Deep learning has achieved remarkable progress in various applications,
heightening the importance of safeguarding the intellectual property (IP) of
well-trained models. It entails not only authorizing usage but also ensuring
the deployment of models in authorized data domains, i.e., making models
exclusive to certain target domains. Previous methods necessitate concurrent
access to source training data and target unauthorized data when performing IP
protection, making them risky and inefficient for decentralized private data.
In this paper, we target a practical setting where only a well-trained source
model is available and investigate how we can realize IP protection. To achieve
this, we propose a novel MAsk Pruning (MAP) framework. MAP stems from an
intuitive hypothesis, i.e., there are target-related parameters in a
well-trained model, locating and pruning them is the key to IP protection.
Technically, MAP freezes the source model and learns a target-specific binary
mask to prevent unauthorized data usage while minimizing performance
degradation on authorized data. Moreover, we introduce a new metric aimed at
achieving a better balance between source and target performance degradation.
To verify the effectiveness and versatility, we have evaluated MAP in a variety
of scenarios, including vanilla source-available, practical source-free, and
challenging data-free. Extensive experiments indicate that MAP yields new
state-of-the-art performance.

基于深度学习模型的知识产权保护问题，本研究提出了一种新颖的 MAsk Pruning（MAP）框架，通过冻结源模型，并学习目标特定的二进制掩模来实现未授权数据使用的防护，同时最小化对授权数据的性能降低，实验证明该方法在多种场景下达到了新的最佳性能。