DeepHider: A Covert NLP Watermarking Framework Based on Multi-task Learning. (arXiv:2208.04676v3 [cs.CR] UPDATED)

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) …

covert framework nlp task watermarking