Evaluating and Mitigating IP Infringement in Visual Generative AI

• URL: http://arxiv.org/abs/2406.04662v1
• Date: Fri, 7 Jun 2024 06:14:18 GMT
• Title: Evaluating and Mitigating IP Infringement in Visual Generative AI
• Authors: Zhenting Wang, Chen Chen, Vikash Sehwag, Minzhou Pan, Lingjuan Lyu,
• Abstract summary: State-of-the-art visual generative models can generate content that bears a striking resemblance to characters protected by intellectual property rights. This happens when the input prompt contains the character's name or even just descriptive details about their characteristics. We develop a revised generation paradigm that can identify potentially infringing generated content and prevent IP infringement.
• Score: 54.24196167576133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Fantastic Copyrighted Beasts and How (Not) to Generate Them [83.77348858322523]
  Copyrighted characters pose a difficult challenge for image generation services. At least one lawsuit has been awarded damages based on the generation of these characters.
  arXiv  Detail & Related papers  (2024-06-20T17:38:16Z)
• Copyright Protection in Generative AI: A Technical Perspective [58.84343394349887]
  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. This work delves into this issue by providing a comprehensive overview of copyright protection from a technical perspective.
  arXiv  Detail & Related papers  (2024-02-04T04:00:33Z)
• The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright Breaches Without Adjusting Finetuning Pipeline [30.80691226540351]
  We formalized the Copyright Infringement Attack on generative AI models and proposed a backdoor attack method, SilentBadDiffusion. Our method strategically embeds connections between pieces of copyrighted information and text references in poisoning data. Our experiments show the stealth and efficacy of the poisoning data.
  arXiv  Detail & Related papers  (2024-01-07T08:37:29Z)
• A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion Models [52.49582606341111]
  Copyright law confers creators the exclusive rights to reproduce, distribute, and monetize their creative works. Recent progress in text-to-image generation has introduced formidable challenges to copyright enforcement. We introduce a novel pipeline that harmonizes CLIP, ChatGPT, and diffusion models to curate a dataset.
  arXiv  Detail & Related papers  (2024-01-04T11:14:01Z)
• CopyScope: Model-level Copyright Infringement Quantification in the Diffusion Workflow [6.6282087165087304]
  Copyright infringement quantification is the primary and challenging step towards AI-generated image copyright traceability. We propose CopyScope, a new framework to quantify the infringement of AI-generated images from the model level.
  arXiv  Detail & Related papers  (2023-10-13T13:08:09Z)
• Warfare:Breaking the Watermark Protection of AI-Generated Content [33.997373647895095]
  A promising solution to achieve this goal is watermarking, which adds unique and imperceptible watermarks on the content for service verification and attribution. We show that an adversary can easily break these watermarking mechanisms. We propose Warfare, a unified methodology to achieve both attacks in a holistic way.
  arXiv  Detail & Related papers  (2023-09-27T06:32:00Z)
• Towards Robust Model Watermark via Reducing Parametric Vulnerability [57.66709830576457]
  backdoor-based ownership verification becomes popular recently, in which the model owner can watermark the model. We propose a mini-max formulation to find these watermark-removed models and recover their watermark behavior. Our method improves the robustness of the model watermarking against parametric changes and numerous watermark-removal attacks.
  arXiv  Detail & Related papers  (2023-09-09T12:46:08Z)
• BAGM: A Backdoor Attack for Manipulating Text-to-Image Generative Models [54.19289900203071]
  The rise in popularity of text-to-image generative artificial intelligence has attracted widespread public interest. We demonstrate that this technology can be attacked to generate content that subtly manipulates its users. We propose a Backdoor Attack on text-to-image Generative Models (BAGM) Our attack is the first to target three popular text-to-image generative models across three stages of the generative process.
  arXiv  Detail & Related papers  (2023-07-31T08:34:24Z)
• Can Copyright be Reduced to Privacy? [23.639303165101385]
  We argue that while algorithmic stability may be perceived as a practical tool to detect copying, such copying does not necessarily constitute copyright infringement. If adopted as a standard for detecting an establishing copyright infringement, algorithmic stability may undermine the intended objectives of copyright law.
  arXiv  Detail & Related papers  (2023-05-24T07:22:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.