## 4 Experimental Results  
To evaluate the proposed embedding algorithm, experiments were conducted by comparing it with classical RDH embedding algorithms such as DE, Skewed HS [20], and PEE. The performance evaluation was based on PSNR (Peak Signal-to-Noise Ratio) with respect to EC (Embedding Capacity) and the rate of change in PSNR, specifically, the slope of the graph for each interval. The rate of change in PSNR was used to assess the magnitude of image distortion based on the size of the embedded data and to understand the variation in image distortion with respect to the size of the embedded data. For experiments, wide and recognized standard images in the field of steganography including Lena, Airplane, Lake, Peppers, Tiffany, and 100 randomly selected images from Kaggle were used. Firstly, when embedding secret data into the 5 standard images in the proposed method, the relation between Embedding Capacity and PSNR is illustrated by Figure 9.  
Figure 9 illustrates the relation between PSNR and EC after embedding secret data into the five standard images using the proposed method. As observed in Figure 9, when  
Figure 8: The proposed reversible data extracting scheme. Figure 9: The relation between PSNR and EC using the proposed method. utilizing the proposed method, an increase in the size of secret data corresponds to an increase in PSNR. This proportional relation between PSNR and EC is a key characteristic of the proposed method. Figure 10 illustrates the comparison between the proposed method and the conventional embedding algorithm widely used in traditional embedding method. Figure 10 illustrates the results of embedding using four different embedding algorithms, including the proposed method, on four standard images. As observed in Figure 10, traditional embedding algorithms exhibit a downward-sloping graph, indicating that as the size of secret data increases, the PSNR decreases, and image distortion rises. In other words, larger hidden data lead to higher image distortion in traditional methods. However, the embedding algorithm proposed in this paper, among the four algorithms utilized, uniquely presents an upward-sloping graph in Figure 10. This implies that as the size of hidden data increases, image distortion decreases, showcasing a distinctive characteristic of the proposed embedding method. The comparison results with the previous four algorithms based on numerical values are shown in Table 1.  
In Table 1, the rate of change in PSNR across intervals for different payload sizes is illustrated using four different embedding methods. The formula for calculating the rate of change in PSNR is defined Equation (18):  
\[R=(D_{n+1}-D_{n})/2 \tag{18}\]  
\(R\) is the rate of change of PSNR and \(D\) represents the PSNR for each interval. \(n\) is represented as the payload size interval, such as 20,000 and 30,000 and so on. Table 1 illustrates that the proposed method is the only one with a positive value, while all other embedding methods show a negative rate of change in PSNR across intervals based on payload size. In other words, the PSNR value increases as the payload size grows when the proposed method is employed for embedding. In contrast, experimental results show that PSNR values decrease as the payload size increases for previous embedding methods. Figure 10: Comparison between the proposed method and traditional embedding algorithms. As evident from Table 2, it can be observed that the average PSNR also increases as the payload size increases. Additionally, the PSNR rate of change indicates positive values. The proposed method assumes that most of images follow the saliency map, and based on this, the cover image is divided into two images as shown in Figure 3. However, the proposed method works correctly even if the images do not follow the saliency map as seen in Figures 11 and 12. Figure 13 shows the cover image of the Lena image before hiding the secret data, the predicted image by the CNN-based predictor, and the stego-image after hiding the secret data shown in Figure 13d.