@inproceedings{
ma2024safesd,
title={Safe-{SD}: Safe and Traceable Stable Diffusion with Text Prompt Trigger for Invisible Generative Watermarking},
author={Zhiyuan Ma and Guoli Jia and Biqing Qi and Bowen Zhou},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=KKLB4ZRboY}
}
Recently, stable diffusion (SD) models have typically flourished in the field of image synthesis and personalized editing, with a range of photorealistic and unprecedented images being successfully generated. As a result, widespread interests have been ignited to develop and use various SD-based tools for visual content creations. However, the exposures of AI-created contents on public platforms could raise both legal and ethical risks. In this regard, the traditional methods of adding watermarks to the already generated images (i.e. post-processing) may face a dilemma (e.g., being erased or modified) in terms of copyright protection and content monitoring, since the powerful image inversion and text-to-image editing techniques have been widely explored in SD-based methods. In this work, we propose a $\textbf{Safe}$ and high-traceable $\textbf{S}$table $\textbf{D}$iffusion framework (namely $\textbf{Safe-SD}$) to adaptively implant the graphical watermarks (e.g., QR code) into the imperceptible structure-related pixels during generative diffusion process for supporting text-driven invisible watermarking and detection. Different previous high-cost injection-then-detection training framework, we design a simple and unified architecture, which makes it possible to simultaneously train watermark injection and detection in a single network, greatly improving the efficiency and convenience of use. Moreover, to further support text-driven generative watermarking and deeply explore its robustness and high-traceability, we elaborately design a $\lambda$-sampling and $\lambda$-encryption algorithm to fine-tune a latent diffuser wrapped by a VAE for balancing high-fidelity image synthesis and high-traceable watermark detection. We present our quantitative and qualitative results on two representative datasets LSUN, COCO and FFHQ, demonstrating state-of-the-art performance of Safe-SD and showing it significantly outperforms the previous approaches.
Safe-SD: Safe and Traceable Stable Diffusion with Text Prompt Trigger for Invisible Generative Watermarking
Zhiyuan Ma Guoli Jia Biqing Qi Bowen Zhou