Data Isotopes for Data Provenance in DNNs. (arXiv:2208.13893v1 [cs.CR])

Today, creators of data-hungry deep neural networks (DNNs) scour the Internet
for training fodder, leaving users with little control over or knowledge of
when their data is appropriated for model training. To empower users to
counteract unwanted data use, we design, implement and evaluate a practical
system that enables users to detect if their data was used to train an DNN
model. We show how users can create special data points we call isotopes, which
introduce "spurious features" into DNNs …

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