generation process are needed. Such solutions can serve as a crucial means to clarify the origin of generated content, strengthen copyright protection, and prevent illegal use of content [3,4]. In recent years, interest in integrating deep learning techniques into image steganography and watermarking technologies has been growing [5]. As deep learning-based generative models continue to advance, watermarking and steganography technologies have also evolved in new ways through this integration. Timely consolidation of the latest research findings is crucial for the development of image steganography and watermarking algorithms. However, there is still a lack of comprehensive and systematic summaries and characterizations of deep learning-based steganography and watermarking algorithms. Existing review papers primarily address traditional watermarking techniques [6] and deep learning methodologies [7, 8, 9, 10], whereas this paper focuses on the emerging field of watermarking using latent diffusion models and provides a detailed analysis of how it differs from existing research. Traditional watermarking studies have mainly focused on embedding watermarks after image generation or adding watermarks to generated content using frequency domains such as Fourier transform [11,12], DCT [13,14], and DWT [15]. These methods are relatively straightforward to implement but can be vulnerable to various attacks, such as image compression, cropping, and filtering. In addition, previous studies have also explored learning-based watermarking [7,8], where deep learning models are used to embed and extract watermarks through end-to-end training. These approaches often utilize convolutional neural networks (CNNs) to embed watermarks invisibly within an image during post-processing [16, 17, 18, 19]. Learning-based methods have demonstrated improved robustness against certain types of attacks compared to traditional methods, as they can adaptively learn features that are resistant to compression, noise, and other transformations. However, learning-based watermarking still primarily focuses on post-generation embedding, which leaves it vulnerable to removal attacks [20] and may affect the image quality if not carefully optimized. In contrast, watermarking using latent diffusion models offers the potential to overcome these limitations by embedding watermarks in the latent space during the image generation process. This allows for the natural internalization of watermarks within the content generation process, enabling robust watermarking without compromising image quality. This paper provides an in-depth analysis of how watermarking techniques can be integrated into latent diffusion model. Latent diffusion model have greater computational efficiency compared to traditional deep learning-based generative models and have the potential to generate high-quality images [1]. These characteristics offer significant advantages for applying watermarking techniques. For example, by performing watermarking in the latent space, it is possible to maintain the quality of the generated image while ensuring the robustness of the watermark [21]. This plays an important role in preventing the illegal reproduction or misuse of AI-generated content and clarifying the origin of content. We explore the unique challenges and opportunities presented by diffusion model, and how various parts of the diffusion process can be utilized for watermark embedding and tracing. For example, since the latent space of diffusion model deals with intermediate representations of image generation, watermarking in this space provides a high level of security without affecting the visual quality of the generated image [21,22]. This helps to address the problems encountered in traditional post-generation watermarking methods. The remainder of this paper is organized as follows: Section 2 presents the LDM architecture and background information. Section 3 reviews recent approaches to watermark embedding in LDMs.