DNN Intellectual Property Protection
Mingfu Xue, Jian Wang, Weiqiang Liu
Abstract
Since the training of deep neural networks (DNN) models requires massive training data, time and expensive hardware resources, the trained DNN model is oftentimes regarded as an intellectual property (IP). Recent researches show that DNN is vulnerable to illegal copy, redistribution and abuse. In order to protect DNN from infringement, a number of DNN IP protection solutions have been proposed in recent years. This paper presents a survey on DNN IP protection methods. First, we propose the first taxonomy for DNN IP protection methods in terms of six attributes: scenario, mechanism, capacity, type, function, and target models. Then, we summarize the existing DNN IP protection works with a focus on the challenges they face as well as their ability to provide proactive protection and resist different levels of attacks. After that, the potential attacks on existing methods from the aspects of model modifications, evasion attacks, and active attacks are analyzed, and a systematic evaluation method for DNN IP protection methods with respect to basic functional metrics, attack-resistance metrics, and customized metrics for different application scenarios is given. Finally, future research opportunities and challenges on DNN IP protection are prospected.