# Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
## 4 Experimental Results
### Performance Evaluation of BW QR Codes
The M283 has a deeper and more consistent black color with smooth color transitions, while the M401 has a less consistent black color with some white patches visible upon closer examination. Model number 11, the Konica Minolta Bizhub C364, has a 90% identification rate. It is vital to think that this does not necessarily mean that it is the most difficult printer to identify. In fact, a 90% identification rate is still quite impressive. The main reason for this model's lower average identification rate is that GoogleNet achieved significantly lower accuracy when using both Adam and RMSprop optimization algorithms. Performance Evaluation for Color QR Codes  
In general, pretrained CNN models tend to train faster when using a Color QR Code dataset rather than a BW QR Code dataset. While it might be expected that an 8-bit grayscale image would be easier to process due to having fewer color channels than a 24-bit RGB image, it turns out that more features can be extracted from a 24-bit image, which significantly speeds up the inference process of the CNN models in this case. According to Table 7, MobileNetv2 with the RMSprop optimization algorithm has the highest accuracy, at 99.9%, after 103 min of training. VGG16 comes in second place, with an accuracy of 99.8% and a faster training time of 86 min. AlexNet takes third place, with a training time of only 33 min and an accuracy of 99.6%. AlexNet has the fastest training time of all the models and is also one of the top three for accuracy, beating ResNet18 by almost 10 min. Previously, ResNet18 was known for having the fastest training time in a BW QR code experiment. Table 8 shows the results of the per-printer identification. The same indicators are used to evaluate the accuracy of each printer and optimization algorithm in order to determine the best-performing CNN model. Model number 2, the Canon LBP9200C, is the easiest printer to identify. In second place is model number 4, the Fuji Xerox DocuCentre-IV, with 98.6% identification accuracy. Model number 1, the Canon imageRunner 6555i, takes third place with 97.7% accuracy, just 0.9% behind the second-place printer. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model Name** & **Optimizer** & **Accuracy** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \multirow{3}{*}{AlexNet} & SCDM & 0.9956 & 0.979 & 1 & 1 & 1 & 0.999 & 1 & 1 & 0.987 \\ \cline{2-9}  & Adam & 0.125 & – & – & – & – & – & – & – \\ \cline{2-9}  & RMSprop & 0.125 & – & – & – & – & – & – & – \\ \hline \multirow{3}{*}{GoogleNet} & SCDM & 0.7366 & 0.997 & 1 & 0.865 & 0.999 & 0.605 & 0.353 & 1 & 0.669 \\ \cline{2-9}  & Adam & 0.7188 & 0.897 & 0.991 & 0.685 & 1 & 0.464 & 0.561 & 1 & 0.145 \\ \cline{2-9}  & RMSprop & 0.5095 & 0.743 & 1 & 0.017 & 0.967 & 0.255 & 0.030 & 0.212 & 0.855 \\ \hline \multirow{3}{*}{DenseNet201} & SCDM & 0.9666 & 1 & 1 & 0.996 & 0.988 & 0.913 & 0.910 & 1 & 0.925 \\ \cline{2-9}  & Adam & 0.9234 & 1 & 1 & 1 & 1 & 0.943 & 0.991 & 1 & 0.447 \\ \cline{2-9}  & RMSprop & 0.905 & 1 & 1 & 1 & 1 & 0.939 & 0.965 & 1 & 0.328 \\ \hline \multirow{3}{*}{MobileNetv2} & SCDM & 0.9685 & 1 & 1 & 1 & 0.969 & 0.936 & 0.996 & 0.996 & 0.995 \\ \cline{2-9}  & Adam & 0.9857 & 0.999 & 1 & 1 & 0.887 & 1 & 1 & 1 \\ \cline{2-9}  & RMSprop & 0.998 & 1 & 1 & 1 & 1 & 0.999 & 1 & 1 & 1 \\ \hline \multirow{3}{*}{ResNet18} & SCDM & 0.9773 & 0.999 & 1 & 0.947 & 1 & 0.879 & 0.996 & 0.997 & 0.999 \\ \cline{2-9}  & Adam & 0.9744 & 0.999 & 1 & 0.875 & 1 & 0.960 & 0.962 & 1 & 1 \\ \cline{2-9}  & RMSprop & 0.9268 & 1 & 1 & 0.895 & 0.999 & 0.978 & 0.987 & 1 & 0.551 \\ \hline \end{tabular}
\end{table} 
Table 8: COLOR QR Code Per Printer Identification Result for Each Pretrained CNN Model.