# 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
Moreover, the study does not provide comparison results with existing techniques. More recently, [PERSON] et al. [136] designed a document-image watermarking scheme for the screen-shooting process using a deep neural network. This framework is an end-to-end neural framework with an encoder to embed the watermark, a distortion layer to simulate the screen-shooting attacks, and a decoder to extract the watermark. In the training stage, the watermark is expanded by a fully connected layer and reshaped to match the size of the cover image; then, it is concatenated with the cover. The result is fed into the encoder network to generate the watermarked image. Next, the distortion layer takes the watermarked image as input to produce the distorted watermarked image. Finally, the decoder network is applied to extract the watermark. During the training and testing phases, adjustment of the embedding strength was proposed to improve the visual quality of the watermarked image. After training, the output of the encoder is directly used as the input of the decoder. Although the captures were taken with care using a camera holder, a high performance in terms of robustness and image quality under screen-shooting distortions was provided. Table 6 shows some of the watermarking schemes based on learning networks for the both embedding and extraction processes. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Architecture** & **Goal** & \begin{tabular}{c} **Embedding** \\ **Location** \\ \end{tabular}  & \begin{tabular}{c} **Cover/Watermark** \\ **Size** \\ \end{tabular}  & \ \begin{tabular}{c} **Evaluation Metric** \\ \end{tabular}  & \begin{tabular}{c} **Advantages and Weaknesses** \\ \end{tabular}  \\ \hline
[127] & \begin{tabular}{c} Image fusion \\ framework \\ \end{tabular}  & Improve robustness and & \begin{tabular}{c} Concatenation \\ of the host \\ image \\ \end{tabular}  & \begin{tabular}{c} 128 \(\times\) 32 \\ \end{tabular}  & \ \begin{tabular}{c} 128 \(\times\) 32 \\ \end{tabular}  & \begin{tabular}{c} + Good performance against typical attacks \\ + Require only the watermarked image for extraction \\ \end{tabular}  \\ \hline
[128] & \begin{tabular}{c} Generator- \\ discriminator \\ network \\ \end{tabular}  & \begin{tabular}{c} Achieve robustness \\ against rotation and \\ JPEG compression \\ \end{tabular}  & \begin{tabular}{c} Luminosity \\ value of the \\ image \\ \end{tabular}  & \begin{tabular}{c} 4608 \(\times\) 3456 \\ bit \\ \end{tabular}  & \begin{tabular}{c} 4508 \(\times\) 3456 \\ bit \\ \end{tabular}  \& \
\begin{tabular}{c} \begin{tabular}{c} + High robustness against rotation and JPEG \\ compression attacks \\ - Could resist only a limited number of attacks \\ - Execution time must be investigated \\ - Low embedding capacity \\ \end{tabular}  \\ \hline
[129] & \begin{tabular}{c} Backpropagation, \\ Auto- \\ encoders \\ WMnet \\ \end{tabular}  & \begin{tabular}{c} Find the robust domain \\ from attacks \\ \end{tabular}  & \begin{tabular}{c} Image blocks \\ \end{tabular}  & \begin{tabular}{c} 512 \(\times\) 512 \\ bit \\ \end{tabular}  & \begin{tabular}{c} 512 \(\times\) 512 \\ bit \\ \end{tabular}  & \begin{tabular}{c} 24 \\ bit \\ \end{tabular}  & \ \begin{tabular}{c} 512 \(\times\) 512 \\ bit \\ \end{tabular}  & \begin{tabular}{c} 24 \\ bit \\ \end{tabular}  \\ \hline
[129] & \begin{tabular}{c} Backpropagation, \\ Auto- \\ encoders \\ WMnet \\ \end{tabular}  & \begin{tabular}{c} Find the robust domain \\ from attacks \\ \end{tabular}  & \begin{tabular}{c} Image blocks \\ \end{tabular}  & \begin{tabular}{c} 512 \(\times\) 512 \\ bit \\ \end{tabular}  & \begin{tabular}{c} 24 \\ bit \\ \end{tabular}  & \ \begin{tabular}{c} 512 \(\times\) 512 \\ bit \\ \end{tabular}  & \begin{tabular}{c} 24 \\ bit \\ \end{tabular}  \\ \hline
[129] & \begin{tabular}{c} Backpropagation, \\ Auto- \\ encoders \\ WMnet \\ \end{tabular}  & \begin{tabular}{c} Find the robust domain \\ from attacks \\ \end{tabular}  &