  In this paper, we propose a novel and practical mechanism which enables the
service provider to verify whether a suspect model is stolen from the victim
model via model extraction attacks. Our key insight is that the profile of a
DNN model's decision boundary can be uniquely characterized by its Universal
Adversarial Perturbations (UAPs). UAPs belong to a low-dimensional subspace and
piracy models' subspaces are more consistent with victim model's subspace
compared with non-piracy model. Based on this, we propose a UAP fingerprinting
method for DNN models and train an encoder via contrastive learning that takes
fingerprint as inputs, outputs a similarity score. Extensive studies show that
our framework can detect model IP breaches with confidence > 99.99 within only
20 fingerprints of the suspect model. It has good generalizability across
different model architectures and is robust against post-modifications on
stolen models.
