A Robust Watermark-based Fingerprint Framework for GNNs Ownership Verification

This paper presents a watermarking and fingerprinting framework to verify ownership of Graph Neural Networks (GNNs), addressing IP protection concerns for proprietary models. The approach embeds robust watermarks into GNN parameters that persist through model fine-tuning and extraction attacks, enabling organizations to prove legitimate ownership of their trained models.

This paper presents a watermarking and fingerprinting framework to verify ownership of Graph Neural Networks (GNNs), addressing IP protection concerns for proprietary models. The approach embeds robust watermarks into GNN parameters that persist through model fine-tuning and extraction attacks, enabling organizations to prove legitimate ownership of their trained models.

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