Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending
This addresses attribution issues for users and developers of generative models, though it is incremental as it builds on existing watermarking methods.
The paper tackles the problem of unauthorized use in latent diffusion models by introducing TEAWIB, a framework for user attribution via watermark-informed blending, which achieves state-of-the-art performance in perceptual quality and attribution accuracy without degrading image quality.
Rapid advancements in multimodal large language models have enabled the creation of hyper-realistic images from textual descriptions. However, these advancements also raise significant concerns about unauthorized use, which hinders their broader distribution. Traditional watermarking methods often require complex integration or degrade image quality. To address these challenges, we introduce a novel framework Towards Effective user Attribution for latent diffusion models via Watermark-Informed Blending (TEAWIB). TEAWIB incorporates a unique ready-to-use configuration approach that allows seamless integration of user-specific watermarks into generative models. This approach ensures that each user can directly apply a pre-configured set of parameters to the model without altering the original model parameters or compromising image quality. Additionally, noise and augmentation operations are embedded at the pixel level to further secure and stabilize watermarked images. Extensive experiments validate the effectiveness of TEAWIB, showcasing the state-of-the-art performance in perceptual quality and attribution accuracy.