  Model protection is vital when deploying Convolutional Neural Networks (CNNs)
for commercial services, due to the massive costs of training them. In this
work, we propose a selective encryption (SE) algorithm to protect CNN models
from unauthorized access, with a unique feature of providing hierarchical
services to users. Our algorithm firstly selects important model parameters via
the proposed Probabilistic Selection Strategy (PSS). It then encrypts the most
important parameters with the designed encryption method called Distribution
Preserving Random Mask (DPRM), so as to maximize the performance degradation by
encrypting only a very small portion of model parameters. We also design a set
of access permissions, using which different amounts of the most important
model parameters can be decrypted. Hence, different levels of model performance
can be naturally provided for users. Experimental results demonstrate that the
proposed scheme could effectively protect the classification model VGG19 by
merely encrypting 8% parameters of convolutional layers. We also implement the
proposed model protection scheme in the denoising model DnCNN, showcasing the
hierarchical denoising services
