mlsanityData to 14 Oct 2026

U Can't Gen This? A Survey of Intellectual Property Protection Methods for Data in Generative AI

arXiv 2406.15386 ↗First posted 22 Apr 2024cs.CY cs.AIcited by 7

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Abstract (arXiv)

Large Generative AI (GAI) models have the unparalleled ability to generate text, images, audio, and other forms of media that are increasingly indistinguishable from human-generated content. As these models often train on publicly available data, including copyrighted materials, art and other creative works, they inadvertently risk violating copyright and misappropriation of intellectual property (IP). Due to the rapid development of generative AI technology and pressing ethical considerations from stakeholders, protective mechanisms and techniques are emerging at a high pace but lack systematisation. In this paper, we study the concerns regarding the intellectual property rights of training data and specifically focus on the properties of generative models that enable misuse leading to potential IP violations. Then we propose a taxonomy that leads to a systematic review of technical solutions for safeguarding the data from intellectual property violations in GAI.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 129 of its 172 references that are in the map. First posted on arXiv 22 Apr 2024.

Builds on

129 of 172 references.

References · 129

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50 most cited of 129 All 129 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 2.

7 papers.

  1. 2025DOGe: Defensive Output Generation for LLM Protection Against Knowledge Distillationcited by 14
  2. 2025First-Place Solution to NeurIPS 2024 Invisible Watermark Removal Challengecited by 9
  3. 2024CopyrightMeter: Revisiting Copyright Protection in Text-to-image Modelscited by 6
  4. 2025SAIF: A Comprehensive Framework for Evaluating the Risks of Generative AI in the Public Sectorcited by 2
  5. 2026RAVEN: Erasing Invisible Watermarks via Novel View Synthesiscited by 2
  6. 2026From Reflection to Repair: A Scoping Review of Dataset Documentation Toolscited by 1
  7. 2024Empirical Perturbation Analysis of Linear System Solvers from a Data Poisoning Perspectivecited by 0