Summary of Guise: Graph Gaussian Shading Watermark, by Renyi Yang
GUISE: Graph GaUssIan Shading watErmark
by Renyi Yang
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multimedia (cs.MM)
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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 an innovative approach to protecting intellectual property in molecular graph generation using a watermarking technique adapted from the Gaussian Shading method. The Latent 3D Graph Diffusion (LDM-3DG) model is used as a basis, which effectively captures the complexities of molecular structures and preserves essential symmetries and topological features. By duplicating and padding the watermark diffusion process, the authors make their technique adaptable to various message types. Experimental results on publicly available datasets QM9 and Drugs demonstrate that watermarked molecules maintain statistical parity in 9 out of 10 performance metrics compared to the original, with a 100% detection rate and 99% extraction rate in a 2D decoded pipeline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep important ideas safe by adding special marks to them. It uses a new way to create complex molecules that can be used for things like making medicine or developing new materials. The authors take an existing method that works well with pictures and music, and adapt it to work with these complex molecules. They test their idea on two big datasets and show that the marked-up molecules are just as good as the originals, but much harder to copy or change. |
Keywords
» Artificial intelligence » Diffusion
