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

arXiv: https://arxiv.org/abs/2403.01152 · HTML: https://mlsanity.com/paper/2403.01152/

## 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 (most cited first, 30 of 54)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [The Falcon Series of Open Language Models](https://mlsanity.com/paper/2311.16867/) (2023)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [The False Promise of Imitating Proprietary LLMs](https://mlsanity.com/paper/2305.15717/) (2023)
- [Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://mlsanity.com/paper/1911.00650/) (2019)
- [DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2306.05540/) (2023)
- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [Automatic Detection of Machine Generated Text: A Critical Survey](https://mlsanity.com/paper/2011.01314/) (2020)
- [LM vs LM: Detecting Factual Errors via Cross Examination](https://mlsanity.com/paper/2305.13281/) (2023)
- [The Science of Detecting LLM-Generated Texts](https://mlsanity.com/paper/2303.07205/) (2023)
- [M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection](https://mlsanity.com/paper/2305.14902/) (2023)
- [TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation](https://mlsanity.com/paper/2109.13296/) (2021)
- [TweepFake: about Detecting Deepfake Tweets](https://mlsanity.com/paper/2008.00036/) (2020)
- [Real or Fake? Learning to Discriminate Machine from Human Generated Text](https://mlsanity.com/paper/1906.03351/) (2019)
- [Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey](https://mlsanity.com/paper/2311.07914/) (2023)
- [Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers](https://mlsanity.com/paper/2311.09000/) (2023)
- [Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations](https://mlsanity.com/paper/2307.08678/) (2023)
- [A Robust Semantics-based Watermark for Large Language Model against Paraphrasing](https://mlsanity.com/paper/2311.08721/) (2023)
- [OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples](https://mlsanity.com/paper/2307.11729/) (2023)
- [AI model GPT-3 (dis)informs us better than humans](https://mlsanity.com/paper/2301.11924/) (2023)
- [Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks](https://mlsanity.com/paper/2310.10830/) (2023)
- [Stylometric Detection of AI-Generated Text in Twitter Timelines](https://mlsanity.com/paper/2303.03697/) (2023)
- [Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection](https://mlsanity.com/paper/1907.09177/) (2019)

## Built on by (most cited first, 19 of 19)

- [LLM Evaluators Recognize and Favor Their Own Generations](https://mlsanity.com/paper/2404.13076/) (2024)
- [MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Texts](https://mlsanity.com/paper/2406.12549/) (2024)
- [On the Limitations of Large Language Models (LLMs): False Attribution](https://mlsanity.com/paper/2404.04631/) (2024)
- [Personalized Attacks of Social Engineering in Multi-turn Conversations: LLM Agents for Simulation and Detection](https://mlsanity.com/paper/2503.15552/) (2025)
- [Digital Forensics in the Age of Large Language Models](https://mlsanity.com/paper/2504.02963/) (2025)
- [Ontology-Aware RAG for Improved Question-Answering in Cybersecurity Education](https://mlsanity.com/paper/2412.14191/) (2024)
- [I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution](https://mlsanity.com/paper/2506.17323/) (2025)
- [EMTeC: A Corpus of Eye Movements on Machine-Generated Texts](https://mlsanity.com/paper/2408.04289/) (2024)
- [PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learning](https://mlsanity.com/paper/2506.15683/) (2025)
- [On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?](https://mlsanity.com/paper/2510.20810/) (2025)
- [Model Attribution in LLM-Generated Disinformation: A Domain Generalization Approach with Supervised Contrastive Learning](https://mlsanity.com/paper/2407.21264/) (2024)
- [Who Wrote the Book? Detecting and Attributing LLM Ghostwriters](https://mlsanity.com/paper/2603.28054/) (2026)
- [Watermarking Should Be Treated as a Monitoring Primitive](https://mlsanity.com/paper/2605.13095/) (2026)
- [SKDU at De-Factify 4.0: Natural Language Features for AI-Generated Text-Detection](https://mlsanity.com/paper/2503.22338/) (2025)
- [T-Detect: Tail-Aware Statistical Normalization for Robust Detection of Adversarial Machine-Generated Text](https://mlsanity.com/paper/2507.23577/) (2025)
- [Detector-Evasive LLM Paraphrasing via Constrained Policy Optimization](https://mlsanity.com/paper/2606.00392/) (2026)
- [When New Generators Arrive: Lifelong Machine-Generated Text Attribution via Ridge Feature Transfer](https://mlsanity.com/paper/2606.05626/) (2026)
- [Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift](https://mlsanity.com/paper/2606.25152/) (2026)
- [Detecting LLM-Assisted Vietnamese Writing via Keystrokes under Behavioral Manipulation](https://mlsanity.com/paper/2610.07700/) (2026)

## 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.