# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 5 Watermarking Analysis and Security Evaluation
### Experimental Results
#### 5.2.2 Robustness Analysis

Table 4: Comparison of the average BER value of the proposed method with [18; 21; 22; 40]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Attacks** & **Average BER** & **Average NC** \\ \hline Median filtering \(2\times 2\) & 0.0080 & 0.9494 \\ Median filtering \(3\times 3\) & 0.0214 & 0.9427 \\ Salt and pepper (0.01) & 0.0333 & 0.9099 \\ Average filtering \(3\times 3\) & 0.0203 & 0.9407 \\ Cropping left top corner & 0.0575 & 0.8445 \\ Gaussian filtering \(3\times 3\) & 0.0172 & 0.9488 \\ Histogram equalization & 0.0918 & 0.4929 \\ Gaussian noise (0.01) & 0.0284 & 0.8980 \\ Rotation 1\({}^{\circ}\) & 0.0221 & 0.9881 \\ Rotation 5\({}^{\circ}\) & 0.0235 & 0.9260 \\ Rotation 10\({}^{\circ}\) & 0.0243 & 0.9226 \\ Sharpening & 0.0047 & 0.9852 \\ Translate (10) & 0.0202 & 0.9287 \\ Blurring & 0.0847 & 0.9904 \\ Contrast Enhancement & 0.0126 & 0.9530 \\ Scaling & 0.0112 & 0.9558 \\ Wiener filtering & 0.0536 & 0.9819 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: NC and BER average values between the original and extracted attacked watermarks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attacks** & **Proposed Method** & [18] & [40] & [21] & [41] \\ \hline Cropping left top corner 25\% & 0.8445 & 0.9997 & - & 1 & 0.9966 \\ Noise attack (0.01) & 0.9114 & 0.9589 & - & - & - \\ Salt and pepper noise (0.01) & 0.9099 & 0.9589 & - & - & 0.9758 \\ Sharpening & 0.9852 & - & 0.9018 & 0.9977 & 0.8898 \\ Histogram equalization & 0.4929 & - & 0.8556 & 0.9921 & 0.6038 \\ Gaussian filter & 0.9488 & - & 0.9322 & - & - \\ Median filtering \(2\times 2\) & 0.9494 & - & - & - & 0.6973 \\ Median filtering \(3\times 3\) & 0.9427 & - & 0.9845 & 0.9430 & - \\ Wiener filtering & 0.9819 & - & - & 0.9539 & - \\ Average filtering & 0.9407 Comparing our method with that of [PERSON] et al. [40], one can see that our method is more robust in the case of sharpening, Gaussian filter, and contrast enhancement attacks. The results show that our method performs well for these three attacks as BER is close to 0. However, when we consider the histogram equalization attack, the method of [PERSON] et al. [40] has a better BER value than ours. The method of [PERSON] et al. [21] is more robust than ours in the case of the cropping left top corner (25%), salt and pepper noise (0.01), sharpening, histogram equalization, Wiener filtering, and Gaussian noise (0.0001), but it is less robust than our method for the other attacks. The method of [PERSON] et al. [22] has been tested for only sharpening, median filtering \(2\times 2\), Wiener filtering, Gaussian noise (0.01), and rotation (10\({}^{\circ}\)). The average BER values of sharpening, Wiener filtering, and Gaussian noise are equal to 0. Therefore, this method is very robust and performs well with these three attacks while in the case of median filtering \(2\times 2\) our method is more robust. A comparison of the proposed technique with [18; 21; 40; 41] for average NC values is shown in Table 5. The comparison of the results with [18] proves that the technique proposed by [PERSON] et al. [18] is more robust than ours in the case of cropping, salt and pepper noise, and noise attacks but in [18] the other attacks were not tested. Comparing our results with [40], our NC values between the original watermark and the extracted watermark in the case of sharpening and Gaussian filtering are better than the results of [40]. By comparing our NC values with the NC values of [21], one can see that in the case of average filtering and rotation (1\({}^{\circ}\)) our method is more robust than the method of [21]. While in the case of the other attacks such as cropping left top corner, sharpening, histogram equalization, median filtering, rotation (5\({}^{\circ}\)) and rotation (10\({}^{\circ}\)), the method of [PERSON] et al. [21] is more robust than our method but there is no big difference. Comparing the results of our method with the method of [PERSON] et al.