## 1 Introduction  
The progression in digital communication supports the exchange of large amounts of data through networks in real time [1]. As a result of this progression, smart health care companies provide remote medical services to patients [2]. These services include transmitting medical data and images of patients to faraway specialists to save time and achieve rapid medical diagnoses [3]. Security and privacy issues appear when these data is transmitted over open communication networks [4]. Different instructions and rules are used to preserve patients' privacy when sending and receiving their medical data for diagnosis or research [5; 6]. Regardless of the several advantages in this, medical data and images may reach unlicensed people, thus threatening privacy. During the COVID-19 pandemic, the need for data security increased [7; 8; 9]. During the transmission of medical images, a watermark is added to the original images. A watermark can be data, an image, or a logo indicating a patient's identity or data about a hospital or clinic. An important point that must be taken into account is that the watermark should not cause significant degradation in the quality of the original image [10]. Watermarking algorithms are vastly used for biomedical images for efficient results. In watermarking, a logo or data are hidden in the cover or original image to detect ownership at any stage [11]. In watermarking, there are two main properties: robustness and invisibility. These should be balanced in order to provide high performance [12]. Nowadays, optimization algorithms are used in different fields, mainly to perform this task efficiently [13; 14; 15; 16; 17; 18]. Optimization algorithms provide the optimal level of embedding of the message in the best regions in the cover image [19]. Figure 1 shows a block diagram of a basic watermarking system. In this figure, the dotted line indicates the possibility of accessing the original data in the decoding process, representing informed watermark detection. In the case of the absence of the original data, such a system is a blind watermark system. Research on digital watermarking has increased dramatically in recent years. As a concept, watermarking was developed around 1990, and several implementations have been described in the literature [20]. Applications, source types (such as images, videos, or audio files), and watermarking methods all play a role in categorizing digital watermarking approaches. Many people favor discrete wavelet transform (DWT)- and discrete cosine transform (DCT)-based approaches because watermarks may be used in both the spatial and frequency domains. In the context of image watermarking, several techniques are ubiquitous. A watermark image and a cover image are used to watermark photographs. The goal is to seamlessly incorporate the watermark into the cover art without anybody being able to tell. Using the DCT, the authors of [21] presented a new approach to digital image watermarking that is resistant to JPEG compression. For stealthy and long-lasting audio watermarking, a different technique using the SVD and DCT was applied in [22]. In [23], the pros and cons of a QIM-based digital watermarking system were weighed. By combining the DWT and DCT, the researchers improved watermarking approaches. An enhanced DCT-DWT approach based on a hybrid intelligent SVD model was suggested in [24]. In [25], one can see an alternative digital watermarking method for spotting and restoring altered photos. DCT (discrete cosine transform) watermarking was the basis for this  
Figure 1: The typical process of watermarking systems. technology. The watermarking of color photographs involved the combination of the DCT, DWT, and singular value decomposition (SVD). Watermarking methods for still images and moving ones may be found in the literature, with many employing the DCT and DWT in some way.