Neural Network Model Protection with Piracy Identification and Tampering Localization Capability
Cheng Xiong, Guorui Feng, Xinran Li, Xinpeng Zhang, Chuan Qin
Abstract
With the rapid development of neural network, a vast number of neural network models have been developed in recent years, which condense numerous manpower and hardware resource. However, the original models are at risk of being pirated by the adversary to obtain illegal profits. On the other hand, malicious tampering on models, such as implanting the vulnerability and backdoor, may cause catastrophic consequences. We propose a model hash generator method to protect neural network models. Detailedly, our model hash sequence is composed of two parts: one is the model piracy identification hash, which is based on the dynamic convolution and a dual-branch network; the other is the model tampering localization hash, which can help the model owner to accurately detect the tampered locations for further recovery. Experimental results demonstrate the effectiveness of the proposed method for neural network model protection.