# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 5 Deep Learning-Based Image Watermarking Schemes
### Learning-Based Embedding Techniques  
The main objective of the embedding stage is to ensure watermark imperceptibility while also taking into account the other requirements, such as robustness and capacity. For this, many neural networks-based approaches have been designed either to conceal the watermark in the host image or to enhance the watermark-embedding system. As an example, [PERSON] et al. [113] proposed a deep learning method to find the appropriate strength factor for watermark embedding in the DWT-DCT domain. In order to calculate the strength factor, the cover image is first fed into the network, which is called R-CNN, to extract masks \(M_{i}\). The maximum importance coefficient \(c_{i}\) is used to compute the strength factor as follows:  
\[M_{E}=\alpha(1-\underset{i}{\max}(c_{i}\times M_{i})),i\in\{1,2,\ldots,k\} \tag{13}\]  
where \(k\) represents the total number of important classes generated by R-CNN and \(\alpha\) is a constant to scale the strength factor \(M_{E}\). In parallel, The host image is split into non-overlapping blocks and the ROI is assigned to all blocks. Then, DWT is applied to these blocks, followed by the DCT, and one bit is embedded for each sub-block. After that, the strength factor is used to maintain the inserted bits in case the host image has been attacked. Finally, DWT and DCT are used to extract hidden watermarks regarding the 15-times redundancy of the watermark bit applied in the embedding phase. The proposed approach is more imperceptible and resistant against image-processing attacks. However, the robustness against various attacks should be investigated. Moreover, a comparison with a single existing method is insufficient. In [114], the improved Arnold transform is executed on the watermark image, and the carrier image is compressed using the backpropagation (BP) neural network, producing an output in the hidden layer. Then, both scrambled watermark and watermark normalization processing are applied to the output of the hidden layer to generate the compressed wa  
Figure 9: Brief diagram of the learning framework in the image watermarking process. The network was trained either to insert the watermark (orange line) or to detect the watermark (sky blue line). The scenario in which both embedding and detection are performed by a trained network is represented by the dark blue line, while input processing and output processing using learning networks are shown by the red line and the green line, respectively. remark image. In Addition, normalization processing tries to reduce the error prediction of the BP neural network, hence improving prediction accuracy. Finally, the compressed watermarked image is further decompressed using the presented network to obtain the watermarked carrier image. For extracting the watermark, The anti-normalization processing of the difference between the original output and the new output of the hidden layer is calculated. Results reveal the feasibility of the proposed algorithm in terms of imperceptibility, complexity, and robustness against conventional and geometrical attacks. Nevertheless, it shows a poor PSNR value that is less than 20 dB for the majority of testing attacks. On the other hand, [PERSON] et al. [115] suggested a watermarking scheme for image documents using fully connected networks (FCN). First, the input image is standardized to enhance robustness against geometric distortions, and the watermark is further encrypted as a security feature. Next, FCN is used to detect the watermarking regions of the image document. Here, constructed hidden patterns are adopted to find the appropriate positions inside these watermarking regions. The pixel values of these patterns are changed to conceal the secret bits based on the corner feature and the edge of document content and according to their neighboring pixels. Experiment analysis has demonstrated a high stability degree of the FCN against document distortions. Moreover, common image watermarking requirements have been satisfied.