  Instead of building deep learning models from scratch, developers are more
and more relying on adapting pre-trained models to their customized tasks.
However, powerful pre-trained models may be misused for unethical or illegal
tasks, e.g., privacy inference and unsafe content generation. In this paper, we
introduce a pioneering learning paradigm, non-fine-tunable learning, which
prevents the pre-trained model from being fine-tuned to indecent tasks while
preserving its performance on the original task. To fulfill this goal, we
propose SOPHON, a protection framework that reinforces a given pre-trained
model to be resistant to being fine-tuned in pre-defined restricted domains.
Nonetheless, this is challenging due to a diversity of complicated fine-tuning
strategies that may be adopted by adversaries. Inspired by model-agnostic
meta-learning, we overcome this difficulty by designing sophisticated
fine-tuning simulation and fine-tuning evaluation algorithms. In addition, we
carefully design the optimization process to entrap the pre-trained model
within a hard-to-escape local optimum regarding restricted domains. We have
conducted extensive experiments on two deep learning modes (classification and
generation), seven restricted domains, and six model architectures to verify
the effectiveness of SOPHON. Experiment results verify that fine-tuning
SOPHON-protected models incurs an overhead comparable to or even greater than
training from scratch. Furthermore, we confirm the robustness of SOPHON to
three fine-tuning methods, five optimizers, various learning rates and batch
sizes. SOPHON may help boost further investigations into safe and responsible
AI.
