Natural language processing (NLP) technology has shown great commercial value
in applications such as sentiment analysis. But NLP models are vulnerable to
the threat of pirated redistribution, damaging the economic interests of model
owners. Digital watermarking technology is an effective means to protect the
intellectual property rights of NLP model. The existing NLP model protection
mainly designs watermarking schemes by improving both security and robustness
purposes, however, the security and robustness of these schemes have the
following problems, respectively: (1) Watermarks are difficult to defend
against fraudulent declaration by adversary and are easily detected and blocked
from verification by human or anomaly detector during the verification process.
(2) The watermarking model cannot meet multiple robustness requirements at the
same time. To solve the above problems, this paper proposes a novel
watermarking framework for NLP model based on the over-parameterization of
depth model and the multi-task learning theory. Specifically, a covert trigger
set is established to realize the perception-free verification of the
watermarking model, and a novel auxiliary network is designed to improve the
robustness and security of the watermarking model. The proposed framework was
evaluated on two benchmark datasets and three mainstream NLP models, and the
results show that the framework can successfully validate model ownership with
100% validation accuracy and advanced robustness and security without
compromising the host model performance.

本研究论文提出了基于深度模型超参数化和多任务学习理论的自然语言处理模型数字水印框架，该框架采用隐藏触发器并设计了一个新的辅助网络来提高数字水印模型的健壮性和安全性，成功验证了模型所有权以及良好的鲁棒性和安全性。