DiffuseTrace: A Transparent and Flexible Watermarking Scheme for Latent Diffusion Model

• URL: http://arxiv.org/abs/2405.02696v2
• Date: Fri, 30 May 2025 08:06:29 GMT
• Title: DiffuseTrace: A Transparent and Flexible Watermarking Scheme for Latent Diffusion Model
• Authors: Liangqi Lei, Keke Gai, Jing Yu, Liehuang Zhu,
• Abstract summary: Adding watermarks to generative model outputs is a vital technique employed for copyright tracking and mitigating potential risks.<n>We propose a novel technique called DiffuseTrace that does not rely on fine-tuning of the diffusion model components.<n>Under 8 types of image processing watermark attacks and 3 types of generative watermark attacks, DiffuseTrace maintains watermark detection rate of 99% and attribution accuracy of over 94%.
• Score: 15.982765272033058
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Latent Diffusion Models (LDMs) enable a wide range of applications but raise ethical concerns regarding illegal utilization. Adding watermarks to generative model outputs is a vital technique employed for copyright tracking and mitigating potential risks associated with Artificial Intelligence (AI)-generated contents. However, post-processed watermarking methods are unable to withstand generative watermark attacks and there exists a trade-off between image fidelity and watermark strength. Therefore, we propose a novel technique called DiffuseTrace. DiffuseTrace does not rely on fine-tuning of the diffusion model components. The multi-bit watermark is a embedded into the image space semantically without compromising image quality. The watermark component can be utilized as a plug-in in arbitrary diffusion models. We validate through experiments the effectiveness and flexibility of DiffuseTrace. Under 8 types of image processing watermark attacks and 3 types of generative watermark attacks, DiffuseTrace maintains watermark detection rate of 99% and attribution accuracy of over 94%.

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