Protecting Pre-Trained Models from Unethical Fine-Tuning: A Non-Fine-Tunable Learning Approach

Core Concepts

SOPHON, a non-fine-tunable learning framework, prevents pre-trained models from being fine-tuned for unethical or harmful tasks while preserving their performance on the original task.

Abstract

The paper introduces a new learning paradigm called "non-fine-tunable learning" to address the issue of powerful pre-trained models being misused for unethical or illegal tasks. The key idea is to protect a pre-trained model from being fine-tuned for restricted domains while preserving its performance on the original task.
The authors develop SOPHON, a non-fine-tunable learning framework, that consists of two key optimization modules:

Fine-Tuning Suppression in Restricted Domain: This module is designed to degrade the fine-tuning performance of the pre-trained model in the restricted domain. It uses simulated fine-tuning processes to approximate the model performance after fine-tuning and optimizes the model accordingly.

Normal Training Reinforcement in Original Domain: This module is designed to maintain the performance of the protected model in the original domain.

The authors propose alternative loss functions, i.e., inverse cross-entropy and KL divergence from uniform distribution, to facilitate the convergence of the fine-tuning suppression process.
Extensive experiments are conducted on classification and generation tasks, covering seven restricted domains and six model architectures. The results show that fine-tuning the SOPHON-protected models incurs an overhead comparable to or even greater than training from scratch, effectively preventing the misuse of pre-trained models.

Stats

The original pre-trained model achieves 99.6% accuracy on the original domain.
Fine-tuning the original model can achieve 84.4%-86.7% accuracy on the restricted domain.
Fine-tuning the SOPHON-protected model only achieves 10.0%-15.2% accuracy on the restricted domain, comparable to random guessing.

Quotes

"Fine-tuning the SOPHON-protected models has overhead close to or even greater than training from scratch, under three fine-tuning methods, five optimizers, various learning rates and batch sizes."
"The proposed inverse cross-entropy and KL divergence from uniform distribution losses can better boost the convergence of fine-tuning suppression process compared to the conventional cross-entropy loss."

Key Insights Distilled From

SOPHON: Non-Fine-Tunable Learning to Restrain Task Transferability For Pre-trained Models

by Jiangyi Deng... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12699.pdf

Deeper Inquiries

How can SOPHON be extended to protect pre-trained models from fine-tuning in multiple restricted domains simultaneously

To extend SOPHON to protect pre-trained models from fine-tuning in multiple restricted domains simultaneously, a few modifications and enhancements can be implemented. One approach is to introduce a multi-task learning framework where the model is trained to resist fine-tuning in multiple restricted domains concurrently. This can involve incorporating different loss functions for each domain and optimizing the model parameters to perform poorly in all specified domains. Additionally, the fine-tuning simulation process can be expanded to simulate various fine-tuning strategies across different domains, ensuring that the model remains non-fine-tunable in each domain. By adjusting the optimization process to handle multiple restricted domains, SOPHON can effectively protect pre-trained models from being fine-tuned for unethical or harmful tasks across various domains simultaneously.

What are the potential limitations or drawbacks of the non-fine-tunable learning approach, and how can they be addressed

While the non-fine-tunable learning approach introduced by SOPHON offers significant advantages in preventing the misuse of pre-trained models, there are potential limitations and drawbacks that need to be addressed. One limitation is the computational complexity and resource requirements involved in simulating fine-tuning processes for multiple strategies and domains. This can lead to increased training times and resource consumption, which may not be feasible in real-time or resource-constrained environments. To address this, optimization techniques such as parallel processing or distributed computing can be utilized to improve efficiency and scalability. Another drawback is the potential trade-off between model performance in the original domain and resistance to fine-tuning in restricted domains. Balancing these objectives effectively requires careful tuning of hyperparameters and loss functions. Regular monitoring and adjustment of these parameters during training can help mitigate this challenge and optimize the overall performance of the model.

How can the non-fine-tunable learning framework be applied to other domains beyond deep learning, such as natural language processing or robotics, to prevent the misuse of powerful models

The non-fine-tunable learning framework can be applied to various domains beyond deep learning, such as natural language processing (NLP) or robotics, to prevent the misuse of powerful models in these fields. In NLP, the framework can be adapted to protect pre-trained language models from being fine-tuned for unethical tasks like generating harmful or misleading content. By designing specialized loss functions and fine-tuning simulation processes tailored to NLP tasks, SOPHON can ensure that language models remain non-fine-tunable for specific applications. Similarly, in robotics, the framework can be utilized to safeguard pre-trained models used for autonomous systems or robotic applications from being repurposed for malicious activities. By extending the principles of non-fine-tunable learning to these domains, SOPHON can contribute to the development of safe and responsible AI technologies across a wide range of applications.