DeepTextMark: A Deep Learning-Driven Text Watermarking Approach for Identifying Large Language Model Generated Text

TL;DR

The paper proposes DeepTextMark, a deep learning–driven text watermarking approach to identify texts generated by large language models. It embeds watermarks using Word2Vec and Sentence Encoding and detects them with a transformer-based classifier, aiming for blindness, robustness, imperceptibility, and reliability. Crucially, the method acts as an add-on that requires no access to or alteration of the underlying text generation mechanism. Experimental results purportedly show high imperceptibility, strong detection accuracy, robust performance, and fast execution, supporting practical deployment for universal text source detection.

Abstract

The rapid advancement of Large Language Models (LLMs) has significantly enhanced the capabilities of text generators. With the potential for misuse escalating, the importance of discerning whether texts are human-authored or generated by LLMs has become paramount. Several preceding studies have ventured to address this challenge by employing binary classifiers to differentiate between human-written and LLM-generated text. Nevertheless, the reliability of these classifiers has been subject to question. Given that consequential decisions may hinge on the outcome of such classification, it is imperative that text source detection is of high caliber. In light of this, the present paper introduces DeepTextMark, a deep learning-driven text watermarking methodology devised for text source identification. By leveraging Word2Vec and Sentence Encoding for watermark insertion, alongside a transformer-based classifier for watermark detection, DeepTextMark epitomizes a blend of blindness, robustness, imperceptibility, and reliability. As elaborated within the paper, these attributes are crucial for universal text source detection, with a particular emphasis in this paper on text produced by LLMs. DeepTextMark offers a viable "add-on" solution to prevailing text generation frameworks, requiring no direct access or alterations to the underlying text generation mechanism. Experimental evaluations underscore the high imperceptibility, elevated detection accuracy, augmented robustness, reliability, and swift execution of DeepTextMark.

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• Figure 1: Sample figure caption.