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Florian Tramèr
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Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators


Feb 27, 2023
Keane Lucas, Matthew Jagielski, Florian Tramèr, Lujo Bauer, Nicholas Carlini

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Poisoning Web-Scale Training Datasets is Practical


Feb 20, 2023
Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum Anderson, Andreas Terzis, Kurt Thomas, Florian Tramèr

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Tight Auditing of Differentially Private Machine Learning


Feb 15, 2023
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis

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Extracting Training Data from Diffusion Models


Jan 30, 2023
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace

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Considerations for Differentially Private Learning with Large-Scale Public Pretraining


Dec 13, 2022
Florian Tramèr, Gautam Kamath, Nicholas Carlini

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Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy


Oct 31, 2022
Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini

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Red-Teaming the Stable Diffusion Safety Filter


Oct 11, 2022
Javier Rando, Daniel Paleka, David Lindner, Lennard Heim, Florian Tramèr

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Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems


Oct 07, 2022
Chawin Sitawarin, Florian Tramèr, Nicholas Carlini

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* Code can be found at https://github.com/google-research/preprocessor-aware-black-box-attack 
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SNAP: Efficient Extraction of Private Properties with Poisoning


Aug 25, 2022
Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan Ullman

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* 27 pages, 13 figures 
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Measuring Forgetting of Memorized Training Examples


Jun 30, 2022
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang

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* 19 pages, 7 figures 
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