Evaluating and Mitigating IP Infringement in Visual Generative AI (2406.04662v1)

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.

• The paper demonstrates that visual generative AI models can inadvertently produce IP-infringing content through both name-based and descriptive prompts.
• It employs a black-box methodology to assess model vulnerabilities by testing six major IP-protected characters such as Spider-Man and Batman.
• The study proposes an innovative mitigation strategy that leverages vision-language systems to detect and reformulate outputs to prevent legal infringement.

Evaluating and Mitigating IP Infringement in Visual Generative AI

The paper "Evaluating and Mitigating IP Infringement in Visual Generative AI" presents a rigorous investigation into the intersection of AI advancement and intellectual property (IP) law. With the increasing utilization of visual generative models, such as DALL-E 3, Stable Diffusion XL, and others, the risk of generating IP-infringing content has escalated. This paper addresses the generation of images that closely resemble IP-protected characters, such as those from entertainment giants Sony, Marvel, and Nintendo, highlighting a pressing legal and ethical challenge within AI generated content.

Methodology and Evaluation

The paper first conducts a comprehensive evaluation of existing AI models, pinpointing their vulnerability to generate IP-infringing content. The researchers developed a methodology involving both name-based and description-based prompts to assess the propensity of these models to generate recognizable IP-protected characters without explicit naming. Using black-box settings, where the model parameters remain inaccessible, the researchers managed to trigger IP infringement through cleverly constructed prompt inputs.

The practical implication of this methodology is profound. It underscores the susceptibility of AI models to unauthorized mimicry of proprietary content, which could lead to complex legal disputes if not addressed. Utilizing six prominent IP-protected characters – Spider-Man, Iron Man, Incredible Hulk, Super Mario, Batman, and Superman – the paper illustrates a wide existence of infringement issues across both commercial and open-source AI models, as evidenced by high infringement rates in both name-based and descriptive scenarios.

Proposed Mitigation Strategy

In response to these findings, the paper proposes a proactive detection and guidance-based mitigation strategy. By implementing a revised generation paradigm, the defense mechanism can identify potentially infringing content during the diffusion generation process. Leveraging large vision-LLMs like GPT-4 and GPT-4V, the system analyzes both input prompts and generated images to detect and suppress potential infringements without having to retrain the underlying model.

This method effectively integrates perception capabilities, blocking input prompts that explicitly or implicitly suggest the generation of IP-protected characters. Furthermore, the paper's approach involves regenerating images using guidance techniques to shift them away from potentially infringing motifs, while maintaining alignment with the original, non-infringing aspects of the user’s prompt.

Impact and Future Work

The implications of this research are significant for stakeholders developing and deploying AI systems capable of visual content generation. By addressing the legal challenges head-on and incorporating mitigation strategies at the model generation level, the research offers a pathway to safe deployment of generative models in creative industries.

Future developments could extend this research by refining the detection and mitigation techniques to adapt to evolving IP landscapes and integrating real-time detection systems as part of the AI deployment lifecycle. Further exploration into algorithmic solutions for infringement detection without human evaluators could also make this approach more scalable.

In conclusion, the paper adeptly confronts the nuanced issue of IP rights within the context of AI-generated visual content. Through systematic evaluation and strategic mitigation design, it sets a precedent for responsible AI deployment amid legal complexities, ensuring creativity isn't stifled by the risks of unintentional IP infringement.