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20242026
most citedSoK: Data Minimization in Machine Learning

1 citations · 1 across the 11 of their papers we have counts for

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cs.CR2026

Every Bit, Everywhere, All at Once: A Binomial Multibit LLM Watermark

Thibaud Gloaguen, Robin Staab, Mark Vero +1

With LLM watermarking already being deployed commercially, practical applications increasingly require multibit watermarks that encode more complex payloads, such as user IDs or ti…

cs.CR2026

LLM Fingerprinting via Semantically Conditioned Watermarks

Thibaud Gloaguen, Robin Staab, Nikola Jovanović +1

Most LLM fingerprinting methods teach the model to respond to a few fixed queries with predefined atypical responses (keys). This memorization often does not survive common deploym…

cs.CR2026

A Unified Framework for LLM Watermarks

Thibaud Gloaguen, Robin Staab, Nikola Jovanović +1

LLM watermarks allow tracing AI-generated texts by inserting a detectable signal into their generated content. Recent works have proposed a wide range of watermarking algorithms, e…

cs.CR2025

Discovering Spoofing Attempts on Language Model Watermarks

Thibaud Gloaguen, Nikola Jovanović, Robin Staab +1

LLM watermarks stand out as a promising way to attribute ownership of LLM-generated text. One threat to watermark credibility comes from spoofing attacks, where an unauthorized thi…

cs.CR2025

Black-Box Detection of Language Model Watermarks

Thibaud Gloaguen, Nikola Jovanović, Robin Staab +1

Watermarking has emerged as a promising way to detect LLM-generated text, by augmenting LLM generations with later detectable signals. Recent work has proposed multiple families of…

cs.CR2025

Towards Watermarking of Open-Source LLMs

Thibaud Gloaguen, Nikola Jovanović, Robin Staab +1

While watermarks for closed LLMs have matured and have been included in large-scale deployments, these methods are not applicable to open-source models, which allow users full cont…