Survey on intellectual property protection for deep learning model - Xinya WANG; Guang HUA; Hao JIANG; Haijian ZHANG (网络与信息安全学报)
With the rapid development of deep learning technology, deep learning models have been widely used in many fields such as image classification and speech recognition.Training a deep learning model relies on a large amount of data and computing power, thus selling the trained model or providing specific services (DLaaS, e.g.) has become a new business.However, the commercial interests of model trainers and the intellectual property rights of model developers may be violated if the model is maliciously stolen.With deep neural network watermarking becoming a new research topic, multimedia copyright protection techniques were used for deep learning model protection.Numerous methods have been proposed in this field and then a comprehensive survey is needed.the existing deep neural network watermarking methods were elaborated and summarized and the future research directions of this field were discussed.The overall framework of neural network watermarking was presented, whereby the basic concepts such as classification model and model backdoor were introduced.Secondly, the existing methods were divided into two types according to the mechanism of watermark embedding, one is to embed the watermark bits into the carrier of internal information of the network, and the other one uses the established backdoor mapping as the watermark.These two existing deep neural network watermarking methods were analyzed and summarized, and attacks to the watermarks were also introduced and discussed.By analyzing the white-box and black-box conditions in watermarking scenario, it comes to the conclusion that the model is difficult to be effectively protected when it is distributed in the white-box manner, and the neural network watermark defenses in the black-box distribution and black-box verification are both worthy for further research.