## 3 Watermarking Approaches
### LDM-Based Watermarking Categorization  
Given the modular architecture of LDMs, several components provide distinct opportunities for embedding watermarks effectively. Below are several approaches that leverage different parts of the LDM architecture for watermark embedding:  
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2. Latent space embedding (Latent): This approach involves modifying the latent space information of LDM's variational autoencoder (VAE)-based latent space to embed a watermark. By manipulating the features within the latent representation, it is possible to incorporate watermark signals in a manner that is highly integrated and resilient to attacks [34]. This method ensures that the watermark becomes intrinsic to the latent features, resulting in a robust watermarking strategy that remains invisible to human observers. 3. Noise removal steps in the diffusion process (Diffusion Process): The iterative nature of the reverse diffusion process in LDMs offers multiple opportunities for embedding watermarks incrementally. By applying watermarking progressively at each noise removal step, the embedded information can achieve redundancy, making it harder to remove during post-processing [24]. This makes the watermark more deeply ingrained in the generated content, improving its resistance to adversarial attacks. 4. Full LDM model optimization (LDM): Another approach is to fine-tune the entire LDM model, ensuring that the watermark is inherently integrated into the outputs generated by the model. By adjusting the overall generative process, the LDM itself learns to produce images that contain embedded watermark signals [35]. This model-level optimization offers a unified watermarking solution that integrates watermarking into every generated image. 5. Decoder parameter manipulation (Decoder): This technique involves modifying the parameters of the VAE decoder within the LDM to embed a watermark. By making targeted adjustments to the decoder parameters, it is possible to introduce watermark information directly during the image reconstruction process [36]. This approach takes advantage of the final stage of image generation, ensuring that the watermark is applied as the latent representation is translated into a complete image. 6. Initial noise modification (Noise): Watermarking can also be embedded by altering the distribution of the initial noise used as input in the diffusion process. By slightly  
Figure 2: Chronological overview of generative image watermarking approaches using latent diffusion models ([22; 25; 26; 27; 28; 29; 30; 31; 32; 33]). changing the characteristics of the initial noise, watermark signals can be subtly incorporated into the generated image [23]. This approach is effective as the watermark is inherently present from the start of the generative process, making it less likely to be fully removed during subsequent steps. Figures 3 and 4 provide visual summaries of these methods. Figure 3 illustrates the structural insertion points for watermarking within the LDM architecture. It provides a simple diagrammatic explanation of where each approach integrates watermarking into the generative model. Figure 4 shows the research trends for each of these approaches, highlighting the progression and preferences in the use of various watermarking techniques within LDMs. In addition to utilizing the inherent structure of LDMs, watermarking techniques can also be categorized based on additional training requirements or the use of fine-tuning. Some approaches require extensive retraining or fine-tuning of the model to integrate watermarking seamlessly into the generation process, while others focus on efficiency and adaptability. For instance, plug-and-play methods using adapters allow for the insertion of watermarking capabilities without modifying the entire model architecture [25]. These adapter-based methods provide a practical and flexible solution, enabling watermarking to be implemented without extensive computational resources or significant alterations to the pre-existing LDM framework.