mlsanityData to 7 Oct 2026

Evaluating and Mitigating IP Infringement in Visual Generative AI

arXiv 2406.04662 ↗First posted 7 Jun 2024cs.CVcited by 13

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Abstract (arXiv)

The popularity of visual generative AI models like DALL-E 3, Stable Diffusion XL, Stable Video Diffusion, and Sora has been increasing. Through extensive evaluation, we discovered that the state-of-the-art visual generative models can generate content that bears a striking resemblance to characters protected by intellectual property rights held by major entertainment companies (such as Sony, Marvel, and Nintendo), which raises potential legal concerns. This happens when the input prompt contains the character's name or even just descriptive details about their characteristics. To mitigate such IP infringement problems, we also propose a defense method against it. In detail, we develop a revised generation paradigm that can identify potentially infringing generated content and prevent IP infringement by utilizing guidance techniques during the diffusion process. It has the capability to recognize generated content that may be infringing on intellectual property rights, and mitigate such infringement by employing guidance methods throughout the diffusion process without retrain or fine-tune the pretrained models. Experiments on well-known character IPs like Spider-Man, Iron Man, and Superman demonstrate the effectiveness of the proposed defense method. Our data and code can be found at https://github.com/ZhentingWang/GAI_IP_Infringement.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 25 of its 38 references that are in the map. First posted on arXiv 7 Jun 2024.

Builds on

25 of 38 references.

References · 25

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  5. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  6. 2022Classifier-Free Diffusion Guidancecited by 5,717
  7. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  8. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  9. 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,526
  10. 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
  11. 2021Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrievalcited by 1,175
  12. 2023Adversarial Diffusion Distillationcited by 741
13 more
  1. 2023Extracting Training Data from Diffusion Modelscited by 723
  2. 2022Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Modelscited by 418
  3. 2023Poisoning Web-Scale Training Datasets is Practicalcited by 276
  4. 2023Understanding and Mitigating Copying in Diffusion Modelscited by 205
  5. 2023On the Reliability of Watermarks for Large Language Modelscited by 170
  6. 2024Stealing Part of a Production Language Modelcited by 152
  7. 2023On Memorization in Diffusion Modelscited by 122
  8. 2023Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusioncited by 114
  9. 2023A Pathway Towards Responsible AI Generated Contentcited by 38
  10. 2024Copyright Protection in Generative AI: A Technical Perspectivecited by 35
  11. 2024The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright Breaches Without Adjusting Finetuning Pipelinecited by 33
  12. 2023On Copyright Risks of Text-to-Image Diffusion Modelscited by 20
  13. 2023Generative AI and US Intellectual Property Lawcited by 4

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Citing papers per year: 2024: 1; 2025: 9; 2026: 3.

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  1. 2024Copyright-Aware Incentive Scheme for Generative Art Models Using Hierarchical Reinforcement Learning
  2. 2023CopyScope: Model-level Copyright Infringement Quantification in the Diffusion Workflow
  3. 2024A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion Models
  4. 2025Guardians of Generation: Dynamic Inference-Time Copyright Shielding with Adaptive Guidance for AI Image Generation
  5. 2024RLCP: A Reinforcement Learning-based Copyright Protection Method for Text-to-Image Diffusion Model
  6. 2024Not All Similarities Are Created Equal: Leveraging Data-Driven Biases to Inform GenAI Copyright Disputes
  7. 2023Measuring the Success of Diffusion Models at Imitating Human Artists
  8. 2024Disguised Copyright Infringement of Latent Diffusion Models