Robust and Secure Hashing Towards Pirated Neural Network Model Detection

With accelerating development of artificial intelligence, neural network models have been applied in many fields. The structure designing and training for models consume vast manpower and computing resources, which are the core interests of related research institutions and enterprises. However, the high-value attributes of neural network models also attract the attention of pirates, who may steal them for illegal profits and also slightly modify model parameters to escape model piracy detection. In order to solve the problem, in this work, we propose a robust and secure model hashing method based on dynamic branch reorganization and multi-feature fusion. Detailedly, we dynamically adjust the branches in our model hashing network to extract the robust features of all kinds of model parameters for generating the hash sequence. Besides, we integrate the encryption and the feature matrix generation to a unified stage in the hash generation for resisting possible encryption escape attack. Thus, the pirated models, even with some modifications, can be correctly detected through calculating hash distances. Experimental results demonstrate the effectiveness and superiority of our model hashing method with respect to pirated model detection, non-pirated model discrimination and security.