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Evaluating and Mitigating IP Infringement in Visual Generative AI

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arxiv 2406.04662 v1 pith:4LE5SNMT submitted 2024-06-07 cs.CV

classification cs.CV
keywords infringementdiffusioncontentgenerativemodelsvisualcharacterdefense
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CLIMP: Contrastive Language-Image Mamba Pretraining

    cs.CV 2026-01 conditional novelty 6.0 of 10

    A fully Mamba-based (VMamba + Mamba LLM) CLIP model matches or beats transformer baselines on retrieval and OOD benchmarks, and natively supports high resolutions and dense captions.

  2. Semantic to Structure: Learning Structural Representations for Infringement Detection

    cs.CV 2025-02 conditional novelty 6.0 of 10

    A contrastive model fine-tuned on diffusion-generated pairs with matching depth maps and rewritten captions detects structural infringement better than DINOv2, MoCoV3, and SSCD on new SIA and SIR benchmarks.

  3. AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models

    cs.LG 2025-08 reject novelty 4.0 of 10

    AMCR combines prompt sanitization, attention-based partial infringement detection, and a similarity-minimizing fine-tuning loss to reduce copyright infringement in text-to-image generation.

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