## 5 Challenges and Open Directions  
### Architectural Innovations and Watermark Integration  
The integration of watermarking modules into the latent diffusion model architecture presents a series of complex challenges. Traditional watermarking methods typically operate in the pixel space, leading to a trade-off between robustness and image quality. The use of latent space for watermark embedding, as discussed in Latent Watermark [37] and Diffustrerace [21], introduces the potential for maintaining high-quality image generation while embedding robust watermarks. However, architectural innovations are required to make these embeddings more efficient without increasing the computational cost of model training. Recent approaches like WMA adapter [25] suggest using plug-and-play modules that allow for flexible integration without retraining, and this approach could pave the way for more dynamic watermarking strategies. Additionally, Tree-Ring [23] proposes a  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Source** & **Model** & **Implementation** & **Links** \\ \hline \multirow{4}{*}{2023} & Neurips & MDM [38] & PyTorch & [[https://github.com/gblhiu/mdm](https://github.com/gblhiu/mdm)]([https://github.com/gblhiu/mdm](https://github.com/gblhiu/mdm)) (23 December 2024) \\  & Neurips & Tree [23] & PyTorch & [[https://github.com/YuxinWenRick/tree-ring-watermark](https://github.com/YuxinWenRick/tree-ring-watermark)]([https://github.com/YuxinWenRick/tree-ring-watermark](https://github.com/YuxinWenRick/tree-ring-watermark)) (23 December 2024) \\  & arXiv & WaterDM [27] & PyTorch & [[https://github.com/yuqing-me/WaterarmKM](https://github.com/yuqing-me/WaterarmKM)]([https://github.com/yuqing-me/WaterarmKM](https://github.com/yuqing-me/WaterarmKM)) (23 December 2024) \\  & ICCV & Stable [36] & PyTorch & [[https://github.com/facebookresearch/stable_signature](https://github.com/facebookresearch/stable_signature)]([https://github.com/facebookresearch/stable_signature](https://github.com/facebookresearch/stable_signature)) (23 December 2024) \\ \hline \multirow{4}{*}{2024} & arXiv & METR [29] & PyTorch & [[https://github.com/deepkv/metr](https://github.com/deepkv/metr)]([https://github.com/deepkv/metr](https://github.com/deepkv/metr)) (23 December 2024) \\  & ECCV & WaDiff [24] & PyTorch & [[https://github.com/rmin2000/WaDiff](https://github.com/rmin2000/WaDiff)]([https://github.com/rmin2000/WaDiff](https://github.com/rmin2000/WaDiff)) (23 December 2024) \\ \cline{1-1}  & CVPR & Gaussian [22] & PyTorch & [[https://github.com/bsmhmmlf/Gaussian-Shading](https://github.com/bsmhmmlf/Gaussian-Shading)]([https://github.com/bsmhmmlf/Gaussian-Shading](https://github.com/bsmhmmlf/Gaussian-Shading)) (23 December 2024) \\ \cline{1-1}  & CVPR & WOUAF [32] & PyTorch & [[https://github.com/kylemin/WOUAF](https://github.com/kylemin/WOUAF)]([https://github.com/kylemin/WOUAF](https://github.com/kylemin/WOUAF)) (23 December 2024) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Collections of open-source implementations for watermarking methods based on LDMs.  
training-free and model-agnostic approach that offers robustness in watermark embedding, which could be valuable in future architectural designs.