## 1 Introduction  
The method of surreptitiously inserting and extracting information from a carrier image is known as digital image watermarking. To make a marked image, the data (i.e., watermark) is hidden within a cover image. However, accurate watermark information extraction is only possible for approved recipients. The watermark can take on a variety of shapes depending on the needs of the user, such as some concealed messages for clandestine communication, some random bits for image protection and verification, or electronic signatures [1]. The watermark can be encoded for a variety of reasons, which include the enhancement of perceivable randomness for further security, by using encryption techniques or reversing the effect of noise using error correction codes for watermark integrity under attacks. While a steganographic system focuses primarily on being undetectable by computer analysis as well as human vision, an image watermarking method frequently prioritizes robustness. Therefore, the watermark should still be visible even if the marked image is corrupted or distorted [2]. An ideal picture watermarking system maintains the watermark under a specified class of distortions, without the need for additional methods. The resilient picture watermarking systems, however, frequently extract the watermark roughly under harmful attacks, using a variety of encoding techniques for restoration [3]. Generally speaking, the technique of grouping pixels or patches so that each one matches the object class of the group is known as semantic segmentation of images. The semantic segmentation findings are also inpainting for authorized users and hidden for unauthorized users. Since the 2000s, inpainting tasks for various computer vision applications have been a topic of study. To produce patches, a variety of algorithms based on contouring, as well as texture propagation or texture synthesis, have been presented [4]. Convolutional Neural Networks (CNNs) have recently been offered as a way to combine image structure with background data. The work of inpainting is not essential for the inverse problem. For this to work, a hidden watermark is created as a set of coordinates for vertices of the polygon that surround the object of interest, as well as a caption for this object in order to automate the operation. A compilation of photographs from various spectral bands that together provide a high-resolution grayscale image in near-infrared (NIR) light range is known as a multispectral satellite image. If real objects have discernible boundaries, boundary information can be easily extracted from NIR images. Recognizing the texture of natural objects like fields, forests, lakes, rivers, etc., is, in fact, a more challenging problem [5]. This can be processed using texture analysis, which has seen great advancements since the 1960s and has gone through numerous stages, from statistical to deep learning techniques. Any of the existing approaches can be the best option in a specific situation since texture analysis has too many different functions. When looking at medical applications, the afterimage cannot be recognized by medical professionals as a sign of the deformation brought on by watermark insertion, since it can affect their judgement. As a result, several academics have concentrated on creating lossless watermarking methods for the medical industry. The extraction of the right features from photos is the key to lossless medical watermarking approaches. A verification image that can be used to confirm the image is created using these image attributes and watermarks. This picture is known as the Ownership Share Picture (OSI). The same image attributes are retrieved for image verification, and the Master Share Image (MSI) can be constructed using this feature [6]. The contribution of this research is as follows:  
1. A novel technique in digital watermarking with data hiding, based on segmentation and classification by using deep learning techniques. 2.