TraceMark-LDM : Authenticatable Watermarking for Latent Diffusion Models via Binary-Guided Rearrangement

Luo, Wenhao and Shen, Zhangyi and Yao, Ye and Ding, Feng and Zhu, Guopu and Meng, Weizhi (2026) TraceMark-LDM : Authenticatable Watermarking for Latent Diffusion Models via Binary-Guided Rearrangement. Expert Systems with Applications, 316: 131831. ISSN 0957-4174

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Abstract

Image generation algorithms are increasingly integral to diverse aspects of human society, driven by their practical applications. However, insufficient oversight in artificial intelligence-generated content (AIGC) can facilitate the spread of malicious content and increase the risk of unauthorized use. Among the diverse range of image generation models, the Latent Diffusion Model (LDM) is currently the most widely used, dominating the majority of the Text-to-Image model market. Currently, most attribution methods for LDMs rely on directly embedding watermarks into the generated images or their intermediate noise, a practice that compromises both the quality and the robustness of the generated content. To address these limitations, we introduce TraceMark-LDM, a novel algorithm that integrates watermarking to attribute generated images while guaranteeing non-destructive performance. Unlike current methods, TraceMark-LDM leverages watermarks as guidance to rearrange random variables sampled from a Gaussian distribution. To mitigate potential deviations caused by inversion errors, the small-magnitude elements are grouped and strategically rearranged. Additionally, we fine-tune the LDM encoder to enhance the robustness of the watermark. Experimental results show that images synthesized using TraceMark-LDM exhibit superior quality and attribution accuracy compared to state-of-the-art (SOTA) techniques. Notably, TraceMark-LDM demonstrates exceptional robustness against various common attack methods, consistently outperforming SOTA methods. Our code is available at https://github.com/luowhDevSpace/TraceMark.