Increasing the Cost of Model Extraction with Calibrated Proof of Work

23 January 2022

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

8 / 8 papers shown

Title
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders Jan Dubiñski Stanislaw Pawlak Franziska Boenisch Tomasz Trzciñski Adam Dziedzic AAML 29 3 0 12 Oct 2023
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 29 196 0 25 May 2023
Dataset Inference for Self-Supervised Models Adam Dziedzic Haonan Duan Muhammad Ahmad Kaleem Nikita Dhawan Jonas Guan Yannis Cattan Franziska Boenisch Nicolas Papernot 24 26 0 16 Sep 2022
I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences Daryna Oliynyk Rudolf Mayer Andreas Rauber 39 106 0 16 Jun 2022
On the Difficulty of Defending Self-Supervised Learning against Model Extraction Adam Dziedzic Nikita Dhawan Muhammad Ahmad Kaleem Jonas Guan Nicolas Papernot MIACV 46 22 0 16 May 2022
One Picture is Worth a Thousand Words: A New Wallet Recovery Process H. Chabanne Vincent Despiegel Linda Guiga 22 0 0 05 May 2022
Stateful Detection of Model Extraction Attacks Soham Pal Yash Gupta Aditya Kanade S. Shevade MLAU 52 24 0 12 Jul 2021
Dataset Inference: Ownership Resolution in Machine Learning Pratyush Maini Mohammad Yaghini Nicolas Papernot FedML 69 103 0 21 Apr 2021