mlsanityData to 7 Oct 2026

Copyright Protection in Generative AI: A Technical Perspective

arXiv 2402.02333 ↗First posted 4 Feb 2024cs.CR cs.CV cs.LGcited by 35

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Abstract (arXiv)

Generative AI has witnessed rapid advancement in recent years, expanding their capabilities to create synthesized content such as text, images, audio, and code. The high fidelity and authenticity of contents generated by these Deep Generative Models (DGMs) have sparked significant copyright concerns. There have been various legal debates on how to effectively safeguard copyrights in DGMs. This work delves into this issue by providing a comprehensive overview of copyright protection from a technical perspective. We examine from two distinct viewpoints: the copyrights pertaining to the source data held by the data owners and those of the generative models maintained by the model builders. For data copyright, we delve into methods data owners can protect their content and DGMs can be utilized without infringing upon these rights. For model copyright, our discussion extends to strategies for preventing model theft and identifying outputs generated by specific models. Finally, we highlight the limitations of existing techniques and identify areas that remain unexplored. Furthermore, we discuss prospective directions for the future of copyright protection, underscoring its importance for the sustainable and ethical development of Generative AI.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 140 of its 177 references that are in the map. First posted on arXiv 4 Feb 2024.

Builds on

140 of 177 references.

References · 140

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  6. 2023GPT-4 Technical Reportcited by 18,132
  7. 2022Training language models to follow instructions with human feedbackcited by 15,712
  8. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  9. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  10. 2020Denoising Diffusion Implicit Modelscited by 9,655
  11. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  12. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
38 more
  1. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  2. 2022Classifier-Free Diffusion Guidancecited by 5,717
  3. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
  4. 2013Auto-Encoding Variational Bayescited by 3,980
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  6. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  7. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  8. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  9. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  10. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
  11. 2014Conditional Generative Adversarial Netscited by 2,152
  12. 2020Extracting Training Data from Large Language Modelscited by 2,144
  13. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  14. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  15. 2022Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answeringcited by 1,881
  16. 2016Adversarial examples in the physical worldcited by 1,762
  17. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,339
  18. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,286
  19. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  20. 2019Machine Unlearningcited by 1,020
  21. 2017Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoningcited by 1,012
  22. 2023StarCoder: may the source be with you!cited by 790
  23. 2022Quantifying Memorization Across Neural Language Modelscited by 755
  24. 2023Extracting Training Data from Diffusion Modelscited by 723
  25. 2021Deduplicating Training Data Makes Language Models Bettercited by 705
  26. 2019Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?cited by 675
  27. 2023A Watermark for Large Language Modelscited by 667
  28. 2023Instruction Tuning for Large Language Models: A Surveycited by 617
  29. 2016Stealing Machine Learning Models via Prediction APIscited by 602
  30. 2017Unsupervised Image-to-Image Translation Networkscited by 602
  31. 2023Erasing Concepts from Diffusion Modelscited by 530
  32. 2022Diffusion Models for Adversarial Purificationcited by 469
  33. 2020In-Domain GAN Inversion for Real Image Editingcited by 420
  34. 2022Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Modelscited by 418
  35. 2023Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defensecited by 401
  36. 2018HiDDeN: Hiding Data With Deep Networkscited by 384
  37. 2023Who's Harry Potter? Approximate Unlearning in LLMscited by 366
  38. 2019Hidden Trigger Backdoor Attackscited by 359

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 16; 2025: 16; 2026: 3.

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  1. 2024A Sanity Check for AI-generated Image Detectioncited by 187
  2. 2024The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)cited by 159
  3. 2024Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Datacited by 52
  4. 2024Machine Unlearning in Generative AI: A Surveycited by 49
  5. 2024SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image Diffusion Modelscited by 26
  6. 2024Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?cited by 15
  7. 2025Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarkingcited by 14
  8. 2024Evaluating and Mitigating IP Infringement in Visual Generative AIcited by 13
  9. 2025A General Framework to Enhance Fine-tuning-based LLM Unlearningcited by 12
  10. 2024Replication in Visual Diffusion Models: A Survey and Outlookcited by 11
  11. 2024Six-CD: Benchmarking Concept Removals for Benign Text-to-image Diffusion Modelscited by 9
  12. 2025SoK: Machine Unlearning for Large Language Modelscited by 9
23 more
  1. 2025DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspectivecited by 8
  2. 2025MIRAGE: Towards AI-Generated Image Detection in the Wildcited by 8
  3. 2025SoK: How Robust is Audio Watermarking in Generative AI models?cited by 7
  4. 2024Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Modelscited by 6
  5. 2024CopyrightMeter: Revisiting Copyright Protection in Text-to-image Modelscited by 6
  6. 2025Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generationcited by 5
  7. 2025CopyJudge: Automated Copyright Infringement Identification and Mitigation in Text-to-Image Diffusion Modelscited by 4
  8. 2024TrustAI at SemEval-2024 Task 8: A Comprehensive Analysis of Multi-domain Machine Generated Text Detection Techniquescited by 3
  9. 2024A Misleading Gallery of Fluid Motion by Generative Artificial Intelligencecited by 3
  10. 2025XAttnMark: Learning Robust Audio Watermarking with Cross-Attentioncited by 3
  11. 2025CoTGuard: Using Chain-of-Thought Triggering for Copyright Protection in Multi-Agent LLM Systemscited by 3
  12. 2025Hey, That's My Data! Token-Only Dataset Inference in Large Language Modelscited by 3
  13. 2025A Statistical Hypothesis Testing Framework for Data Misappropriation Detection in Large Language Modelscited by 2
  14. 2026RealHD: A High-Quality Dataset for Robust Detection of State-of-the-Art AI-Generated Imagescited by 2
  15. 2024Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AIcited by 1
  16. 2026Perception, Verdict, and Evolution: Hindsight-Driven Self-Refining Forensics Agent for AI-Generated Image Detectioncited by 1
  17. 2024EnTruth: Enhancing the Traceability of Unauthorized Dataset Usage in Text-to-image Diffusion Models with Minimal and Robust Alterationscited by 0
  18. 2024CAP: Detecting Unauthorized Data Usage in Generative Models via Prompt Generationcited by 0
  19. 2025Nine Ways to Break Copyright Law and Why Our LLM Won't: A Fair Use Aligned Generation Frameworkcited by 0
  20. 2025Redefining Generalization in Visual Domains: A Two-Axis Framework for Fake Image Detection with FusionDetectcited by 0
  21. 2025Beyond Superficial Forgetting: Thorough Unlearning through Knowledge Density Estimation and Block Re-insertioncited by 0
  22. 2025CryptoTensors: A Light-Weight Large Language Model File Format for Highly-Secure Model Distributioncited by 0
  23. 2026UniMark: Unified Adaptive Multi-bit Watermarking for Autoregressive Image Generatorscited by 0