knowledge
Extracting Cloud-based Model with Prior Knowledge
Songtao Zhao
Kangjie Chen
Meng Hao
Jian Zhang
Guowen Xu
Hongwei Li
Tianwei Zhang
AAMLMIACVSILMMLAUSLR
484
6
0
07 Jun 2023
The False Promise of Imitating Proprietary LLMs
The False Promise of Imitating Proprietary LLMs
Arnav Gudibande
Eric Wallace
Charles Burton Snell
Xinyang Geng
Hao Liu
Pieter Abbeel
Sergey Levine
Dawn Song
ALM
475
264
0
25 May 2023
On the Robustness of Dataset Inference
On the Robustness of Dataset Inference
S. Szyller
Rui Zhang
Enchao Gong
Nadarajah Asokan
AAML
393
10
0
24 Oct 2022
Dataset Inference for Self-Supervised Models
Dataset Inference for Self-Supervised ModelsNeural Information Processing Systems (NeurIPS), 2022
Adam Dziedzic
Haonan Duan
Muhammad Ahmad Kaleem
Nikita Dhawan
Jonas Guan
Yannis Cattan
Franziska Boenisch
Nicolas Papernot
462
44
0
16 Sep 2022
Conflicting Interactions Among Protection Mechanisms for Machine
  Learning Models
Conflicting Interactions Among Protection Mechanisms for Machine Learning ModelsAAAI Conference on Artificial Intelligence (AAAI), 2022
S. Szyller
Nadarajah Asokan
AAML
436
13
0
05 Jul 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 DefencesACM Computing Surveys (ACM CSUR), 2022
Daryna Oliynyk
Rudolf Mayer
Andreas Rauber
393
167
0
16 Jun 2022
On the Difficulty of Defending Self-Supervised Learning against Model
  Extraction
On the Difficulty of Defending Self-Supervised Learning against Model ExtractionInternational Conference on Machine Learning (ICML), 2022
Adam Dziedzic
Nikita Dhawan
Muhammad Ahmad Kaleem
Jonas Guan
Nicolas Papernot
MIACV
446
32
0
16 May 2022
One Picture is Worth a Thousand Words: A New Wallet Recovery Process
One Picture is Worth a Thousand Words: A New Wallet Recovery ProcessGlobal Communications Conference (GLOBECOM), 2022
H. Chabanne
Vincent Despiegel
Linda Guiga
320
0
0
05 May 2022
ShadowNet: A Secure and Efficient On-device Model Inference System for
  Convolutional Neural Networks
ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural NetworksIEEE Symposium on Security and Privacy (IEEE S&P), 2020
Zhichuang Sun
Ruimin Sun
Changming Liu
A. Chowdhury
Long Lu
S. Jha
FedML
383
36
0
11 Nov 2020
Thieves on Sesame Street! Model Extraction of BERT-based APIs
Thieves on Sesame Street! Model Extraction of BERT-based APIsInternational Conference on Learning Representations (ICLR), 2019
Kalpesh Krishna
Gaurav Singh Tomar
Ankur P. Parikh
Nicolas Papernot
Mohit Iyyer
MIACVMLAU
688
240
0
27 Oct 2019
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