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A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterization

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arxiv 2403.01152 v1 pith:FZOIES22 submitted 2024-03-02 cs.CL cs.AI

Tharindu Kumarage , Garima Agrawal , Paras Sheth , Raha Moraffah , Aman Chadha , Joshua Garland , Huan Liu This is my paper · ORCID
classification cs.CLcs.AI
keywords textai-generatedattributioncharacterizationdetectionforensicsystemschallenges
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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.

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Cited by 2 Pith papers

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  1. I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A fine-tuned encoder-only CodeT5 model attributes LLM-generated C code to its source model with up to 97.56% binary and 95.40% five-class accuracy on a new 32,000-program benchmark.

  2. PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learning

    cs.CL 2025-06 conditional novelty 6.0 of 10

    PhantomHunter detects text from privately fine-tuned LLMs by learning shared token-probability traits within LLaMA, Gemma and Mistral families, reporting F1 above 96% on held-out derivatives.

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