# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 5 Deep Learning-Based Image Watermarking Schemes
### Learning-Based Embedding Techniques
However, the proposed scheme still vulnerable to Gaussian filtering, especially printing and scanning attacks at lower resolutions. With the same motivation, [PERSON] et al. [116] designed a deep convolutional neural network associated with water wave optimization (WWO) and the chaotic fruit fly optimization algorithm (CFOA). The network is composed of three layers, including a fully connected layer, and it uses feature input to find the optimal region for embedding the secret message. In addition, the classifier is trained using the water chaotic fruit fly optimization algorithm (WCFOA), which was developed by integrating the WWO and CFOA. To compute the best solution with the minimal error value, a fitness function is calculated as:  
\[F=\frac{1}{\sigma}\sum_{v=1}^{\sigma}\beta_{p(v)}^{q}-\overline{\omega}_{v} \tag{14}\]  
where, \(\sigma\) represents the total number of samples, \(\overline{\omega}_{v}\) is the estimated output, and \(\beta_{p(v)}^{q}\) represents the output of the classifier. Finally, the watermark is inserted in the wavelet transform sub-bands according to the optimal region and based on the fitness function \(F\), wherein the optimal region helps to effectively increase the embedding performance. At the extraction stage, the cover image is required for extracting the hidden information based on wavelet transform, as the prime objective of the proposed scheme is to achieve a good imperceptibility of the watermarked image. The method provides high values in terms of the correlation coefficient and PSNR. However, working on scheme robustness will increase the scientific value of the present work. Moreover, the computational cost of the scheme must be investigated because various mechanisms have been involved. In contrast, [PERSON] et al. [117] developed an artificial neural network to reduce the computational cost of the embedding process. First, the Y channel of the YCbCr image is divided into blocks.The embedding blocks are then selected in a randomized manner using the Mersenne twister random number generator with a secret key to improve the robustness. Then, the watermark bits are concealed in the IWT-DCT sub-bands via a trained artificial neural network (ANN). Specifically, the selected blocks and four copies of the watermark bit are used to generate 20 values as the input of the ANN, thus modifying the pixel values of a block, while the watermark is extracted by applying IWT on the blocks of the watermarked image followed by using the DCT transform on the LL band. The performance comparison reveals good values in terms of imperceptibility and robustness against different image-processing attacks, as well as fewer values against some of them, including Gaussian noise, JPEG, and median filter. Further, [PERSON] et al. [118] designed a novel learning scheme based on two convolutional neural network (CNN) auto-encoders for non-blind image watermarking. The positive and the negative of the original image are fed to the CNN's auto-encoders, generating two sets of codebook images. These codebook images are permuted and then vectorized. Finally, the watermarked image is obtained from one of the two vectorized codebook images according to the permuted watermark. The use of CNN provides higher security as the weights are initialized randomly, and it learns features of the input to generate two codebook images. In the process of extraction, codebook vectors are generated using the parameters of the trained network in the embedding phase along with keys. The permuted extracted watermark is obtained by computing the Euclidean distance (nearest vector) of the watermarked image from the codebook vectors. Although, the proposed scheme required the original signal and keys for the extraction process, which is not suitable for many real-time applications, the training of the CNN was performed offline, and it is robust against various types of attacks. Finally, the ability of deep learning classification for blind image watermarking is discussed in [119; 120].