## 3 Watermarking Approaches
### Watermarking Methods
#### 3.2.1 Watermark Embedding Through Latent Space  
One of the significant strengths of latent space watermarking is its adaptability across different models and applications. Since the watermark is embedded in the latent space, it can be seamlessly integrated into various versions of the Stable Diffusion model without the need for extensive retraining or fine-tuning [23]. This plug-and-play characteristic not only simplifies the implementation process but also enhances the practicality of deploying robust watermarking in real-world scenarios. [PERSON] et al. [34] introduce a method for embedding watermarks directly into the latent space during image generation, ensuring invisibility and robustness while maintaining image quality. The framework is adaptable to different versions of Stable Diffusion without performance degradation. Figure 5 provides a comparison of watermarked images. [PERSON] et al. [25] present a lightweight, plug-and-play watermark plugin that enables dynamic watermark embedding without requiring per-watermark fine-tuning. WMA adapter uses a novel contextual adapter structure to efficiently integrate watermark information during image generation, maintaining high image quality while providing robust watermarking. A hybrid fine-tuning strategy further enhances image sharpness and minimizes artifacts, offering strong protection against watermark removal attacks. [PERSON] et al. [37] embed watermarks directly into the latent space, unlike traditional pixel-based approaches. This technique ensures robustness without retraining and avoids degrading image quality by progressively injecting watermarks during forward propagation, making it more resistant to removal and tampering. Embedding watermarks in latent space provides a robust and adaptive solution to watermarking [37]. It ensures  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Embedding Type** & **Requires Training** & **Controllable** & **Task** & **Fine-Tuning** \\ \hline [34] & Latent & O (Adaptor) & Adaptor & Identification & \(\times\) \\ \hline [25] & Latent & O (Adaptor) & Adaptor & Identification & \(\times\) \\ \hline [37] & Latent & O (Adaptor) & Adaptor & Identification & \(\times\) \\ \hline [38] & Diffusion Process & X & Controllable & Verification & \(\times\) \\ \hline [26] & LDM & O (Adaptor) & Prompt, Adaptor & Verification & O \\ \hline [35] & LDM & O & Prompt & Verification & O \\ \hline [31] & LDM & O & Prompt & Verification & O \\ \hline [27] & LDM & O & Prompt & Verification & O \\ \hline [39] & LDM & X & Uncontrollable & Identification & O \\ \hline [24] & LDM & X & Uncontrollable & Identification & \(\times\) \\ \hline [40] & LDM & X & Uncontrollable & Verification & \(\times\) \\ \hline [32] & Dec & X & Uncontrollable & Identification & O (Dec) \\ \hline [28] & Dec & X & Uncontrollable & Verification & O (Dec) \\ \hline [41] & Dec & X & Adaptor & Verification & \(\times\) \\ \hline [29] & Dec, Initial Noise & X & Controllable & Identification & O (Dec) \\ \hline [29] & Initial Noise & X & Controllable & Identification & \(\times\) \\ \hline [30] & Initial Noise & X & Controllable & Identification & \(\times\) \\ \hline [22] & Initial Noise & X & Adaptor & Identification & \(\times\) \\ \hline [23] & Initial Noise & X & Controllable & Verification & \(\times\) \\ \hline [42] & Initial Noise & X & Controllable & Verification & \(\times\) \\ \hline [21] & Initial Noise & X & Uncontrollable & Verification & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of watermarking methods in latent diffusion model. (Dec means LDM Decoder.)that the watermark remains invisible while offering flexibility in embedding strategies, which helps maintain image quality and enhance protection against attacks.