## 1 Introduction  
In the modern age of technology, most of our day-to-day activities depend on digital applications and technology has significantly influenced the way we perform tasks. With rapid developments in technology, online platforms have become a medium through which we pay our bills, learn and even work. Along with these platforms, cloud technology has become a major resource that we use to store our data, such as pictures, personal information, and professional files [1, 2]. The activities we perform online result in the generation of large amounts of data every day. This is not just \"any data\" but data that has to be kept safe from exploitation [3, 4]. Even in online social media networks, people increasingly use images to convey information. When images are stored or transmitted through these public networks, sensitive content may get subjected to attack. Thus, protecting data from attack has become an important task [5]. Data hiding is a technique that helps to protect data by embedding data into a cover medium, such as an image. One of the techniques used is watermarking, where the owner's copyright data is embedded into the image [6]. In case of unauthorized access to the watermark image, the embedded data is extracted to prove ownership. Embedded watermark data assists in withstanding various forms of attack. A watermarked image remains useless when the system cannot recover the embedded watermark from the processed image, i.e., most details get destroyed. In fragile watermarking, the embedded watermark serves as a verification code, such as an integrity check [7]. When any part of the watermark-protected image shows discrepancies, those parts are subjected to investigation to determine if they have been tampered with. Watermarking techniques may lead to cover image distortion. Steganography is another widely known data-hiding technique, where the data to be hidden are embedded in a cover medium such that the embedded data are imperceptible to humans. Often the data to be communicated securely are converted into other forms, including encoded or encrypted forms, to make detection of the hidden data detection challenging. The goal of steganography is the secure communication of the secret data and not the cover. This implies that we could adopt any cover to transmit the secret data. The receiver will extract the hidden message from the cover and, during the process, the cover may get distorted, which does not represent a concern from a steganographic point of view [8; 9; 10]. This makes reversible data hiding (RDH) different from steganography. Steganography and steganalysis are two sides of the same coin. Steganalysis for hidden message extraction is quite difficult when the data hider embeds less information or when the image is in encrypted form. Machine-learning-based approaches have recently been explored to enable steganalysis to overcome these challenges [11]. RDH schemes are very useful and can be applied in scenarios such as medical image transmission, where the recipient cannot afford any form of distortion in the cover image. In such cases, after extraction of the hidden data the original cover image needs to be restored. Thus, RDH techniques are widely used in medical image transmission. In addition, RDH schemes are useful for forensic and military applications, satellite image transmission, etc., where even slight distortion of the restored image is unacceptable. RDH can be implemented on a natural image [12; 13; 14; 15] or over an encrypted image [16; 17; 18]. Drawbacks of using natural images as the cover image include:  
1. The cover image can be easily noticeable and, hence, the contents are exposed to unauthorized users [19]. 2. Embedding of secret data on a cover image modifies the actual pixels of the image, which can pose challenges for reversibility [20]. 3. Such schemes cannot be applied to highly sensitive applications, such as medical image transmission, military operations, satellite image transmission, etc. [21].