  The training of Deep Neural Networks (DNN) is costly, thus DNN can be
considered as the intellectual properties (IP) of model owners. To date, most
of the existing protection works focus on verifying the ownership after the DNN
model is stolen, which cannot resist piracy in advance. To this end, we propose
an active DNN IP protection method based on adversarial examples against DNN
piracy, named ActiveGuard. ActiveGuard aims to achieve authorization control
and users' fingerprints management through adversarial examples, and can
provide ownership verification. Specifically, ActiveGuard exploits the
elaborate adversarial examples as users' fingerprints to distinguish authorized
users from unauthorized users. Legitimate users can enter fingerprints into DNN
for identity authentication and authorized usage, while unauthorized users will
obtain poor model performance due to an additional control layer. In addition,
ActiveGuard enables the model owner to embed a watermark into the weights of
DNN. When the DNN is illegally pirated, the model owner can extract the
embedded watermark and perform ownership verification. Experimental results
show that, for authorized users, the test accuracy of LeNet-5 and Wide Residual
Network (WRN) models are 99.15% and 91.46%, respectively, while for
unauthorized users, the test accuracy of the two DNNs are only 8.92% (LeNet-5)
and 10% (WRN), respectively. Besides, each authorized user can pass the
fingerprint authentication with a high success rate (up to 100%). For ownership
verification, the embedded watermark can be successfully extracted, while the
normal performance of the DNN model will not be affected. Further, ActiveGuard
is demonstrated to be robust against fingerprint forgery attack, model
fine-tuning attack and pruning attack.
