  Since production-level trained deep neural networks (DNNs) are of a great
business value, protecting such DNN models against copyright infringement and
unauthorized access is in a rising demand. However, conventional model
protection methods focused only the image classification task, and these
protection methods were never applied to semantic segmentation although it has
an increasing number of applications. In this paper, we propose to protect
semantic segmentation models from unauthorized access by utilizing block-wise
transformation with a secret key for the first time. Protected models are
trained by using transformed images. Experiment results show that the proposed
protection method allows rightful users with the correct key to access the
model to full capacity and deteriorate the performance for unauthorized users.
However, protected models slightly drop the segmentation performance compared
to non-protected models.
