Cluster-Aware Attacks on Graph Watermarks

• URL: http://arxiv.org/abs/2504.17971v1
• Date: Thu, 24 Apr 2025 22:49:28 GMT
• Title: Cluster-Aware Attacks on Graph Watermarks
• Authors: Alexander Nemecek, Emre Yilmaz, Erman Ayday,
• Abstract summary: We introduce a cluster-aware threat model in which adversaries apply community-guided modifications to evade detection.<n>Our results show that cluster-aware attacks can reduce attribution accuracy by up to 80% more than random baselines.<n>We propose a lightweight embedding enhancement that distributes watermark nodes across graph communities.
• Score: 50.19105800063768
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data from domains such as social networks, healthcare, finance, and cybersecurity can be represented as graph-structured information. Given the sensitive nature of this data and their frequent distribution among collaborators, ensuring secure and attributable sharing is essential. Graph watermarking enables attribution by embedding user-specific signatures into graph-structured data. While prior work has addressed random perturbation attacks, the threat posed by adversaries leveraging structural properties through community detection remains unexplored. In this work, we introduce a cluster-aware threat model in which adversaries apply community-guided modifications to evade detection. We propose two novel attack strategies and evaluate them on real-world social network graphs. Our results show that cluster-aware attacks can reduce attribution accuracy by up to 80% more than random baselines under equivalent perturbation budgets on sparse graphs. To mitigate this threat, we propose a lightweight embedding enhancement that distributes watermark nodes across graph communities. This approach improves attribution accuracy by up to 60% under attack on dense graphs, without increasing runtime or structural distortion. Our findings underscore the importance of cluster-topological awareness in both watermarking design and adversarial modeling.

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