## 4 Proposed Methodology
But, our system can still extract the watermark image from the generated watermarked image, as NC is close to 1. Figure 7 shows the PSNR (dB) and NC values after the noise attack for the chest X-ray medical image. #### 5.2.2 Filter Attack

Table 3 shows the performance of various filter attacks, like the average filter (AF), median filter (MF), Wiener filter (WF), and low pass Gaussian filter (LPGF), for different groups of images. In Figure 8, for the chest X-ray image, we found the lowest PSNR value (35.3040 dB) after the AF attack. But, still, the NC value is 0.9627, which indicates that the method is capable of removing the watermark from the watermarked image. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Image Type** & **Image Name** & **SN** & **GN** & **SPN** & **PN** \\ \hline Miscellaneous & Pepper & 0.9916 & 0.9911 & 0.9915 & 0.9469 \\ \cline{2-6}  & female & 0.9905 & 0.9909 & 0.9906 & 0.9313 \\ \cline{2-6}  & Baboon & 0.9942 & 0.9924 & 0.9944 & 0.9446 \\ \cline{2-6}  & Lena & 0.9933 & 0.9920 & 0.9929 & 0.9674 \\ \hline Texture & Straw & 0.9912 & 0.9922 & 0.9936 & 0.9643 \\ \cline{2-6}  & Grass & 0.9946 & 0.9955 & 0.9954 & 0.9776 \\ \hline Medical Image & Chest X-ray & 0.9941 & 0.9923 & 0.9951 & 0.9707 \\ \cline{2-6}  & ECG signal & 0.9957 & 0.9927 & 0.9953 & 0.9640 \\ \hline Underwater Image & Fish Species & 0.9944 & 0.9920 & 0.9944 & 0.9744 \\ \cline{2-6}  & Marine Animal & 0.9904 & 0.9908 & 0.9914 & 0.9468 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Robustness Analysis for Noise Attack with Different Groups of Images by NC. Figure 7: PSNR(dB) and NC values after noise attacks for chest X-ray image. #### 5.2.3 Geometric and Blur Attacks

Image blur and geometric attacks like image resize and jpeg compression are highlighted in Table 4. We used the built-in function of Matlab \"imresize\" for image resizing. This means that the \"imresize\" function has been used as B = imresize(A, [rows cols]), which resizes image A to image B so that it has the desired size containing a specified number of rows and cols with the Matlab default cubic interpolation method. In Figure 9, for the chest X-ray image, the resultant PSNR and NC values are shown in place of the image resize, jpeg compression, and image blur attacks. Figure 8: PSNR(dB) and NC values after filter attacks for chest X-ray image. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Image Type** & **Image Name** & **AF** & **MF** & **WF** & **LPGF** \\ \hline \multirow{3}{*}{Miscellaneous} & Pepper & 0.9431 & 0.9789 & 0.9897 & 0.9978 \\  & Female & 0.9576 & 0.9978 & 0.9952 & 0.9993 \\  & Baboon & 0.9512 & 0.8753 & 0.9652 & 0.9963 \\  & Lena & 0.9713 & 0.9803 & 0.9936 & 0.9983 \\ \hline \multirow{3}{*}{Texture} & Straw & 0.9641 & 0.9520 & 0.9732 & 0.9972 \\  & Grass & 0.9245 & 0.9854 & 0.8967 & 0.9963 \\ \hline \multirow{3}{*}{Medical Image} & Chest X-ray & 0.9627 & 0.9942 & 0.9940 & 0.9994 \\  & ECG signal & 0.9233 & 0.8437 & 0.9517 & 0.9954 \\ \hline \multirow{3}{*}{Underwater Image} & Fish Species & 0.9849 & 0.9658 & 0.9942 & 0.9989 \\  & Marine Animal & 0.9939 & 0.9997 & 0.9993 & 0.9999 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Robustness Analysis for Filter Attack with Different Groups of Images by NC. Figure 9: PSNR(dB) and NC values after geometric and blur attacks for chest X-ray image. #### 5.2.4 Hybrid Attacks

We analyzed some hybrid attacks like MF+blur, SPN+WF, WF+GN, SN+MF, and MF+PN in Table 5. In Figure 10, for the chest X-ray image, we found the lowest PSNR (26.7531 dB) for hybrid attack MF+PN. But, still, the method can extract the watermark image successfully, as the NC value is close to 1.