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Due to advancements in digital communication over the internet, the sensitive information is prone to various security attacks. This has led to the need for proposing methods to address the security- related issues. Data hiding is a popular approach for information and data security where the main goal is to safely conceal or hide the secret data into some cover medium such as images, video, or audio. In some specific fields like telemedicine, biometrics, intrusion detect system, and military applications, it is required that the cover medium be not changed while retrieving the hidden data. It is because these applications are very information sensitive and even a small change in the information content can have menacing effects. To overcome this issue, Reversible image data hiding comes into the picture. Reversible image data hiding assures that the original cover image is recovered without causing any modification after we extract the secretly concealed data from it. Besides data security, this approach has also found its applications in watermarking. Watermarking consists of embedding some secret information in an image to preserve its copyright. This hidden information can be extracted from the image to prove its ownership. The difference between the usage of reversible data hiding in watermarking and information security lies in the fact that in the former, less amount of data is embedded as compared to the latter. Reversible data hiding (RDH) can be used in order to protect the privacy of patient records. The patient information such as personal details, medical history, reports, etc. are sensitive pieces of information and their protection must be ensured when transmitted over internet. It can be used for covert communication in fields such as military, criminal investigations, or other applications requiring transmission of sensitive information over the internet. Another application is the centralized nature of cloud computing makes sensitive data susceptible to security attacks. In order to protect user-data over cloud, data coloring approach along with cloud watermarking is used. RDH can be employed in transmission of satellite information embedded in the satellite images. This ensures the protection of satellite data from unauthorized access. The major challenges to be addressed in any reversible image data hiding technique are to keep the image distortion low and making the payload carrying capacity high at the same time. Sometimes we are just interested in having a balance between the image distortion and data-carrying capacity. And one main feature of any data hiding method that makes it reversible is obvious i.e. reversibility, which means that after data extraction, the original grayscale cover image must be recoverable and there should not be any loss. There is the various application M. et al. (2020); Panda (2019); Bhardwaj (2020); Pierce et al. (2019); 855 (2018); Shukla et al. (2019) which can be implemented for secure data hiding communication.
Many RDH methods have been introduced that work in the compression domain Fridrich et al. (2001), spatial domain Tian (2003); Li et al. (2013a); Ni et al. (2006); Li et al. (2013b); Kim et al. (2008); Ou et al. (2014); Peng et al. (2014); Sachnev et al. (2009), transform domain Battisti et al. (2010), and encryption domain Huang et al. (2016). In the compression domain, cover-image is first losslessly compressed to make vacant space for the secret message Spatial domain methods directly manipulate the pixel values in a reversible manner hide secret data. In the transform domain, some transformations are applied to the image, and then the embedding is performed on the transformed image. In some fields to protect the original cover image from security compromises, image encryption and data hiding are used together i.e. encryption domain. Among these, spatial domain-based methods are the least complex. The spatial domain methods are based on difference expansion Tian (2003); Kim et al. (2008), histogram shifting Li et al. (2013a); Ni et al. (2006), sorting and prediction Sachnev et al. (2009); Kamstra and Heijmans (2005), prediction error expansion Li et al. (2013b); Ou et al. (2014); Peng et al. (2014); Thodi and Rodriguez (2007), pixel value ordering Li et al. (2013b); Peng et al. (2014) etc.