Summary of Pregip: Watermarking the Pretraining Of Graph Neural Networks For Deep Intellectual Property Protection, by Enyan Dai et al.
PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protection
by Enyan Dai, Minhua Lin, Suhang Wang
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel framework, PreGIP, to watermark Graph Neural Networks (GNNs) during pretraining for Intellectual Property (IP) protection. GNNs have shown great power in various downstream tasks when pre-trained on large amounts of data and computational resources. However, adversaries may illegally copy and deploy these pre-trained models, making IP protection crucial. The proposed method incorporates a task-free watermarking loss to watermark the embedding space of pretrained GNN encoders, ensuring finetuning resistance while maintaining high-quality embeddings. Experimental results demonstrate the effectiveness of PreGIP in protecting IP and achieving high performance for downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that people can’t steal Graph Neural Networks (GNNs) that are really good at doing certain jobs. GNNs are like super smart computers that learn from lots of data, but this makes them very valuable and someone might try to copy them without permission. The researchers came up with a new way called PreGIP to make these GNNs “watermarked” so they can’t be copied or changed. This helps keep the original creators’ ideas safe while still allowing other people to use the GNNs for their own projects. |
Keywords
* Artificial intelligence * Embedding space * Gnn * Pretraining
