Robust Multi-bit Natural Language Watermarking through Invariant Features
KiYoon Yoo, Wonhyuk Ahn, Jiho Jang, Nojun Kwak
Abstract
Recent years have witnessed a proliferation of valuable original natural language contents found in subscription-based media outlets, web novel platforms, and outputs of large language models. However, these contents are susceptible to illegal piracy and potential misuse without proper security measures. This calls for a secure watermarking system to guarantee copyright protection through leakage tracing or ownership identification. To effectively combat piracy and protect copyrights, a multi-bit watermarking framework should be able to embed adequate bits of information and extract the watermarks in a robust manner despite possible corruption. In this work, we explore ways to advance both payload and robustness by following a well-known proposition from image watermarking and identify features in natural language that are invariant to minor corruption. Through a systematic analysis of the possible sources of errors, we further propose a corruption-resistant infill model. Our full method improves upon the previous work on robustness by +16.8% point on average on four datasets, three corruption types, and two corruption ratios
BibTeX
@inproceedings{yoo-etal-2023-robust,
title = "Robust Multi-bit Natural Language Watermarking through Invariant Features",
author = "Yoo, KiYoon and
Ahn, Wonhyuk and
Jang, Jiho and
Kwak, Nojun",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.117/",
doi = "10.18653/v1/2023.acl-long.117",
pages = "2092--2115"
}