## 4 Experimental Results and Discussion
### Robustness
#### 4.2.1 Adaptive ISF  
Robustness of the proposed scheme using adaptive ISF for the test cover images Figure 10 under zero attacks is presented in Table 1, and the corresponding extracted watermark is shown in Figure 11. For all grayscale images, NC and BER are equal to ideal values 1 and 0, respectively, as observed from Table 1. Whereas, for all color images, average NC is 0.9940, and the average BER is 0.0115. Furthermore, it is observed from Table 2 that NC and BER, for 50 images of different modalities, under zero attacks are also equal to an ideal value. These observations implicate that the watermark is successfully extracted under zero attack. Further robustness performance of the proposed scheme is examined for different cover images (Lena, Baboon, MRI Chest, Tulips) and watermark (Cameraman, Pirate, Circle, Trishool) images under common image processing attacks. Attacked watermarked image and corresponding extracted watermark with NC and BER are presented in Figures 12 and 13, where it can be observed that the proposed scheme successfully extracts the watermark under filtering, geometrical and compression attacks from all cover images. For noise attacks, the proposed scheme extracts watermarks with little distortion. The above sampled consequences indicate that the proposed scheme is robust against the majority of attacks.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Grayscale** & \multirow{2}{*}{**With GA**} & \multicolumn{4}{c}{**With ABC**} & \multicolumn{2}{c}{**With FO**} \\
**Images** & & & & & & & & \\ \hline  & **PSNR (dB)** & **SSIM** & **Alpha** & **PSNR (dB)** & **SSIM** & **Alpha** & **PSNR (dB)** & **SSIM** & **Alpha** \\ \hline Lena & 53.14 & 0.9894 & 9.18692 & 52.32 & 0.9871 & 4.2647 & 52.29 & 0.9869 & 4.47517 \\ Mandrill & 51.96 & 0.9984 & 8.84809 & 51.60 & 0.9983 & 4.2157 & 51.60 & 0.9983 & 4.04169 \\ Lighthouse & 53.44 & 0.9597 & 9.34731 & 52.92 & 0.9570 & 4.1358 & 52.79 & 0.9560 & 3.63865 \\ Desert & 53.34 & 0.9713 & 6.06826 & 53.09 & 0.9707 & 4.2014 & 53.04 & 0.9706 & 3.82451 \\ Grain & 51.52 & 0.9862 & 6.18985 & 51.54 & 0.9866 & 4.0224 & 51.57 & 0.9845 & 5.04537 \\ MRI & 54.27 & 0.9062 & 5.69925 & 54.07 & 0.9059 & 3.8877 & 53.88 & 0.9055 & 2.70833 \\ \hline Average & 52.95 & 0.9685 & & 52.59 & 0.9676 & & 52.52 & 0.9669 & \\ \hline
**Color** & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**SSIM**} & \multirow{2}{*}{**Alpha**} & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**SSIM**} & \multirow{2}{*}{**Apha**} & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**SSIM**} & \multirow{2}{*}{**Alpha**} \\
**Images** & & & & & & & & \\ \hline Koala & 57.11 & 0.9993 & 9.62690 & 56.56 & 0.9991 & 4.7327 & 56.48 & 0.9991 & 4.1267 \\ Penguins & 57.66 & 0.9990 & 9.88692 & 57.21 & 0.9990 & 4.7606 & 57.17 & 0.9990 & 3.9985 \\ Tulips & 61.50 & 1 & 6.67544 & 61.37 & 1 & 5.1535 & 61.35 & 1 & 4.9423 \\ Tiffany & 57.46 & 0.9978 & 9.80044 & 59.22 & 0.9978 & 4.7247 & 59.32 & 0.9972 & 4.2167 \\ Splash & 58.14 & 0.9976 & 5.32084 & 58.12 & 0.9977 & 6.5745 & 58.03 & 0.9976 & 5.9291 \\ Skin & 58.68 & 0.9999 & 9.26637 & 57.81 & 0.9999 & 4.8773 & 57.60 & 0.9999 & \\ \hline Average & 58.42 & 0.9989 & & 58.38 & 0.9989 & & 58.32 & 0.9988 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: PSNR, SSIM using optimized ISF with GA, ABC, FO.