  This paper presents a novel model protection paradigm ModelLock that locks
(destroys) the performance of a model on normal clean data so as to make it
unusable or unextractable without the right key. Specifically, we proposed a
diffusion-based framework dubbed ModelLock that explores text-guided image
editing to transform the training data into unique styles or add new objects in
the background. A model finetuned on this edited dataset will be locked and can
only be unlocked by the key prompt, i.e., the text prompt used to transform the
data. We conduct extensive experiments on both image classification and
segmentation tasks, and show that 1) ModelLock can effectively lock the
finetuned models without significantly reducing the expected performance, and
more importantly, 2) the locked model cannot be easily unlocked without knowing
both the key prompt and the diffusion model. Our work opens up a new direction
for intellectual property protection of private models.
