  Membership inference (MI) determines if a sample was part of a victim model
training set. Recent development of MI attacks focus on record-level membership
inference which limits their application in many real-world scenarios. For
example, in the person re-identification task, the attacker (or investigator)
is interested in determining if a user's images have been used during training
or not. However, the exact training images might not be accessible to the
attacker. In this paper, we develop a user-level MI attack where the goal is to
find if any sample from the target user has been used during training even when
no exact training sample is available to the attacker. We focus on metric
embedding learning due to its dominance in person re-identification, where
user-level MI attack is more sensible. We conduct an extensive evaluation on
several datasets and show that our approach achieves high accuracy on
user-level MI task.
