# Enhanced Dual Reversible Data Hiding Using Combined Approaches
## 4 Experimental Results
This analysis classifies pixel blocks in an image as either \"Regular\" or \"Singular\" to assess the impact of data hiding on the statistical properties and block patterns of the image. Typically, in the natural state of an image, \"Regular\" blocks make up the majority, while the proportion of \"Singular\" blocks gradually increases as data hiding is performed. RS analysis proceeds in the following steps:  
**(1) Block Generation:** The original image and the marked image are divided into multiple small blocks. Typically, the block size is set to \(n\times n\) pixels. **(2) Applying Masking Function:** A masking function is applied to each block to evaluate changes in the block. The masking function modifies the pixel values of the block in a specific manner. The most commonly used masking functions either increase or decrease the difference between pixel values. For example, if the pixel values of a block are \(P_{1},P_{2},\ldots,P_{n}\), after applying the masking function, the block becomes \(Q_{1},Q_{2},\ldots,Q_{n}\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**5000 bits**} & \multicolumn{3}{c}{**10,000 bits**} & \multicolumn{3}{c}{**20,000 bits**} \\ \cline{2-7}  & **Shadow\({}_{1}\)** & **Shadow\({}_{2}\)** & **Shadow\({}_{1}\)** & **Shadow\({}_{2}\)** & **Shadow\({}_{1}\)** & **Shadow\({}_{2}\)** \\ \hline [PERSON] et al. [15] & 65.63 & 65.6 & 62.55 & 62.54 & 59.56 & 59.55 \\ [PERSON] et al. [16] & 69.72 & 69.67 & 66.72 & 66.69 & 63.71 & 63.7 \\ [PERSON] et al. [18] & 59.35 & 59.31 & 56.33 & 56.36 & 53.34 & 53.31 \\ [PERSON] et al. [20] & 71.90 & 65.96 & 68.93 & 62.91 & 65.93 & 59.87 \\ [PERSON] et al. [23] & 61.25 & 61.36 & 54.96 & 55.11 & 50.39 & 50.47 \\ [PERSON] et al. [25] & 65.33 & 65.38 & 62.32 & 62.49 & 59.31 & 59.43 \\ [PERSON] et al. [26] & 71.25 & 71.33 & 68.25 & 68.31 & 65.35 & 65.42 \\ Proposed Scheme & 78.35 & 81.17 & 75.67 & 78.62 & 72.57 & 75.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative PSNR of each shadow of different schemes with different ER. **(3) Calculating RS Value:** The RS value is calculated using the number of Regular and Singular blocks. This can be defined as follows:  
\[RS=\frac{R_{M}-S_{M}}{R_{M}+S_{M}} \tag{17}\]  
Here, \(R_{M}\) and \(S_{M}\) represent the number of Regular and Singular blocks, respectively, after the masking function is applied. The same calculation is performed when the masking function is applied in the opposite direction. When two masking functions are applied, the RS value can be obtained using the values \(R_{-M}\) and \(S_{-M}\) for each case. **(4) Detecting Data Hiding:** The presence of data hiding is assessed by analyzing the RS value. Generally, if the RS value is close to 0, it can be determined that no data have been hidden in the image. If the RS value deviates significantly, it can be concluded that data hiding has occurred. This method is particularly useful when compared to the statistical characteristics of the original image and can be used to detect the presence and intensity of hidden data in the image. RS analysis can identify traces of data hiding and assess the integrity of the image. The two RS analysis results in Tables 3 and 4 were conducted on two different marked images, \(I_{1}^{\prime}\) and \(I_{2}^{\prime}\). Each experiment measured RS values for various images, such as Boat, Barbara, Tiffany, Pepper, and Goldhill, using different message lengths (20,000 bits and 50,000 bits). This evaluation analyzes the differences and consistency of the RS values by examining the measured values of \(R_{M}\), \(R_{-M}\), \(S_{M}\), and \(S_{-M}\) for each image.