# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 5 Deep Learning-Based Image Watermarking Schemes
### Learning-Based Extraction Techniques  
Instead of using the trained neural network for the embedding process, it can be adopted at the extraction stage, either to completely extract the watermark or to improve extraction results. Various contributions-based learning networks were proposed for this purpose. For instance, [PERSON] et al. [17] presented a novel CNN-based security-guaranteed image watermarking for smart city applications. The main object of using CNN in this case is to classify images according to the method of image generation. First, a gray watermark image was embedded into the DCT-block component. Then, a cooperative neural network  
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called Fast R-CNN is used for watermark extraction, which takes the suspected watermark as input and produces an output based on the recognition process. The proposed Fast R-CNN has improved the processing speed. However, the computation of potential regions continues to experience delays; therefore, the model should be investigated using different image databases. [PERSON] et al. [121] designed a neural network-based watermarking framework to deal with color images under a set of attacks. First, The contourlet transform is chosen to produce the directional coefficients, and the Zenzo edge detector generates the edge of the carrier image. The watermark logo is inserted into the edge of the color image appropriately using a genetic algorithm. Finally, differential and multi-layer perceptron (MLP) is used to extract the logo information. Experiments with different scenarios were carried out to verify the effectiveness of the discussed approach. Accordingly, it shows good performance values to deal with different kinds of attacks, even if the method is less powerful against a few attacks, such as histogram equalization and average filtering. In [122], the authors designed a new image watermarking method based on the combination of DWT, DCT, SVD, and backpropagation neural network (BPNN) for healthcare applications. Third-level DWT, DCT, and SVD are applied on the cover image, and three watermarks are used for embedding including a Lump image watermark, a symptom, and a text watermark. These watermarks are concealed on the DWT sub-band level, which offers better performance in terms of imperceptibility, robustness, and capacity. To improve the security of the scheme, Arnold transforms, lossless arithmetic compression technique, and Hamming error correction code are performed on the three watermarks, respectively, before embedding. After watermarks extraction, BPNN is then applied to the extracted watermarks to remove the noise, therefore, enhancing the robustness. The experimental results indicate that the proposed method is able to resist different image-processing attacks considering robustness, imperceptibility, capacity, and security simultaneously. However, the computational complexity should be investigated due to the combination of many watermarking mechanisms. Further, [PERSON] et al. [123] came up with a hybrid image watermarking based on discrete wavelet transforms (DWT) and singular value decomposition (SVD) using a backpropagation neural network (BPNN). First, the color image is divided into third-level DWT; then, SVD is used to transform both the low-frequency band LL3 and the watermark image. The \(S\) vector of the host image is adopted to conceal the \(S\) vector of the watermark information. Finally, the hidden watermark is extracted using an extraction algorithm. BPNN, in this case, is performed on the extracted watermark to reduce the noise effects so as to improve the robustness of the watermark image. The experimental results showed that the presented watermarking scheme is robust against common signal processing, with good imperceptibility, which was the main objective of this work.