# WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication
## 4 Experimental Results
In addition, WMNet was evaluated using 5-fold cross validation, with the aim of ensuring that such training materials for simulating distorted watermarks were not specifically composed of sliced data used to train effective WMNets. The simulated 14,336 distorted watermarks were divided into five groups, and the datasets of each group served as the testing dataset once, while the subsets of the other four groups served as the training dataset, so that five WMNets were obtained. The performance results of these five WMNets were evaluated in the testing dataset. In addition, we analyzed the performance using  
Figure 8: Confusion matrix for WMNet training. (**a**) Training dataset, (**b**) validation dataset, and (**c**) testing dataset. Figure 9: Visualization distribution of incorrectly classified distorted watermarks (\(T_{c}\) is 0.65). (**a**) Validation dataset and (**b**) testing dataset. the following performance measures, where TP is true positive, TN is true negative, FP is false positive, and FN is false negative; the results are compiled in Table 2. It can be seen from the table that the results of each test dataset are very approximate, showing that such datasets generated by the simulation distorted watermark method and WMNet constructed through deep learning techniques have very good efficacy. Thus, WMNet is not only able to generalize the dataset, but overfitting also does not occur. Moreover, it was confirmed that the method of generating several distorted watermarks by simulation and then collecting them into a dataset is free from the problem of data selection bias and can train valid WMNets effectively. \[Accuracy=\ \frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[Specificity=\ \frac{TN}{TN+FP} \tag{2}\]  
Figure 4:0  
Figure 4:1  
Figure 4:2  
Figure 4:3  
The main purpose of the proposed WMNet was to assist in the watermark system verification process. Specifically, if the extracted watermark was distorted because the protected image was attacked by image processing, we intended to determine the effectiveness of confirming the watermark information using WMNet. For simulation experiments using the actual watermarking system, we distorted the medical images using several common image-processing methods: JPEG compression (quality factor = 10%), JPEG 2000 (compression ratio = 8), Gaussian noise addition (mean = 0, variance = 0.5), salt-and-pepper noise addition (noise density = 50%), median filtering (window size = 4 \(\times\) 4), rotation (\(7^{\circ}\)), cropping (cropped area of 25%), and scaling (reduction to 1/16). We used peak signal-to-noise ratio (PSNR) [20] and NC to measure the attack intensity of the image. Table 3 summarizes the PSNR results for each medical image that was subjected to each attack. Different image-processing attacks cause different degrees of image distortion. The copyright protection mechanisms published in recent years have good robustness; hence, we chose those lossless image watermarking systems published in the past that still have room for breakthrough in terms of effectiveness [14; 18], applied them to medical images, and used WMNet to assist in the watermark verification stage. Table 4 shows the NC results after a medical image was subjected to attack, the watermark extraction results after the image underwent each image-processing attack, and the WMNet interpretation results. The results in the table show that there is some distortion in most medical images \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Performance Metrics (\%)**} & \multicolumn{5}{c}{**Testing Dataset**} \\ \cline{2-7}  & **1** & **2** & **3** & **4** & **5** & **Average** \\ \hline Accuracy & 97.2 & 97.9 & 98.1 & 98 & 97.9 & 97.8 \\ Specificity & 96.4 & 98.4 & 97.8 & 97.7 & 97.4 & 97.5 \\ Sensitivity & 97.9 & 98.4 & 97.8 & 97.7 & 97.4 & 97.8 \\ Negative predictive value & 97.9 & 97.4 & 98.4 & 98.3 & 98.4 & 98 \\ Positive predictive value & 96.5 & 98.4 & 97.8 & 97.7 & 97.4 & 97.6 \\ F-Measure & 97.2 & 97.9 & 98.1 & 98 & 97.9 & 97.8 \\ \hline \hline \end{tabular}
\end{table}