  As Artificial Intelligence as a Service gains popularity, protecting
well-trained models as intellectual property is becoming increasingly
important. There are two common types of protection methods: ownership
verification and usage authorization. In this paper, we propose
Non-Transferable Learning (NTL), a novel approach that captures the exclusive
data representation in the learned model and restricts the model generalization
ability to certain domains. This approach provides effective solutions to both
model verification and authorization. Specifically: 1) For ownership
verification, watermarking techniques are commonly used but are often
vulnerable to sophisticated watermark removal methods. By comparison, our
NTL-based ownership verification provides robust resistance to state-of-the-art
watermark removal methods, as shown in extensive experiments with 6 removal
approaches over the digits, CIFAR10 & STL10, and VisDA datasets. 2) For usage
authorization, prior solutions focus on authorizing specific users to access
the model, but authorized users can still apply the model to any data without
restriction. Our NTL-based authorization approach instead provides data-centric
protection, which we call applicability authorization, by significantly
degrading the performance of the model on unauthorized data. Its effectiveness
is also shown through experiments on the aforementioned datasets.
