GUISE: Graph GaUssIan Shading watErmark
Renyi Yang

TL;DR
This paper introduces GUISE, a watermarking method adapted for molecular graph generation using latent 3D graph diffusion, ensuring content protection without compromising model performance.
Contribution
It adapts Gaussian Shading watermarking to the domain of molecular graph diffusion, enabling robust, lossless watermarking for complex graph-based AI models.
Findings
Watermarked molecules retain statistical properties in 9 out of 10 metrics.
Achieves 100% detection and 99% extraction rates.
Demonstrates robustness against post-editing attacks.
Abstract
In the expanding field of generative artificial intelligence, integrating robust watermarking technologies is essential to protect intellectual property and maintain content authenticity. Traditionally, watermarking techniques have been developed primarily for rich information media such as images and audio. However, these methods have not been adequately adapted for graph-based data, particularly molecular graphs. Latent 3D graph diffusion(LDM-3DG) is an ascendant approach in the molecular graph generation field. This model effectively manages the complexities of molecular structures, preserving essential symmetries and topological features. We adapt the Gaussian Shading, a proven performance lossless watermarking technique, to the latent graph diffusion domain to protect this sophisticated new technology. Our adaptation simplifies the watermark diffusion process through duplication…
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Taxonomy
TopicsAlgorithms and Data Compression · Semantic Web and Ontologies · Data Mining Algorithms and Applications
MethodsDiffusion
