Multi-concept Model Immunization through Differentiable Model Merging
This addresses the risk of model misuse in real-world scenarios where immunization against multiple concepts is needed, representing an incremental extension from single-concept to multi-concept settings.
The paper tackles the problem of making open-sourced models resistant to fine-tuning on multiple harmful concepts, proposing a method that learns a single difficult initialization using differentiable merging, and demonstrates effectiveness in multi-concept re-learning and personalization experiments.
Model immunization is an emerging direction that aims to mitigate the potential risk of misuse associated with open-sourced models and advancing adaptation methods. The idea is to make the released models' weights difficult to fine-tune on certain harmful applications, hence the name ``immunized''. Recent work on model immunization focuses on the single-concept setting. However, models need to be immunized against multiple concepts in real-world situations. To address this gap, we propose an immunization algorithm that, simultaneously, learns a single ``difficult initialization'' for adaptation methods over a set of concepts. We achieve this by incorporating a differentiable merging layer that combines a set of model weights adapted over multiple concepts. In our experiments, we demonstrate the effectiveness of multi-concept immunization by generalizing prior work's experiment setup of re-learning and personalization adaptation to multiple concepts.