Training a good deep learning model requires substantial data and computing
resources, which makes the resulting neural model a valuable intellectual
property. To prevent the neural network from being undesirably exploited,
non-transferable learning has been proposed to reduce the model generalization
ability in specific target domains. However, existing approaches require
labeled data for the target domain which can be difficult to obtain.
Furthermore, they do not have the mechanism to still recover the model's
ability to access the target domain. In this paper, we propose a novel
unsupervised non-transferable learning method for the text classification task
that does not require annotated target domain data. We further introduce a
secret key component in our approach for recovering the access to the target
domain, where we design both an explicit and an implicit method for doing so.
Extensive experiments demonstrate the effectiveness of our approach.

本文提出了一种新的针对文本分类任务的无监督非可转移学习方法，并介绍了一个秘密密钥组件来恢复访问目标域的能力。经过广泛的实验，证明了我们的方法的有效性。

无监督不可迁移文本分类

Unsupervised Non-transferable Text Classification

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.

提出了一种名为非可转让学习（NTL）的方法来保护良好训练的人工智能模型的知识产权，NTL 可用于所有权验证和使用授权，既能抵御最新的水印去除方法，又能够限制模型的推广能力，最终实现通过数据本身的授权，达到了对数据的保护。