Summary of Sophon: Non-fine-tunable Learning to Restrain Task Transferability For Pre-trained Models, by Jiangyi Deng (1) et al.
SOPHON: Non-Fine-Tunable Learning to Restrain Task Transferability For Pre-trained Models
by Jiangyi Deng, Shengyuan Pang, Yanjiao Chen, Liangming Xia, Yijie Bai, Haiqin Weng, Wenyuan Xu
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach called non-fine-tunable learning, which aims to prevent pre-trained deep learning models from being adapted for unethical or illegal purposes, such as privacy inference and unsafe content generation. The proposed protection framework, SOPHON, reinforces the pre-trained model to resist fine-tuning in specific restricted domains. To achieve this, the authors design sophisticated fine-tuning simulation and evaluation algorithms inspired by model-agnostic meta-learning. The optimization process is carefully crafted to entrap the pre-trained model within a hard-to-escape local optimum regarding restricted domains. Experimental results demonstrate the effectiveness of SOPHON on various deep learning models, including classification and generation tasks, across multiple restricted domains and model architectures. SOPHON’s robustness is further confirmed against different fine-tuning methods, optimizers, learning rates, and batch sizes. This research contributes to the development of safe and responsible AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if someone could easily take a powerful artificial intelligence model and use it for something bad, like invading your privacy or spreading harmful content. That’s exactly what this paper is trying to prevent. The researchers have created a new way to protect pre-trained models from being misused, called non-fine-tunable learning. They’ve also developed a special framework called SOPHON that makes sure the model can’t be adapted for bad purposes while still keeping its original abilities. The team tested SOPHON on different types of AI models and scenarios, and it worked well. This breakthrough has important implications for making artificial intelligence safer and more responsible. |
Keywords
» Artificial intelligence » Classification » Deep learning » Fine tuning » Inference » Meta learning » Optimization
