A Training-Free Plug-and-Play Watermark Framework for Stable Diffusion

• URL: http://arxiv.org/abs/2404.05607v1
• Date: Mon, 8 Apr 2024 15:29:46 GMT
• Title: A Training-Free Plug-and-Play Watermark Framework for Stable Diffusion
• Authors: Guokai Zhang, Lanjun Wang, Yuting Su, An-An Liu,
• Abstract summary: Existing approaches involve training components or entire SDs to embed a watermark in generated images for traceability and responsibility attribution. In the era of AI-generated content (AIGC), the rapid iteration of SDs renders retraining with watermark models costly. We propose a training-free plug-and-play watermark framework for SDs.
• Score: 47.97443554073836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, the family of Stable Diffusion (SD) models has gained prominence for its high quality outputs and scalability. This has also raised security concerns on social media, as malicious users can create and disseminate harmful content. Existing approaches involve training components or entire SDs to embed a watermark in generated images for traceability and responsibility attribution. However, in the era of AI-generated content (AIGC), the rapid iteration of SDs renders retraining with watermark models costly. To address this, we propose a training-free plug-and-play watermark framework for SDs. Without modifying any components of SDs, we embed diverse watermarks in the latent space, adapting to the denoising process. Our experimental findings reveal that our method effectively harmonizes image quality and watermark invisibility. Furthermore, it performs robustly under various attacks. We also have validated that our method is generalized to multiple versions of SDs, even without retraining the watermark model.

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