GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? 事件

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? arXiv:2605.12827v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) deployed as cloud services can be stolen through model-extraction attacks, which train a surrogate from query responses to reproduce the target's behavior, and a growing line of ownership defenses tries to prevent or trace such theft. This paper asks two questions: how hard is it to steal a GNN, and can we stop it? Prior work c