# A Watermarking Technique for Biomedical Images Using SMQT, Otsu, and Fuzzy C-Means
## 4 Experimental Results
Table 1 illustrates the PSNR values of the proposed model along with existing models after extraction of the watermark that had passed through different attacks such as the Salt and Pepper, Median Attack, Shear Attack, Blur Attack, Contrast Adjustment Attack, and Average Attack or without attack. The PSNR values of the other models are less than the PSNR values of the proposed model, which indicates that the proposed model is superior to others in terms of imperceptibility and distortion. Table 1 illustrates the PSNR values of the watermark image of different models including our proposed model after extracting the watermark from the host image, which clearly indicates that the proposed model has higher PSNR values compared to other models. Table 2 exhibits the BER values of the proposed model along with other models with and without different attacks such as the Median Attack, Shear Attack, Average Attack, Blur Attack, and Gamma Correction attack. BER suggests the bit-wise error rate. Usually, lower values of BER indicate better image quality. As depicted in Table 2, BER values of the other models are much greater than the BER values of the proposed model for different attacks. This indicates that the proposed model is superior to others in terms of robustness and distortion. Table 3 exhibits the values of CC of the proposed model along with other state-of-the-art models with and without different attacks such as the Salt and Pepper, Median Attack, Average Attack, Gaussian Noise Attack, Speckle Attack, and Poison attack. CC values close to 1 suggest that the image has better quality. In Table 3 the CC values of our proposed model are much closer to 1 compared with the other models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attacks** & **DWT** & **PSO-DWT** & **SVD-SWT** & **DCT-DWT** &
\begin{tabular}{c} **Proposed** \\ **Model** \\ \end{tabular} \\ \hline Without Attack & 40.108059 & 32.66059 & 41.957983 & 42.033778 & 42.140669 \\ Salt and Pepper & 44.166558 & 37.125007 & 28.742353 & 10.092619 & 43.127117 \\ Median & 42.585024 & 43.937647 & 31.415702 & 22.23475 & 51.721594 \\ Shear & 34.130048 & 11.020953 & 2.575604 & 19.689004 & 21.623157 \\ Blur & 47.791597 & 31.063986 & 19.923953 & 18.70545 & 50.712338 \\ Contrast Adjustment & 22.060379 & 29.604466 & 58.46861 & 26.743678 & 33.096049 \\ Average & 43.34289 & 30.828611 & 27.850806 & 20.158203 & 49.301436 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Peak signal-to-noise ratio (PSNR) values of different models and the proposed model after extraction of the watermark. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Attacks** & **DWT** & **FCM-DWT** & **PSO-DWT** & **SVD-SWT** & **DCT-DWT** &
\begin{tabular}{c} **Proposed** \\ **Model** \\ \end{tabular} \\ \hline Without Attack & 0.05030 & 0.053559 & 0.424059 & 0.072922 & 0.045898 & 0.037152 \\ Median & 0.05055 & 0.060419 & 0.478366 & 0.082062 & 0.094727 & 0.041833 \\ Shear & 0.55753 & 0.527414 & 0.737429 & 0.698776 & 0.114258 & 0.524626 \\ Average & 0.05598 & 0.075205 & 0.537355 & 0.090286 & 0.112305 & 0.053675 \\ Blur & 0.09947 & 0.099012 & 0.574958 & 0.116898 & 0.125977 & 0.071027 \\ Gamma Correction & 0.08033 & 0.074181 & 0.36595 & 0.086029 & 0.06543 & 0.04863 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Bit error rate (BER) values of different models and the proposed model after extracting the watermark. Table 4 exhibits the values of SSIM of the DWT model, FCM-DWT model, PSO-DWT model, SWT-SVD model, DCT-DWT model, and our proposed model after passing through different attacks such as the Median Attack, Shear attack, Average Attack, Blur Attack, Gamma Correction Attack, and Rotation Attack or without any attacks. SSIM suggests the similarity between the two images (original watermark and extracted watermark image). Greater values of SSIM represent more similarity between two images. As presented in Table 4, the SSIM values of the proposed model are greater than the other models after passing through different attacks.