# Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
## 4 Experimental Results
### Performance Evaluation of Customized Fine-Tuned Residual Model  
The customized model's optimizers (SGDM, Adam, and RMSprop) and hyperparameters (as detailed in Table 4) are also used to train and evaluate the model's performance. Table 10 shows the accuracy for different optimizers and kernel sizes. The model performs best with an SGDM optimizer for BW QR codes and an Adam optimizer for color QR codes. It was also found that the model works best with a kernel size of \(7\times 7\). Therefore, a kernel size of \(7\times 7\) is used to evaluate the standard performance metrics in the next evaluation.  
Table 11 displays the standard performance metrics, including accuracy, precision, recall (or sensitivity), and specificity, for each optimizer for BW QR codes (A) and color QR codes (B).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{(A) BW QR CODE} \\ \hline \multirow{2}{*}{Optimizers} & \multicolumn{5}{c}{BW} \\ \cline{2-5}  & \(3\times 3\) & \(5\times 5\) & \(7\times 7\) & Time \\ \hline SGDM & 0.9989 & 0.9930 & 0.9963 & 52 min \\ \hline Adam & 0.9991 & 0.9982 & 0.9970 & 59 min \\ \hline RMSprop & 0.9955 & 0.9640 & 0.9989 & 55 min \\ \hline \multicolumn{5}{c}{(B) COLOR QR CODE} \\ \hline \multirow{2}{*}{Optimizers} & \multicolumn{5}{c}{Color} \\ \cline{2-5}  & \(3\times 3\) & \(5\times 5\) & \(7\times 7\) & Time \\ \hline SGDM & 0.9865 & 0.9557 & 0.9963 & 40 min \\ \hline Adam & 0.9917 & 0.9991 & 0.9949 & 47 min \\ \hline RMSprop & 0.9998 & 1 & 0.9863 & 44 min \\ \hline \hline \end{tabular}
\end{table}
Table 10: Customized residual model. Identification accuracy under different kernel sizes for the first layer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{(A) BW QR CODE} \\ \hline \multirow{2}{*}{SGDM} & \multirow{2}{*}{99.69} & 0.997 & 0.997 & 1 & 51.38 \\ \hline Adam & 99.80 & 0.997 & 0.997 & 1 & 55.75 \\ \hline RMSprop & 99.89 & 0.999 & 0.999 & 1 & 55.16 \\ \hline \multicolumn{5}{c}{(B) COLOR QR CODE} \\ \hline \multirow{2}{*}{SGDM} & \multirow{2}{*}{99.69} & \multirow{2}{*}{0.997} & \multirow{2}{*}{0.997} & \multirow{2}{*}{1} & \multirow{2}{*}{51.38} \\ \cline{1-1} \cline{5-5}  & & & & & \\ \hline Adam & 99.80 & 0.997 & 0.997 & 1 & 55.75 \\ \hline RMSprop & 99.89 & 0.999 & 0.999 & 1 & 55.16 \\ \hline \multicolumn{5}{c}{(B) COLOR QR CODE} \\ \hline \multirow{2}{*}{SGDM} & \multirow{2}{*}{99.7} & \multirow{2}{*}{0.996} & \multirow{2}{*}{0.996} & \multirow{2}{*}{1} & \multirow{2}{*}{40.38} \\ \cline{1-1} \cline{5-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performing metrics for the customized residual model.  
As shown in Table 11, the training of BW QR codes is still slower than the training of color QR codes, similar to the results of the previous experiment comparing pretrained CNN models. However, the training time for our model for BW and color QR codes only differs by about 9-12 min, which is similar to the training time for the pretrained ResNet18 model in the earlier experiment for the BW QR Code.  
Our model also has better overall stability in terms of accuracy across the three provided optimizers. For the BW QR code results in Table 5 SGDM has the fastest training time at 51.3 min, about 4 min faster than the other two optimizers. Adam and RMSprop provide better identification accuracy (99.8%) but at the cost of longer training times.  
For identifying a color QR code, RMSprop has fluctuating accuracy between 98% and 99%, making SGDM and Adam the better choices. Table 10B shows that SGDM has the fastest training time for color QR codes at 40.3 min. However, the performance of SGDM and Adam is consistently very similar at 99.7%.