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Computer Science > Machine Learning

arXiv:2510.25934 (cs)
[Submitted on 29 Oct 2025]

Title:Robust GNN Watermarking via Implicit Perception of Topological
Invariants

Authors:Jipeng Li, Yannning Shen
View a PDF of the paper titled Robust GNN Watermarking via Implicit
Perception of Topological Invariants, by Jipeng Li and Yannning Shen
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    Abstract:Graph Neural Networks (GNNs) are valuable intellectual
    property, yet many watermarks rely on backdoor triggers that
    break under common model edits and create ownership ambiguity. We
    present InvGNN-WM, which ties ownership to a model's implicit
    perception of a graph invariant, enabling trigger-free, black-box
    verification with negligible task impact. A lightweight head
    predicts normalized algebraic connectivity on an owner-private
    carrier set; a sign-sensitive decoder outputs bits, and a
    calibrated threshold controls the false-positive rate. Across
    diverse node and graph classification datasets and backbones,
    InvGNN-WM matches clean accuracy while yielding higher watermark
    accuracy than trigger- and compression-based baselines. It
    remains strong under unstructured pruning, fine-tuning, and
    post-training quantization; plain knowledge distillation (KD)
    weakens the mark, while KD with a watermark loss (KD+WM) restores
    it. We provide guarantees for imperceptibility and robustness,
    and we prove that exact removal is NP-complete.

Subjects: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
Cite as:  arXiv:2510.25934 [cs.LG]
          (or arXiv:2510.25934v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2510.25934
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jipeng Li [view email]
[v1] Wed, 29 Oct 2025 20:12:42 UTC (103 KB)
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    View a PDF of the paper titled Robust GNN Watermarking via
    Implicit Perception of Topological Invariants, by Jipeng Li and
    Yannning Shen
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