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The capabilities of text generators have grown with the rapid development ofLarge Language Models (LLM). To prevent potential misuse, the ability to detectwhether texts are produced by LLM has become increasingly important. Severalrelated works have attempted to solve this problem using binary classifiersthat categorize input text as human-written or LLM-generated. However, theseclassifiers have been shown to be unreliable. As impactful decisions could bemade based on the result of the classification, the text source detection needsto be high-quality. To this end, this paper presents DeepTextMark, a deeplearning-based text watermarking method for text source detection. ApplyingWord2Vec and Sentence Encoding for watermark insertion and a transformer-basedclassifier for watermark detection, DeepTextMark achieves blindness,robustness, imperceptibility, and reliability simultaneously. As discussedfurther in the paper, these traits are indispensable for generic text sourcedetection, and the application focus of this paper is on the text generated byLLM. DeepTextMark can be implemented as an "add-on" to existing text generationsystems. That is, the method does not require access or modification to thetext generation technique. Experiments have shown high imperceptibility, highdetection accuracy, enhanced robustness, reliability, and fast running speed ofDeepTextMark.

DeepTextMark: Deep Learning based Text Watermarking for Detection of Large Language Model Generated Text