###### Abstract
In order to accommodate messages, they proposed to substitute some LSBs in the encrypted image. Compared to the techniques described in [3; 4; 6], this method is likely to accommodate payloads that are more than 10 times larger while maintaining an acceptable PSNR close to 40 dB. [PERSON] et al. [7] have suggested to embed message bits according to the binary-block embedding method. This method embeds binary bits in several LSB planes of the original image into its MSB planes, and then encrypts it and hides the secret data inside its LSB planes. After embedding but before encryption, a bit-level scrambling process is employed to ensure resistance against noise and data loss attacks. The maximum ER of 10,000 images in the BOWSBase dataset is 2.2034 bpp, which outperforms many of the existing RDHEI methods. A new RDHEI method was suggested by [PERSON] et al. [8] with higher capacity and error-free data extraction and image decryption based on multi-MSB prediction and Huffman coding. Specifically, based on its predicted value, a tag value of each pixel is obtained and the total data embedding capacity of an image is determined. The advantage of using Huffman coding is to compress the label map to accommodate more space for embedding information. In this method, the obtained average ERs in two well known image datasets, BOSSbase and BOWS-2, reached 3.361 bpp and 3.246 bpp, respectively,  
Figure 1: The RDHEI framework. leading to an increment in terms of the net payload of approximately 1 bpp compared to the previous works. In [9], [PERSON] et al. proposed a RDHEI based on multi-MSB embedding strategy. Through experimentation, they demonstrated that their approach can achieve an average ER of 1.721 bpp for the BOW-2 database. [PERSON] et al. [10] introduced an RDHEI algorithm that incorporates pixel prediction and multi-MSB plane rearrangement. The prediction errors are calculated through the median edge detector (MED) predictor. They are then separated into two components: the sign of the errors represented by one bit plane, and the absolute values of the errors represented by other bit planes. These bit planes are further divided into small blocks of \(4\times 4\) pixels and categorized as uniform or non-uniform blocks. This categorization enables the rearrangement of the blocks to accommodate auxiliary information and additional data. It is important to note that using small blocks of pixels increases the amount of auxiliary information, which, in turn, decreases the ER. A RDHEI with extended parametric binary tree labeling was introduced in [11]. The experimental results demonstrated that the ERs for commonly used datasets, including Bossbase, BOWS-2, and UCID, are 3.2305 bpp, 3.1619 bpp, and 2.8113 bpp, respectively. Additional methods [12; 13; 14; 15; 16; 17; 18; 19; 20] for RDHEI and many others can be found in [21], which is a survey that presents the birth and evolution of RDHEI methods over 12 years. The motivation for our research in RDHEI stems from the critical need to find innovative solutions that strike a balance between data security and the processes of securely hiding and retrieving data within encrypted content. This paper presents a significant improvement in the advancement of a secure and efficient RDHEI technique, which holds substantial potential across various domains, including secure communication, healthcare, and cloud computing. From the exploration of the existing methods for RDHEI, it is evident that various approaches have been developed to strike a balance between data hiding capacity and the preservation of error-free reversibility. Notably, among the methods under consideration, the RRBE approach consistently stands out, showcasing superior performance metrics, particularly in ER and error-free reversibility when compared to VRAE methods as they face significant challenges in achieving a satisfactory EC due to the lack of redundant space in encrypted images.