TY - JOUR
T1 - The Latent Seal
T2 - Robust Model Watermarking for Latent Diffusion Model
AU - Zhao, Qin
AU - Liu, Tong
AU - Yuan, Xiaochen
AU - Huang, Guoheng
AU - Gong, Xueyuan
AU - Wang, Wei
N1 - Publisher Copyright:
© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2025.
PY - 2026
Y1 - 2026
N2 - In recent years, the latent diffusion model (LDM) has gained widespread adoption across various industries due to its significant commercial value. However, the content generated by LDM currently lacks sufficient copyright protection, raising serious legal and ethical concerns. To address this issue, model watermarking technologies have been proposed as viable solutions. Nevertheless, traditional watermarking methods typically embed watermarks after the content has been generated, thus limiting their effectiveness. In this paper, we propose a novel watermarking model, named Latent Seal, that embeds watermarks directly during the content generation process. The proposed Latent Seal employs an encoder-decoder architecture, where a latent-space encoder embeds image watermarks within the latentspace during content generation, and a latentspace decoder ensures that the target watermark can only be extracted from watermarked images. Extensive experimental results demonstrate that Latent Seal exhibits outstanding performance in terms of imperceptibility and robustness.
AB - In recent years, the latent diffusion model (LDM) has gained widespread adoption across various industries due to its significant commercial value. However, the content generated by LDM currently lacks sufficient copyright protection, raising serious legal and ethical concerns. To address this issue, model watermarking technologies have been proposed as viable solutions. Nevertheless, traditional watermarking methods typically embed watermarks after the content has been generated, thus limiting their effectiveness. In this paper, we propose a novel watermarking model, named Latent Seal, that embeds watermarks directly during the content generation process. The proposed Latent Seal employs an encoder-decoder architecture, where a latent-space encoder embeds image watermarks within the latentspace during content generation, and a latentspace decoder ensures that the target watermark can only be extracted from watermarked images. Extensive experimental results demonstrate that Latent Seal exhibits outstanding performance in terms of imperceptibility and robustness.
KW - artificial intelligence generated content
KW - copyright protection
KW - generative detection
KW - Latent diffusion model
KW - model watermarking
UR - https://www.scopus.com/pages/publications/105042040722
U2 - 10.1007/s11633-025-1620-y
DO - 10.1007/s11633-025-1620-y
M3 - Article
AN - SCOPUS:105042040722
SN - 2731-538X
JO - Machine Intelligence Research
JF - Machine Intelligence Research
ER -