On the Difficulty of Defending Self-Supervised Learning against Model
Extraction

On the Difficulty of Defending Self-Supervised Learning against Model Extraction

16 May 2022

Muhammad Ahmad Kaleem

Nicolas Papernot

Papers citing "On the Difficulty of Defending Self-Supervised Learning against Model Extraction"

9 / 9 papers shown

Title
Examining the Threat Landscape: Foundation Models and Model Stealing Ankita Raj Deepankar Varma Chetan Arora AAML 73 1 0 25 Feb 2025
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems Guangjing Wang Ce Zhou Yuanda Wang Bocheng Chen Hanqing Guo Qiben Yan AAML SILM 60 3 0 20 Nov 2023
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 41 196 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
Text and Code Embeddings by Contrastive Pre-Training Arvind Neelakantan Tao Xu Raul Puri Alec Radford Jesse Michael Han ... Tabarak Khan Toki Sherbakov Joanne Jang Peter Welinder Lilian Weng SSL AI4TS 218 421 0 24 Jan 2022
Increasing the Cost of Model Extraction with Calibrated Proof of Work Adam Dziedzic Muhammad Ahmad Kaleem Y. Lu Nicolas Papernot FedML MIACV AAML MLAU 60 28 0 23 Jan 2022
Dataset Inference: Ownership Resolution in Machine Learning Pratyush Maini Mohammad Yaghini Nicolas Papernot FedML 69 104 0 21 Apr 2021