3 citations · 5 across the 4 of their papers we have counts for
5 papers
I Stolenly Swear That I Am Up to (No) Good: Design and Evaluation of Model Stealing Attacks
Daryna Oliynyk, Rudolf Mayer, Kathrin Grosse +1
Model stealing attacks endanger the confidentiality of machine learning models offered as a service. Although these models are kept secret, a malicious party can query a model to l…
NCorr-FP: A Neighbourhood-based Correlation-preserving Fingerprinting Scheme for Intellectual Property Protection of Structured Data
Tanja Šarčević, Andreas Rauber, Rudolf Mayer
Ensuring data ownership and traceability of unauthorised redistribution are central to safeguarding intellectual property in shared data environments. Data fingerprinting addresses…
Attackers Can Do Better: Over- and Understated Factors of Model Stealing Attacks
Daryna Oliynyk, Rudolf Mayer, Andreas Rauber
Machine learning models were shown to be vulnerable to model stealing attacks, which lead to intellectual property infringement. Among other methods, substitute model training is a…
U Can't Gen This? A Survey of Intellectual Property Protection Methods for Data in Generative AI
Tanja Šarčević, Alicja Karlowicz, Rudolf Mayer +2
Large Generative AI (GAI) models have the unparalleled ability to generate text, images, audio, and other forms of media that are increasingly indistinguishable from human-generate…
Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis
Anahid Jalali, Bernhard Haslhofer, Simone Kriglstein +1
Post-hoc explainability methods aim to clarify predictions of black-box machine learning models. However, it is still largely unclear how well users comprehend the provided explana…