  Auditing the use of data in training machine-learning (ML) models is an
increasingly pressing challenge, as myriad ML practitioners routinely leverage
the effort of content creators to train models without their permission. In
this paper, we propose a general method to audit an ML model for the use of a
data-owner's data in training, without prior knowledge of the ML task for which
the data might be used. Our method leverages any existing black-box membership
inference method, together with a sequential hypothesis test of our own design,
to detect data use with a quantifiable, tunable false-detection rate. We show
the effectiveness of our proposed framework by applying it to audit data use in
two types of ML models, namely image classifiers and foundation models.
