  The deployment of Graph Neural Networks (GNNs) within Machine Learning as a
Service (MLaaS) has opened up new attack surfaces and an escalation in security
concerns regarding model-centric attacks. These attacks can directly manipulate
the GNN model parameters during serving, causing incorrect predictions and
posing substantial threats to essential GNN applications. Traditional integrity
verification methods falter in this context due to the limitations imposed by
MLaaS and the distinct characteristics of GNN models.
  In this research, we introduce a groundbreaking approach to protect GNN
models in MLaaS from model-centric attacks. Our approach includes a
comprehensive verification schema for GNN's integrity, taking into account both
transductive and inductive GNNs, and accommodating varying pre-deployment
knowledge of the models. We propose a query-based verification technique,
fortified with innovative node fingerprint generation algorithms. To deal with
advanced attackers who know our mechanisms in advance, we introduce randomized
fingerprint nodes within our design. The experimental evaluation demonstrates
that our method can detect five representative adversarial model-centric
attacks, displaying 2 to 4 times greater efficiency compared to baselines.
