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Chobola, T.* ; Usynin, D.* ; Kaissis, G.

Membership inference attacks against semantic segmentation models.

In: (AISec 2023 - Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, 30 November 2023, Copenhagen, Denmark). 1601 Broadway, 10th Floor, New York, Ny, United States: Assoc Computing Machinery, 2023. 43-53 (AISec 2023 - Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security)
DOI
Membership inference attacks aim to infer whether a data record has been used to train a target model by observing its predictions. In sensitive domains such as healthcare, this can constitute a severe privacy violation. In this work we attempt to address an existing knowledge gap by conducting an exhaustive study of membership inference attacks and defences in the domain of semantic image segmentation. Our findings indicate that for certain threat models, these learning settings can be considerably more vulnerable than the previously considered classification settings. We quantitatively evaluate the attacks on a number of popular model architectures across a variety of semantic segmentation tasks, demonstrating that membership inference attacks in this domain can achieve a high success rate and defending against them may result in unfavourable privacy-utility trade-offs or increased computational costs.
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Publication type Article: Conference contribution
Corresponding Author
Keywords Membership Inference Attack ; Neural Networks ; Semantic Segmentation; Privacy
ISSN (print) / ISBN 9798400702600
Conference Title AISec 2023 - Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security
Conference Date 30 November 2023
Conference Location Copenhagen, Denmark
Quellenangaben Volume: , Issue: , Pages: 43-53 Article Number: , Supplement: ,
Publisher Assoc Computing Machinery
Publishing Place 1601 Broadway, 10th Floor, New York, Ny, United States
Non-patent literature Publications
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute for Machine Learning in Biomed Imaging (IML)
Grants Technical University of Munich/Imperial College London Joint Academy for Doctoral Studies
Bavarian State Ministry for Science and the Arts
German Federal Ministry of Education and Research