# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 5 Deep Learning-Based Image Watermarking Schemes
### End-to-End Learning-Based Watermarking Techniques
\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} p{42.7 pt} p{42.7 pt}} \hline \hline
**Reference** & **Architecture** & **Goal** & \multicolumn{3}{c}{**Embedding Location**} & \multicolumn{3}{c}{**Cover/Watermark Size**} & \multicolumn{3}{c}{**Evaluation Metric**} & \multicolumn{1}{p{42.7 pt}}{**Advantages and Weaknesses**} \\ \hline
[124] & Encoder-Decoder & \begin{tabular}{c} Recover robust watermark from the host \\ image \\ \end{tabular} & \begin{tabular}{c} DWT coeffients \\ \end{tabular} & \begin{tabular}{c} 512 \(\times\) 564 \\ \end{tabular} & \begin{tabular}{c} \(\times\) 512 \\ \(64\times 64\) \\ \end{tabular} & \
\begin{tabular}{c} Avg NC NC = 0.99 \\ \(47\times 68\) dB \\ Avg SSIM = 0.99 \\ \end{tabular} &
\begin{tabular}{c} + Withstand image transformations and geometric distortions \\ - High PSNR values \\ - Security performance should be investigated \\ \end{tabular} \\ \hline
[125] & DNN & \begin{tabular}{c} Identify changes made by \\ attacks \\ \end{tabular} & Block \begin{tabular}{c} LWT \\ component \\ \end{tabular} & \begin{tabular}{c} 512 \(\times\) 532 \\ \end{tabular} & \begin{tabular}{c} 512 \(\times\) 512 \\ \(32\times 32\) \\ \end{tabular} & \
\begin{tabular}{c} Avg NC = 0.98 \\ \(44.11\) dB \\ \end{tabular} &
\begin{tabular}{c} + Robust against common image-processing \\ attacks \\ - Needs further improvement in robustness \\ performance \\ \end{tabular} \\ \hline
[23] & R-CNN & \begin{tabular}{c} Map the relationship between the host image and the watermark image \\ \end{tabular} & \begin{tabular}{c} DCT-SVD sub- \\ blocks \\ \end{tabular} & \begin{tabular}{c} 512 \(\times\) 32 \\ \end{tabular} & \begin{tabular}{c} 512 \(\times\) 512 \\ \(32\times 32\) \\ \end{tabular} & \begin{tabular}{c} 512 \(\times\) 512 \\ \(32\times 32\) \\ \end{tabular} & \
\begin{tabular}{c} NC = 0.9930 (salt and pepper 0.5) \\ \end{tabular} &
\begin{tabular}{c} + Outperform the false detection problem \\ + Support multi-type watermark \\ + High security and robustness \\ - Less robust to rotation and cropping \\ - Lack of complexity analysis \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Some studies rely on deep neural networks to find a robust watermarking domain instead of the frequency domain. In [129], an automated framework was presented based on reinforcement learning for robust watermarking. Three stages were considered in this case, namely watermarking embedding, attack simulation, and weight updating. In the embedding stage, the image block is modified by the network until the network correctly detects the message in the image. Applying this process for all the blocks produces the watermarked image. For that purpose, two methods were proposed. The first is based on backpropagation, and the second is based on an auto-encoder designed only for embedding. Furthermore, an attack simulation and stochastic gradient descent (SGD) are used for robust feature extraction and to correctly capture the message from the host image, respectively. Finally, the weight parameters of the detector network can be used for watermark extraction. The proposed learning network optimized the robustness while considering the high quality of the retrieved images. Consequently, it provided good robustness against different attacks. Due to system generalization ability, it could withstand seen and unseen attacks in the training phase. However, the designed network did not provide a watermark solution for high-definition images. In contrast, [PERSON] et al. [130] integrates CNNs with keypoint detection to solve high-definition image watermarking problems. First, various scale-invariant regions in the host image are acquired. Here, the normalized watermark image is fitted in the center of these regions. This manner of insertion ensures locating the watermark even under geometric distortions.