Membership Inference Attacks Against Semantic Segmentation Models. (arXiv:2212.01082v1 [cs.CR])

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 the 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 …

attacks segmentation