# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 5 Deep Learning-Based Image Watermarking Schemes
### Learning-Based Extraction Techniques
Although, the combination of many techniques improved the robustness and the imperceptibility, it may also slightly raise processing cost. In another paper, [PERSON] et al. [124] proposed a blind image watermarking technique that exploited a deep convolutional encoder-decoder network for extraction purposes. The watermark is inserted into selective wavelet coefficients for image imperceptibility enhancement. Then, the embedding maps, defined as the difference values between wavelet coefficients from different attacking simulations of the watermarked image, are used to train the deep learning model for watermark extraction, wherein the trained model can precisely recover the watermark data from its host image. The predicted watermark bit \(W^{\prime}\) corresponds to the maximum probability of the output \(p\) of the softmax layer in the network as follows:  
\[W^{\prime}=\underset{i}{\mathrm{argmax}}(p) \tag{15}\]  
From the results, this approach achieved a good trade-off between watermark robustness and image imperceptibility. However, encryption techniques are not included; hence, security performance should be investigated. Likewise, in [125], the watermark is concealed in the LWT-Block to maintain a good balance between imperceptibility and robustness. In the extraction phase, the attacked image is received and then transformed using three-level LWT to extract the sub-band LH/LH1/HL2. The extracted HL2 sub-band is converted into a feature set, which is fed to the trained network to generate the watermark array. The predicted values were then transformed into a matrix to create the watermark. To create a feature vector, the watermarked images from a total of 100 datasets used to train the network were attacked with different types of attacks, including noises, filters, cropping, and scaling. In addition, the prime goal of DNN is to identify the changes made by these attacks to enhance the robustness of the scheme. The proposed study shows a good withstanding of several types of both signal-processing attacks and geometric attacks. However, it does not provide sufficient results for robustness improvements against some image-processing attacks, such as speckle noise and salt and pepper noise. Moreover, rotation and translation issues must be investigated. The proposed approach in [126] combines a user-specific watermark and a messenger application-specific watermark to form the source watermark. Furthermore, randomized orthogonal codes are used on messenger-specific watermark for better security, and an optimization approach has also been used to reduce the computational execution times for embedding and extraction processes. In order to achieve better reliability, three copies of the source watermark are used for embedding on the Slantlet domain. A multi-layer backpropagation neural network is used to extract the watermark appropriately and quickly, under the pressure of various attacks. In general, the presented method was tested against a variety of signal-processing attacks and it showed high robustness with significant imperceptibility. However, geometric attacks are not studied in this approach. Moreover, the validity of the scheme for different types of watermarks could be the future work. Lastly, [PERSON] et al. [23] designed a blind extraction (non-embedding) image watermarking framework based on a residual convolution neural network (RCNN) to perform the mapping relationship between the host image and the watermark image. First, the host image is processed using the median filter to improve the robustness and reduce texture information. Then, DCT and SVD were applied to the image sub-blocks to generate the information matrix, which is used as the input of the RCNN. After training, the trained network parameters are securely stored in order to be used in watermark extraction.