A Survey of AI-generated Text Forensic Systems: Detection, Attribution,
  and Characterization
Tharindu Kumarage Garima Agrawal Paras Sheth Raha Moraffah Aman Chadha Joshua Garland Huan Liu
We have witnessed lately a rapid proliferation of advanced Large Language
Models (LLMs) capable of generating high-quality text. While these LLMs have
revolutionized text generation across various domains, they also pose
significant risks to the information ecosystem, such as the potential for
generating convincing propaganda, misinformation, and disinformation at scale.
This paper offers a review of AI-generated text forensic systems, an emerging
field addressing the challenges of LLM misuses. We present an overview of the
existing efforts in AI-generated text forensics by introducing a detailed
taxonomy, focusing on three primary pillars: detection, attribution, and
characterization. These pillars enable a practical understanding of
AI-generated text, from identifying AI-generated content (detection),
determining the specific AI model involved (attribution), and grouping the
underlying intents of the text (characterization). Furthermore, we explore
available resources for AI-generated text forensics research and discuss the
evolving challenges and future directions of forensic systems in an AI era.