mlsanityData to 7 Oct 2026

Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective

arXiv 2210.10488 ↗First posted 19 Oct 2022cs.CL cs.LGcited by 40

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Abstract (arXiv)

Two interlocking research questions of growing interest and importance in privacy research are Authorship Attribution (AA) and Authorship Obfuscation (AO). Given an artifact, especially a text t in question, an AA solution aims to accurately attribute t to its true author out of many candidate authors while an AO solution aims to modify t to hide its true authorship. Traditionally, the notion of authorship and its accompanying privacy concern is only toward human authors. However, in recent years, due to the explosive advancements in Neural Text Generation (NTG) techniques in NLP, capable of synthesizing human-quality open-ended texts (so-called "neural texts"), one has to now consider authorships by humans, machines, or their combination. Due to the implications and potential threats of neural texts when used maliciously, it has become critical to understand the limitations of traditional AA/AO solutions and develop novel AA/AO solutions in dealing with neural texts. In this survey, therefore, we make a comprehensive review of recent literature on the attribution and obfuscation of neural text authorship from a Data Mining perspective, and share our view on their limitations and promising research directions.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 76 of its 133 references that are in the map. First posted on arXiv 19 Oct 2022.

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76 of 133 references.

References · 76

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 12; 2024: 13; 2025: 10; 2026: 5.

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  1. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  2. 2023GPT-who: An Information Density-based Machine-Generated Text Detectorcited by 38
  3. 2024SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detectioncited by 30
  4. 2024Beemo: Benchmark of Expert-edited Machine-generated Outputscited by 26
  5. 2023A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Textscited by 23
  6. 2024Detecting AI-Generated Text: Factors Influencing Detectability with Current Methodscited by 23
  7. 2023Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?cited by 21
  8. 2023AI-generated text boundary detection with RoFTcited by 20
  9. 2023Who Wrote it and Why? Prompting Large-Language Models for Authorship Verificationcited by 19
  10. 2024Authorship Obfuscation in Multilingual Machine-Generated Text Detectioncited by 19
  11. 2024A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterizationcited by 18
  12. 2023Neural Authorship Attribution: Stylometric Analysis on Large Language Modelscited by 11
28 more
  1. 2023TOPFORMER: Topology-Aware Authorship Attribution of Deepfake Texts with Diverse Writing Stylescited by 10
  2. 2023HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysiscited by 10
  3. 2024Keep It Private: Unsupervised Privatization of Online Textcited by 10
  4. 2025Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectorscited by 9
  5. 2025Masks and Mimicry: Strategic Obfuscation and Impersonation Attacks on Authorship Verificationcited by 7
  6. 2023Algorithmic Ghost in the Research Shell: Large Language Models and Academic Knowledge Creation in Management Researchcited by 6
  7. 2024KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detectioncited by 4
  8. 2025FDLLM: A Dedicated Detector for Black-Box LLMs Fingerprintingcited by 4
  9. 2025When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Textcited by 4
  10. 2025PRISMM-Bench: A Benchmark of Peer-Review Grounded Multimodal Inconsistenciescited by 4
  11. 2023Stacking the Odds: Transformer-Based Ensemble for AI-Generated Text Detectioncited by 3
  12. 2024Lying Blindly: Bypassing ChatGPT's Safeguards to Generate Hard-to-Detect Disinformation Claimscited by 3
  13. 2024Generative AI Policies under the Microscope: How CS Conferences Are Navigating the New Frontier in Scholarly Writingcited by 3
  14. 2024A Survey on Automatic Credibility Assessment Using Textual Credibility Signals in the Era of Large Language Modelscited by 3
  15. 2025Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attributioncited by 3
  16. 2025Unraveling Interwoven Roles of Large Language Models in Authorship Privacy: Obfuscation, Mimicking, and Verificationcited by 2
  17. 2025Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approachescited by 2
  18. 2023UID as a Guiding Metric for Automated Authorship Obfuscationcited by 1
  19. 2024Deep Learning Detection Method for Large Language Models-Generated Scientific Contentcited by 1
  20. 2024Is the Digital Forensics and Incident Response Pipeline Ready for Text-Based Threats in LLM Era?cited by 1
  21. 2025What You Read Isn't What You Hear: Linguistic Sensitivity in Deepfake Speech Detectioncited by 1
  22. 2026Decoding AI Authorship: Can LLMs Truly Mimic Human Style Across Literature and Politics?cited by 1
  23. 2024Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentationcited by 0
  24. 2025Signature vs. Substance: Evaluating the Balance of Adversarial Resistance and Linguistic Quality in Watermarking Large Language Modelscited by 0
  25. 2026Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitutioncited by 0
  26. 2026Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attributioncited by 0
  27. 2026When AI Rewrites, Classifiers Relax: Uncertainty-Aware Sentiment Analysis on Sarcastic and AI-Paraphrased Social Textcited by 0
  28. 2026PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatizationcited by 0