高师理科学刊2026，Vol.46Issue(5)：28-34,7.DOI:10.3969/j.issn.1007-9831.2026.05.005
基于卷积神经网络的扩散模型潜在水印嵌入算法
A latent watermark embedding algorithm for diffusion models based on convolutional neural networks
摘要
Abstract
The existing digital watermarking algorithms have problems with weak robustness,insufficient invisibility,or poor generation quality when applied to diffusion models.To address these issues,this paper proposes a latent watermark embedding algorithm for diffusion models based on convolutional neural networks.Firstly,a watermark encoder is design to convert one-dimensional watermark information into two-dimensional features,which can adapt to the diffusion process while ensure stable embedding of the watermark.Subsequently,a pre-trained watermark encoder is obtained by conducting independent training on the frozen image encoder-decoder.Finally,the complete embedding of watermark information is achieved by introducing the dynamic process of forward noise addition and multi-step reverse denoising recovery,as well as constraining the predicted noise.Experimental results demonstrate that the proposed algorithm maintains high extraction accuracy under various common noise attacks,while noise attacks exert negligible impact on the visual quality of generated images.关键词
扩散模型/数字水印/卷积神经网络Key words
diffusion model/digital watermark/convolutional neural networks分类
信息技术与安全科学引用本文复制引用
杨子鑫,李敬有,张光妲..基于卷积神经网络的扩散模型潜在水印嵌入算法[J].高师理科学刊,2026,46(5):28-34,7.基金项目
黑龙江省教育厅基本科研业务专项(145409441) （145409441）
齐齐哈尔大学教育科学研究项目(GJZRZX202410) （GJZRZX202410）
