  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.
