GNNFingers: A Fingerprinting Framework for Verifying Ownerships of Graph Neural Networks

Graph neural networks (GNNs) have emerged as the state of the art for a variety of graph-related tasks and have been widely commercialized in real-world scenarios. Behind its revolutionary representation capability, the huge training costs also expose GNNs to the risks of potential model piracy attacks which threaten the intellectual property (IP) of GNNs. In this work, we design a novel and effective ownership verification framework for GNNs called GNNFingers to safeguard the IP of GNNs. The key design of the proposed framework is two-fold: graph fingerprint construction and robust verification module. With GNNFingers, a GNN model owner can verify if a deployed model is stolen from the source GNN simply by querying with graph inputs. Besides, GNNFingers could be applied to various GNN models and graph-related tasks. We extensively evaluate the proposed framework on various GNNs designed for multiple graph-related tasks including graph classification, graph matching, node classification, and link prediction. Our results show that GNNFingers can robustly distinguish post-processed surrogate GNNs from irrelevant GNNs, e.g., GNNFingers achieves 100% true positives and 100% true negatives on the test of 200 suspect GNNs of both graph classification and node classification tasks.