mlsanityData to 14 Oct 2026

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

arXiv 2403.01152 ↗First posted 2 Mar 2024cs.CL cs.AIcited by 19

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Abstract (arXiv)

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.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 54 of its 91 references that are in the map. First posted on arXiv 2 Mar 2024.

Builds on

54 of 91 references.

References · 54

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38 more
  1. 2023DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Textcited by 210
  2. 2023Can LLM-Generated Misinformation Be Detected?cited by 177
  3. 2020Automatic Detection of Machine Generated Text: A Critical Surveycited by 166
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Citing papers per year: 2024: 6; 2025: 7; 2026: 6.

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  1. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 686
  2. 2024MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Textscited by 14
  3. 2024On the Limitations of Large Language Models (LLMs): False Attributioncited by 13
  4. 2025Personalized Attacks of Social Engineering in Multi-turn Conversations: LLM Agents for Simulation and Detectioncited by 13
  5. 2025Digital Forensics in the Age of Large Language Modelscited by 10
  6. 2024Ontology-Aware RAG for Improved Question-Answering in Cybersecurity Educationcited by 9
  7. 2025I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attributioncited by 9
  8. 2024EMTeC: A Corpus of Eye Movements on Machine-Generated Textscited by 5
  9. 2025PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learningcited by 3
  10. 2025On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?cited by 3
  11. 2024Model Attribution in LLM-Generated Disinformation: A Domain Generalization Approach with Supervised Contrastive Learningcited by 2
  12. 2026Who Wrote the Book? Detecting and Attributing LLM Ghostwriterscited by 2
7 more
  1. 2026Watermarking Should Be Treated as a Monitoring Primitivecited by 2
  2. 2025SKDU at De-Factify 4.0: Natural Language Features for AI-Generated Text-Detectioncited by 1
  3. 2025T-Detect: Tail-Aware Statistical Normalization for Robust Detection of Adversarial Machine-Generated Textcited by 1
  4. 2026Detector-Evasive LLM Paraphrasing via Constrained Policy Optimizationcited by 1
  5. 2026When New Generators Arrive: Lifelong Machine-Generated Text Attribution via Ridge Feature Transfercited by 1
  6. 2026Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shiftcited by 1
  7. 2026Detecting LLM-Assisted Vietnamese Writing via Keystrokes under Behavioral Manipulationcited by 0