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.

翻译：视觉生成式AI模型（如DALL-E 3、Stable Diffusion XL、Stable Video Diffusion及Sora）的普及程度日益提升。通过广泛评估，我们发现当前最先进的视觉生成模型能够生成与大型娱乐公司（如索尼、漫威、任天堂）持有的知识产权角色高度相似的内容，这引发了潜在的法律问题。当输入提示中包含角色名称，甚至仅包含对其特征的描述性细节时，此类问题便会发生。为缓解此类知识产权侵权问题，我们还提出了一种防御方法。具体而言，我们开发了一种改进的生成范式，能够识别可能侵权的生成内容，并通过在扩散过程中运用引导技术来防止知识产权侵权。该方法无需重新训练或微调预训练模型，即可识别可能侵犯知识产权的生成内容，并利用扩散过程中的引导方法减轻此类侵权。针对蜘蛛侠、钢铁侠、超人等知名角色知识产权的实验证明了所提防御方法的有效性。我们的数据和代码可访问 https://github.com/ZhentingWang/GAI_IP_Infringement。