6.3k citations
- University of California, BerkeleyUS18 papers
- Google (United States)US11 papers
- Stanford UniversityUS8 papers
- Carnegie Mellon UniversityUS6 papers
- Berkeley CollegeUS4 papers
- Uber AI (United States)US4 papers
- ETH ZurichCH3 papers
- Institut national de recherche en sciences et technologies du numériqueFR3 papers
- McGill UniversityCA3 papers
- Microsoft (United States)US3 papers
- Mila - Quebec Artificial Intelligence InstituteCA3 papers
- The University of Texas at AustinUS3 papers
Showing cs.CRShow all
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cs.CR2020★ 273 cited
Extracting Training Data from Large Language Models
Nicholas Carlini, Florian Tramer, Eric Wallace +9
It has become common to publish large (billion parameter) language models that have been trained on private datasets. This paper demonstrates that in such settings, an adversary ca…
cs.CR2016★ 95 cited
Practical Black-Box Attacks against Machine Learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow +3
Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing…