  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.
