Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fingerprinting Deep Neural Networks Globally via Universal Adversarial
  Perturbations   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
\textit{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
\textit{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.

31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom