## 4 Proposed Methodology
In this experiment, different groups of grayscale 512 \(\times\) 512 images are taken as hosts and a grayscale copyright image whose size is equal to the size of LL4 is used as a watermark image. Our watermark image size is equal to the size of LL4. Therefore, it depends on the host image size. Our host image size is 512 \(\times\) 512. Then, our watermark image size will be 64 \(\times\) 64 after performing LL4. Hence, our watermark image can be easily applied to new content without any adjustment. There is no scope to be the same size of host and watermark images. We have used PSNR, the structural similarity index measure (SSIM), and normalized correlation (NC) to assess the watermarking system's effectiveness. The input images are shown in Figure 5, where (a-j) serve as host images, and (k) serves as a watermark image. Here, (a-c) are miscellaneous images and (d-e) are texture images that are taken from the USC (University of Southern California)-SIPI (Signal and Image Processing Institute) image database [32]. Image (f) is taken from the image database [33]. On the other hand, (g) and (h) are medical images that are taken from chest X-ray images (Pneumonia) [34] and the ECG heartbeat categorization dataset [35], respectively. Finally, (i) and (j) are underwater images that are taken from the fish species image data [36] and the Brackish dataset [37], respectively. However, this section analyses the system's imperceptibility, robustness, security, and capacity. At the conclusion of this section, a comparison of the imperceptibility and robustness of our suggested method with existing methods is shown. Figure 4: Proposed method for watermark extraction. ### Imperceptibility Analysis

We have analyzed the imperceptibility of our proposed method for different groups of images by PSNR (dB) and SSIM, which is observed in Table 1. From this Table, we can say that, for different groups of images, the value of PSNR is greater than 48 dB, and the value of SSIM is close to 1. Also, we noticed that for chest X-ray medical images, we found the largest value of PSNR, which is 48.9688 dB. Therefore, we can state that our proposed method ensures improved the imperceptibility for different groups of images. The generated watermarked and extracted watermark images are shown in Figure 6.

### Robustness Analysis

We analyzed noise, filter, geometric, image blur, and hybrid attacks for different groups of images. For all of these images, we found that the values of NC are greater than 0.9, indicating that our method can successfully recover the watermark image. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Image Type** & **Image Name** & **PSNR (dB)** & **SSIM** \\ \hline \multirow{2}{*}{Miscellaneous} & Pepper & 48.9192 & 0.9997 \\ \cline{2-4}  & Female & 48.6165 & 0.9995 \\ \cline{2-4}  & Baboon & 48.7890 & 0.9999 \\ \cline{2-4}  & Lena & 48.6339 & 0.9998 \\ \hline \multirow{2}{*}{Texture} & Straw & 48.8687 & 0.9999 \\ \cline{2-4}  & Grass & 48.8001 & 1.0000 \\ \hline \multirow{2}{*}{Medical Image} & Chest X-ray & 48.9688 & 0.9997 \\ \cline{2-4}  & ECG signal & 48.5490 & 0.9998 \\ \hline \multirow{2}{*}{Underwater Image} & Fish Species & 48.8687 & 0.9997 \\ \cline{2-4}  & Marine Animal & 48.5780 & 0.9993 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Imperceptibility Analysis for Different Groups of Images by PSNR (dB) and SSIM. Figure 5: Input images: (**a**) Pepper; (**b**) Female; (**c**) Baboon; (**d**) Straw; (**e**) Grass; (**f**) Lena; (**g**) Chest X-ray; (**h**) ECG; (**i**) Fish Species; (**j**) Marine Animal; (**k**) Copyright (watermark image). Figure 6: (**a**) Chest X-ray (Host Image); (**b**) Copyright (Watermark Image); (**c**) Watermarked Image; (**d**) Extracted Watermark. #### 5.2.1 Noise Attack

Table 2 analyzes speckle noise (SN), Gaussian noise (GN), SPN, and Poisson noise (PN). For the chest X-ray image, the image is very much distorted by the PN attack, since the watermarked image's PSNR (26.9433 dB) is sufficiently low for visualization.