# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 6 Discussion, Issues and Opportunities  
Clearly, the primary goal of watermarking research is to balance the restrictive requirements of imperceptibility, robustness, and embedding capacity. However, it is difficult to maintain a good relationship between those three factors. Most of the methods have improved one property or two but at the expense others. Security and computational complexity are further equally crucial, which increases the difficulty of maintaining a good relationship between all these potential requirements. As a result, several concerns and challenges must be addressed in this field. Image watermarking research was carried out to exploit various embedding domains. As mentioned previously, different domains have their own advantages depending on specific applications. Several approaches have been proposed in the spatial [57; 72], transform [86; 92], and hybrid domains [105; 106] to maintain the trade-off between watermark properties. The spatial domain saves time and improves efficiency, yet it is vulnerable to most types of attacks, while the transform domain offers high robustness and cost compared with spatial domain-based watermarking. In contrast, the hybrid domain came as a solution to fill in the gap between these domains. However, hybrid methods suffer from a high computational cost, which is not suitable for several real-time applications. Encryption algorithms were designed in certain approaches from the perspective of watermark security [82; 103]. However, encryption raises the overall computational cost of the process, and the security analysis of some methods is insufficient and should  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Reference** & **Architecture** & **Goal** & **Embedding Location** & **Cover/Watermark Size** & **Evaluation Metric** & **Advantages and Weaknesses** \\ \hline
[130] & \begin{tabular}{c} Keypoint \\ detection \\ MDResNet \\ \end{tabular}  & \begin{tabular}{c} Solve high-definition image watermarking problems \\ \end{tabular}  & \begin{tabular}{c} Various scale- \\ invariant \\ regions \\ \end{tabular}  & \begin{tabular}{c} \(512\times 512\) px \\ \(512\times 512\) bit \\ \end{tabular}  & \ \begin{tabular}{c} PSNR = 43.68 dB \\ \(512\times 512\) bit \\ \end{tabular}  & \begin{tabular}{c} + Robust to both common signal operations \\ and geometric attacks \\ \(512\times 512\) bit \\ \end{tabular}  \\ \hline
[131] & \begin{tabular}{c} CNNs \\ \end{tabular}  & \begin{tabular}{c} Learn invisible and ro- \\ but watermarking \\ \end{tabular}  & \begin{tabular}{c} Concatenation \\ of the pre- \\ processed \\ over and the pre-processed \\ watermark \\ \end{tabular}  & \begin{tabular}{c} \(128\times 8\) \\ \(8\times 8\) \\ \end{tabular}  & \begin{tabular}{c} \(128\) \\ \(8\times 8\) \\ \end{tabular}  & \ \begin{tabular}{c} + Embedding and extraction without restrictions on the cover and the watermark \\ \(512\times 512\) bit \\ \end{tabular}  & \begin{tabular}{c} + Embedding and extraction without restrictions on the cover and the watermark \\ \(512\times 512\) bit \\ \end{tabular}  \\ \hline
[132] & \begin{tabular}{c} CNNs \\ \end{tabular}  & \begin{tabular}{c} Secure robustness of watermarking \\ \end{tabular}  & \begin{tabular}{c} Host image \\ blocks \\ \end{tabular}  & \begin{tabular}{c} \(512\times 512\) \\ \(64\times 64\) \\ \end{tabular}  & \begin{tabular}{c} \(512\times 64\) \\ \end{tabular}  & \begin{tabular}{c} \(512\) \\ \(64\times 64\) \\ \end{tabular}  & \ \begin{tabular}{c} \(512\) \\ \(64\times 64\) \\ \end{tabular}  & \begin{tabular}{c} \(512\) \\ \(25\times 512\) \\ \end{tabular}   
be further investigated in detail. Likewise, the weights of neural networks are usually randomly initialized, thus, the learning ability of these networks provide higher security performance [118]. Many factors greatly affect the embedding capacity, including robustness, image quality level, and cover image size.