## 4 Proposed Methodology
\begin{table}
\begin{tabular}{c c c c c} \hline
**Image Type** & **Image Name** & **Resize** & **JPEG Compression** & **Image Blur** \\ \hline \multirow{4}{*}{Miscellaneous} & Pepper & 0.9934 & 0.9923 & 0.9974 \\  & female & 0.9907 & 0.9903 & 0.9978 \\  & Baboon & 0.9948 & 0.9961 & 0.9970 \\  & Lena & 0.9935 & 0.9943 & 0.9988 \\ \hline \multirow{2}{*}{Texture} & Straw & 0.9936 & 0.9939 & 0.9978 \\  & Grass & 0.9943 & 0.9951 & 0.9969 \\ \hline \multirow{2}{*}{Medical Image} & Chest X-ray & 0.9944 & 0.9946 & 0.9981 \\  & ECG signal & 0.9929 & 0.9955 & 0.9952 \\ \hline \multirow{2}{*}{Underwater Image} & Fish Species & 0.9950 & 0.9950 & 0.9991 \\  & Marine Animal & 0.9917 & 0.9921 & 0.9995 \\ \hline \end{tabular}
\end{table} 
Table 4: Robustness Analysis for Geometric (resize and jpeg compression) and Blur Attacks with Different Groups of Images by NC. Figure 10: PSNR(dB) and NC values after hybrid attacks for a chest X-ray image. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Image Type** & **Image Name** & **MF+Blur** & **SPN+WF** & **WF+GN** & **SN+MF** & **MF+PN** \\ \hline Miscellaneous & Pepper & 0.9656 & 0.9834 & 0.9885 & 0.9750 & 0.9623 \\ \cline{2-6}  & Female & 0.9952 & 0.9841 & 0.9934 & 0.9865 & 0.9824 \\ \cline{2-6}  & Baboon & 0.8657 & 0.9549 & 0.9654 & 0.8772 & 0.8907 \\ \cline{2-6}  & Lena & 0.9718 & 0.9805 & 0.9902 & 0.9730 & 0.9554 \\ \hline Texture & Straw & 0.9412 & 0.9657 & 0.9755 & 0.9469 & 0.9371 \\ \cline{2-6}  & Grass & 0.9834 & 0.8602 & 0.8940 & 0.9840 & 0.9820 \\ \hline \multirow{2}{*}{Medical Image} & Chest X-ray & 0.9906 & 0.9851 & 0.9929 & 0.9802 & 0.9638 \\ \cline{2-6}  & ECG signal & 0.8329 & 0.9438 & 0.9493 & 0.8335 & 0.8859 \\ \hline \multirow{2}{*}{Underwater Image} & Fish Species & 0.9589 & 0.9907 & 0.9919 & 0.9627 & 0.9417 \\ \cline{2-6}  & Marine Animal & 0.9995 & 0.9910 & 0.9979 & 0.9905 & 0.9269 \\ \hline \end{tabular}
\end{table} 
Table 5: Robustness Analysis for Hybrid Attacks with Different Groups of Images by NC. ### Security Analysis

We enhanced the security of the watermark image with an Arnold map. The Arnold map is highly secure and sensitive for the iteration number (key), which the sender and receiver keep private. The watermark image cannot be retrieved without the key. From Figure 10(a), It is shown that the right key (32) is required to extract the watermark image. Otherwise, for Figure 10(b), the right watermark image cannot be extracted. For a fake watermark, there is no image correlation between neighboring pixels, which indicates a meaningless image. So, the method we propose is sufficiently secure. ### Capacity Analysis

In the given scenario, the PSNR and SSIM values are constant for increasing bit depths. PSNR is a metric that measures the quality of an image by comparing it to the original image. Higher PSNR values indicate better quality, and typically, a PSNR value above 30 is considered good. In Table 6, for the pepper image, the PSNR value is 47.5334 dB, which suggests a high level of similarity between the watermarked image and the original host image, indicating a good-quality image. SSIM is another metric that quantifies the structural similarity between two images. SSIM values range from \(-1\) to \(1\), where \(1\) indicates a perfect match. The SSIM value of 0.9984 is close to 1, indicating a high level of similarity between the watermarked image and the original host image. ### Comparison with Existing Methods

Table 7 compares the characteristics of our proposed method with existing recent methods. We used different parameters like method, cover image type, cover image size and color, watermark image type, watermark image size and color, PSNR(dB) and SSIM, security techniques, and application. These parameters reflect the characteristics of our proposed method and compare the characteristics of our proposed method with existing recent methods. From this Table, we can say that the value of PSNR (dB) of the watermarked image of our proposed method is greater than the existing methods [18; 21; 22; 23].