  In this paper, we propose a model protection method for convolutional neural
networks (CNNs) with a secret key so that authorized users get a high
classification accuracy, and unauthorized users get a low classification
accuracy. The proposed method applies a block-wise transformation with a secret
key to feature maps in the network. Conventional key-based model protection
methods cannot maintain a high accuracy when a large key space is selected. In
contrast, the proposed method not only maintains almost the same accuracy as
non-protected accuracy, but also has a larger key space. Experiments were
carried out on the CIFAR-10 dataset, and results show that the proposed model
protection method outperformed the previous key-based model protection methods
in terms of classification accuracy, key space, and robustness against key
estimation attacks and fine-tuning attacks.
