# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 5 Watermarking Analysis and Security Evaluation
### Experimental Results
#### 5.2.2 Robustness Analysis  
Robustness analysis is evaluated by calculating BER and NC. The BER is the number of bit errors divided by the total number of bits of the watermark. It is calculated to measure the similarity between the extracted attacked watermark and the original one. Lower BER expresses high robustness of watermarking against different attacks. The NC is used to indicate the similarity between original and extracted watermark, its value is between [1,\(-\)1]. When the NC= 1 the original and extracted watermarks are absolutely identical. When NC= 0 the original and extracted watermarks are divergent. When NC = \(-\)1 the original and extracted watermarks are completely anti-similar. The watermark should be robust against attacks (the distortions due to attacks should remain minimal). In our experiments, we consider some geometric and non-geometric attacks. These attacks consist of median filtering, salt-and-pepper, average filter, Wiener filtering, cropping, contrast enhancement, scaling, Gaussian filtering, low pass filtering, histogram equalization, noise, rotation, sharpening, and translate attacks. Detailed results of BER and NC in an average for all images are summarized in Table 3.  
We can see from Table 3 that the average values of NC between original and extracted watermarks are close to 1 except in one case, and the average values of BER between the original watermark and the extracted one are close to 0, which shows that the proposed scheme is robust against different processing attacks. To demonstrate the effectiveness of the proposed method, comparisons with other works are presented in Tables 4 and 5. From Table 4, we can see that our method has a better BER value for salt and pepper noise and noise attack (0.01) than the method of [PERSON] et al. [18], while the method of [PERSON] et al. [18] performs well than our method in the case of the cropping left top corner (25%) attack with a BER value equal to 0. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Attacks** & **Average SSIM** & **Average PSNR** \\ \hline Median filtering 2 \(\times\) 2 & 0.9776 & 50.49 \\ Median filtering 3 \(\times\) 3 & 0.9748 & 54.80 \\ Salt and pepper (0.01) & 0.9820 & 50.69 \\ Average filtering 3 \(\times\) 3 & 0.9624 & 55.92 \\ Cropping left top corner & 0.9741 & 52.23 \\ Gaussian filtering 3 \(\times\) 3 & 0.9844 & 50.00 \\ Histogram equalization & 0.9874 & 50.15 \\ Gaussian noise (0.01) & 0.9779 & 53.72 \\ Rotation 1\({}^{\circ}\) & 0.9977 & 52.12 \\ Rotation 5\({}^{\circ}\) & 0.9906 & 49.99 \\ Rotation 10\({}^{\circ}\) & 0.9838 & 55.60 \\ Sharpening & 0.9854 & 57.25 \\ Translate (10) & 0.9782 & 52.85 \\ Blurring & 0.9847 & 57.69 \\ Contrast Enhancement & 0.9999 & 56.96 \\ Scaling & 0.9882 & 52.69 \\ Wiener filtering & 0.9707 & 55.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SSIM and PSNR average values between watermarked and original images in case of attacks. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attacks** & **Proposed Method** & [18] & [40] & [21] & [22] \\ \hline Cropping left top corner 25\% & 0.0575 & 0 & - & 0.0566 & - \\ Noise attack (0.01) & 0.0284 & 0.1418 & - & - & - \\ Salt and pepper noise (0.01) & 0.0333 & 0.4323 & - & 0.0175 & - \\ Sharpering & 0.0047 & - & 0.0180 & 0.0026 & 0 \\ Histogram equalization & 0.0918 & - & 0.0259 & 0.0080 & - \\ Gaussian filter & 0.0102 & - & 0.0117 & - & - \\ Median filtering \(2\times 2\) & 0.0080 & - & 0.0027 & 0.0596 & 0.0383 \\ Wiener filtering & 0.0536 & - & - & 0.0488 & 0 \\ Average filtering & 0.0150 & - & - & 0.0654 & - \\ Gaussian noise (0.0001) & 0.0994 & - & - & 0.0800 & - \\ Gaussian noise (0.01) & 0.0284 & - & - & - & 0 \\ Rotation 1\({}^{\circ}\) & 0.0221 & - & - & 0.0259 & - \\ Rotation 5\({}^{\circ}\) & 0.0235 & - & - & 0.0283 & - \\ Rotation 10\({}^{\circ}\) & 0.0243 & - & - & 0.0330 & 0.0597 \\ Blurring & 0.0847 & - & 0.0738 & - & - \\ Contrast Enhancement & 0.0126 & - & 0.0131 & - & - \\ \hline \hline \end{tabular}
\end{table}