## 2 Literature Review  
Digital image watermarking has been targeted by many researchers in recent years [27; 28; 29; 30; 31]. As a platform, the Internet of Things (IoT) has enhanced the development of many types of research in the medical field. A medical image algorithm based on IoT was proposed in which the doctor's signature was embedded into different images to provide robustness and security [32]. The drawbacks of this method were its low robustness in the case of geometric attacks and that the capacity of the data payload was not recognized. So, a different optimization method would provide a trade-off between robustness and size. An idea for saving time consumption was presented by using ant colony optimization and the light gradient boosting algorithm. The embedding was performed by using the discrete cosine transform, and the optimizer was used to obtain the optimal embedding parameters [33]. A different deep learning algorithm was used in watermarking systems to allow automation of the process. The available deep learning algorithms did not guarantee robust and blind embedding simultaneously. A new technique based on deep learning was proposed in which the functions of both embedding and extraction were performed by using automation [34]. The use of particle swarm optimization with a watermarking system depended on the discrete wavelet transform and the discrete cosine transform in [35], and this provided efficient results. The results of this method were satisfying compared to those of existing methods. An optimized blind watermarking technique for protecting copyright data used principal component analysis; it operated on a redundant discrete wavelet domain and used an improved gray wolf optimizer [36]. The proposed technique achieved efficient imperceptibility and robustness. Now, neural networks are being used to develop the efficiency of watermarking techniques. An efficient blind image watermarking technique based on thirteen layers of a convolutional neural network was proposed in [37]. The new method increased the image resolution by increasing the invisibility of the watermark image. The trade-off was carried out according to user desire. The results indicate satisfactory robustness in the cases of both geometric and pixel value change attacks. A new protocol for watermarking systems called a buyer and seller protocol has appeared in recent years to ensure security for systems depending on the blockchain technology [38]. A proposed watermarking framework based on a neural network in which the watermark logo was embedded into the edge of the host image was presented in [39]. This method used an optimizer with a genetic algorithm to insert the logo data and used a multilayer perceptron to extract the logo with high performance. Another secure watermarking algorithm used a particle swarm optimizer in which a color logo was embedded into the three primary colors in color images (red, green, and blue) of the original image by using the discrete cosine transform [40]. The three primary colors were arranged according to the fuzzy entropy value, and a particle swarm optimizer determined the performance of the embedding in order to achieve a balance between invisibility and robustness. A hybrid method was used to perform watermarking with the discrete cosine transform, and the discrete wavelet transform was proposed by the authors of [41]. In this method, the watermark was extracted from the original image without using the host image, but this depended on a two-stage neural network. The system achieved better invisibility with high robustness. Another hybrid system aimed to protect copyright data and was based on the discrete cosine transform (DCT), discrete wavelet transform (DWT), singular value decomposition (SVD), and hologram presented in [42]. The steps of this method were as follows: First, the watermark logo was embedded into the three-color components of the required original image.