Robust Hashing for Neural Network Models via Heterogeneous Graph Representation

Abstract

How to protect the intellectual property (IP) of neural network models has become a hot topic in current research. Model hashing as an important model protection scheme, which achieves model IP protection by extracting model feature-based, compact hash codes and calculating the hash distance between original and suspicious models. To realize model IP protection across platforms and environments, we propose a robust hashing scheme for neural network models via heterogeneous graph representation, which can effectively detect the illegal copy of neural network models and doesn't degrade the model performance. Specifically, we first convert the neural network model into a heterogeneous graph and analyze its node attribute data. Then, a graph embedding learning method is used to extract the feature vectors of the model based on different attribute data of graph nodes. Finally, the hash code that can be used for model copy detection is generated based on the designed hash networks with quantization and triplet losses. Experimental results show that our scheme not only exhibits satisfactory robustness to different types of robustness graph attacks but also achieves satisfactory performances of discrimination and generalizability.