# A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
## 1 Introduction
#### Objectives and Distinctiveness of This Survey  
We illustrate the objectives and distinctiveness of our survey by summarizing an overview of the topic concentrations of existing related survey papers in Table 1. Current surveys predominantly orient toward deep learning model architectures, diversified artificial intelligence methodologies, data hiding techniques, and prominent proposals. It should be noted that our review is intricately tailored to encapsulate the synopsis of works germane to deep learning-based image watermarking. Consequently, extended domains, including the watermarking of the deep learning models themselves [13], fall beyond the scope of our discussion.  
In contrast to existing work, our survey focuses on deep learning-based image watermarking and provides a brief yet in-depth analysis, distinguished by three primary advantages. (1) We systematically categorize deep learning-based image watermarking into embedder-extractor, deep networks for feature transformation, and hybrid methods. This categorization, grounded in the distinct roles deep learning assu  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Methods** & **Concentration** \\ \hline [PERSON] and [PERSON] [14] & Summarizing various convolutional neural network model architectures used in deep learning-based image watermarking \\ \hline [PERSON] and [PERSON] [5] & Summarizing watermarking using artificial intelligence, machine learning, and deep learning \\ \hline [PERSON] et al. [15] & Reviewing deep learning-based data hiding, classifying based on capacity, security, and robustness, and outlining three commonly used architectures \\ \hline [PERSON] et al. [16] & Surveying deep learning techniques for data hiding in watermarking and steganography, and categorizing them based on model architectures and noise injection methods \\ \hline [PERSON] et al. [17] & Reviewing the popular deep-learning model-based digital watermarking methods and summarizing/comparing contributions in the literature \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of existing related surveys.  
aspires to offer technical insights and guidance. (2) We study representative methodologies and encapsulate the directions and challenges of research within each specified category, offering a coherent synopsis. (3) We extend our discussion to encompass a detailed exploration of prospective research avenues, delineating emerging frontiers in the domain of deep learning-based image watermarking.  
Through the systematic analysis, critical research direction discussion, and prospective outlooks, one primary objective of our survey is to connect past research, present innovations, and future prospects, potentially propelling the field toward refined methodologies, enhanced effectiveness, and broader applicative horizons. The rest of this paper is structured as follows: Section 2 talks about the relevant preliminaries in conventional image watermarking, Section 3 categorizes the techniques and provides a survey of image watermarking based on deep learning, Section 4 explores potential research avenues for the future, and Section 5 presents our conclusion.