# WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication
## 4 Experimental Results  
In this section, we show the actual simulation results of WMNet in the ownership authentication verification phase. Figure 4 shows the medical images used in the simulation, which are of size \(512\times 512\) pixels. The medical images are computed tomography (CT) and X-ray images. Figure 5 shows the watermarks used in the simulation, which are binary images of sizes \(64\times 64\) and \(32\times 32\), respectively. Figure 6 shows the simulated result using [PERSON]'s lossless watermarking system [15] with no imaging processing damage done to the medical images by the proposed system. Figure 6a,b show the \(128\times 128\) OSI and MSI generated from the \(128\times 128\) CT image, and Figure 6c shows the extracted watermarks. Figure 6d,e show the \(128\times 128\) OSI and MSI generated from the X-ray image, and Figure 6f shows the extracted watermarks. From the simulation results, we can see that the lossless watermarking system can correctly retrieve the watermark if the image has not been attacked by image-processing methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Type** & **Input** & **Filter Size** & **Output** \\ \hline
1 & Convolution & \(64\times 64\times 1\) & \(3\times 3\) & \(32\times 32\times 16\) \\
2 & Max pooling & \(32\times 32\times 16\) & \(3\times 3\) & \(16\times 16\times 32\) \\
3 & Convolution & \(16\times 16\times 32\) & \(3\times 3\) & \(8\times 8\times 32\) \\
4 & Max pooling & \(8\times 8\times 32\) & \(3\times 3\) & \(4\times 4\times 32\) \\
5 & Fully connected & \(4\times 4\times 32\) & \(3\times 3\) & \(1\times 2\) \\
6 & Softmax & \(1\times 2\) & - & Result \\ \hline \hline \end{tabular}
\end{table} 
Table 1: WMNet parameters. Figure 4: Medical images used in the experiment. (**a**) Computed tomography (CT) and (**b**) X-ray. Figure 5: Watermark images used in the experiment: (**a**) \(64\times 64\) and (**b**) \(32\times 32\). For WMNet, we used MATLAB 2020b with an NVIDIA GeForce GTX 1080 Ti, \(\tau\) was set to 5, and \(\alpha\) contained 14,336 distorted watermarks. During training, \(\alpha\) was divided into the training, validation, and testing datasets in the ratios of 80%, 10%, and 10%, respectively. In addition, \(T_{c}\) was set as 0.65. This meant that when the NC value for the distorted watermark was greater than \(T_{c}\), it was defined as \"Correct\" and the number of images was 7165 images. Otherwise, it was defined as \"Incorrect\" and the number of images was 7170 images. Figure 7 shows the accuracy and loss during the WMNet training processing phase. From Figure 7, we can see that during training, accuracy continuously increased while loss continuously decreased. In addition, the accuracies of the final WMNet training, validation, and testing datasets were 99.8%, 97.9%, and 97.3%, respectively. Furthermore, the confusion matrices of the training, validation and testing datasets, shown in Figure 8, indicate that the rate of incorrect classification is low. Figure 9 shows the corresponding NC values of the incorrectly classified distorted watermarks. It was found that most of the NC values of distorted watermarks that were incorrectly classified by WMNet were located near the set \(T_{c}\) value. In other words, WMNet had good classification results on most distorted watermarks. The \(T_{c}\) value was set at 0.65, which is considered a sufficient threshold in most of the previously published watermarking systems. Figure 6: Simulation results when the medical image was not attacked by any image processing. (**a,b**) are the ownership share image (OSI) and master share image (MSI) (\(128\times 128\)) and (**c**) is retrieved watermarks (\(64\times 64\) ) after CT image simulation; (**d,e**) are the OSI and MSI (\(128\times 128\) ), and (**f**) is the retrieved watermark (\(64\times 64\)) after X-ray image simulation. Figure 7: Accuracy and loss results for WMNet training. (**a**) Accuracy and (**b**) loss.