# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 5 Deep Learning-Based Image Watermarking Schemes  
Previously, various conventional image watermarking methods were described in different domains using different embedding and extraction techniques. These approaches rely on understanding many different types of carrier images and need the careful consideration of several factors in each case when implementing the watermarking scheme. At the present, with the growth of multimedia technologies, using images has become easier but more difficult to process. This makes the task of protecting data information more challenging using conventional methods, thus renovation is needed to speed up the development in this area.  
On the other hand, deep learning (DL) is a subset of artificial intelligence based on neural networks to simulate the behavior of the human brain in terms of data processing and decision making [110, 111]. DL improves prediction performance using big data and plentiful computing resources. It is used mostly in the image-processing field to solve difficult problems, such as image classification, semantic and instance segmentation, and object detection. Recently, deep learning-based watermarking methods have received extensive attention and have become the state of the art in the image watermarking field [18]. Instead of using an engineered algorithm, DL has the ability to learn complicated features from images to represent labels automatically as a minimization of some loss function on a training set of data. Convolutional neural networks (CNNs) are the most successful subfield of DL-based image-processing techniques, as they underlie many deep learning architectures of data images [112]. Further, they are able to take advantage of graphics processing units (GPUs) for computation, while many other network architectures cannot. In our case, various watermarking methods have been proposed in the literature related to the powerful ability of deep learning [21], especially CNNs, for data hiding and data extraction, which improved automation and carrying out analytical tasks without the need for human intervention. Thus, they provide satisfactory performance and effectively maintain the trade-off between robustness and image quality.  
In this section, we will explain the impact of deep learning on the development of the image watermarking field. For this purpose, a comprehensive description of recent learning-based methods is explained in detail. The reviewed methods will be separated into three categories: those that use deep learning in embedding, in extraction, and in both embedding and extraction. Figure 9 describes this classification.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference Approaches** & **Objectives** & \begin{tabular}{c} **Cover/** \\ **Watermark** \\ **Size** \\ \end{tabular} & \
\begin{tabular}{c} **Evaluation** \\ **Metric** \\ \end{tabular} &
\begin{tabular}{c} **Advantages and Weaknesses** \\ \end{tabular} \\ \hline
[108] & \begin{tabular}{c} DWT DCT \\ SVD \\ \end{tabular} & \begin{tabular}{c} Robustness against rotation attacks \\ tacks \\ \end{tabular} & \begin{tabular}{c} 512 \(\times\) 512 \\ Multiple \\ sizes \\ \end{tabular} & \
\begin{tabular}{c} 512 \(\times\) 512 \\ Multiple \\ sizes \\ \end{tabular} &
\begin{tabular}{c} + Robust against various types of attacks \\ including rotation attacks \\ - Few attack analytics \\ - Security was not considered in the scheme \\ \end{tabular} \\ \hline
[109] & \begin{tabular}{c} DWT DCT \\ Arnold \\ transform \\ \end{tabular} & \begin{tabular}{c} Improve imperceptibility considering robustness \\ \end{tabular} & \begin{tabular}{c} ARE \\ SSIM \\ 32 \(\times\)32 \\ \end{tabular} & \
\begin{tabular}{c} + High PSNR and SSIM values \\ PSNR NC \\ BER \\ \end{tabular} &
\begin{tabular}{c} + High PSNR and SSIM values \\ - Lower BER values under various attacks \\ - Limited embedding capacity \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of hybrid domain-based image watermarking techniques.