DeepHider: A Covert NLP Watermarking Framework Based on Multi-task Learning

Understand

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.