# Watermarking Tiny MLCommons Image Applications Without Extra Deployability Costs
## 7 Results
### White-Box Watermarking Results and Using a Custom Regularizer
#### 7.1.2 Image Classification Results  
For the Section 5.2 use case, employing the CIFAR-10 dataset and the ResNet-8 model, the watermark has been applied with all three types of matrices explained in Section 6.1.2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Total** & \multirow{2}{*}{**Scale**} & **Training** & **Test** & \multirow{2}{*}{**\(b\)-Length**} & **Bits** & \multirow{2}{*}{**BER [\%]**} \\
**Numbers** & & **Accuracy** & **Accuracy** & & & \(\
eq\)**\(b\)** & \\ \hline
3 & 0.01 & 67.76 & 68.42 & 256 & 78 & 30.47 \\ \hline
3 & 0.02 & 66.25 & 67.23 & 256 & 76 & 29.69 \\ \hline
3 & 0.05 & 67.16 & 67.85 & 256 & 64 & 25.00 \\ \hline
6 & 0.01 & 71.34 & 70.62 & 256 & 63 & 24.61 \\ \hline
15 & 0.01 & 78.42 & 78.29 & 256 & 68 & 26.56 \\ \hline
30 & 0.01 & 88.99 & 74.11 & 256 & 61 & 23.82 \\ \hline
**90** & **0.01** & **85.42** & **83.47** & **256** & **57** & **22.27** \\ \hline
100 & 0.01 & 94.52 & 75.76 & 256 & 59 & 23.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results achieved using the Section 5.1 case with the random type of Section 6.1.2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Total** & \multirow{2}{*}{**Scale**} & **Training** & **Test** & \multirow{2}{*}{**\(b\)-Length**} & **Bits** & \multirow{2}{*}{**BER [\%]**} \\
**Numbers** & & **Accuracy** & **Accuracy** & & & \(\
eq\)**\(b\)** & \\
**of Epochs** & & **[\%]** & **[\%]** & & & \(\
eq\)**\(b\)** & \\ \hline
3 & 0.01 & 67.95 & 67.65 & 256 & 2 & 0.78 \\ \hline
3 & 0.02 & 68.51 & 69.72 & 256 & 2 & 0.78 \\ \hline
3 & 0.05 & 66,54 & 67.24 & 256 & 0 & 0.00 \\ \hline
6 & 0.01 & 73.40 & 73,79 & 256 & 0 & 0.00 \\ \hline
15 & 0.01 & 77.13 & 76.15 & 256 & 0 & 0.00 \\ \hline
30 & 0.01 & 89.29 & 72.55 & 256 & 0 & 0.00 \\ \hline
90 & 0.01 & 84.63 & 75.23 & 256 & 0 & 0.00 \\ \hline
**100** & **0.01** & **94.78** & **71.66** & **256** & **0** & **0.00** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results achieved using the Section 5.1 case set direct type of Section 6.1.2. In Table 6, the WMd model trained and evaluated has been described for the Section 5.2 case with the form of the watermark matrix in Section 6.1.2 set as direct. In Table 7, the trained and evaluated WMd model has been described for the Section 5.2 case with the form of the watermark matrix in Section 6.1.2 set as random. In Table 8, the WMd model trained and evaluated has been described for the Section 5.2 case with the form of the watermark matrix in Section 6.1.2 set as diff. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Total** & \multirow{2}{*}{**Scale**} & \multicolumn{2}{c}{**Training Accuracy**} & \multicolumn{2}{c}{**Test Accuracy**} & \multirow{2}{*}{\(b\)**-Length**} & \multicolumn{2}{c}{**Bits**} \\
**Numbers of Epochs** & & & **[\%]** & & **[\%]** & & **\(\
eq b\)** \\ \hline \(1\) & 0.01 & 48.77 & 44.41 & 256 & 2 & 0.78 \\ \hline \(1\) & 0.02 & 48.04 & 50.32 & 256 & 0 & 0.00 \\ \hline \(1\) & 0.05 & 48.81 & 41.56 & 256 & 0 & 0.00 \\ \hline \(2\) & 0.01 & 60.28 & 57.41 & 256 & 1 & 0.39 \\ \hline \(5\) & 0.01 & 72.07 & 64.82 & 256 & 0 & 0.00 \\ \hline \(10\) & 0.01 & 79.83 & 71.93 & 256 & 0 & 0.00 \\ \hline \(30\) & 0.01 & 75.37 & 65.75 & 256 & 0 & 0.00 \\ \hline \(\mathbf{100}\) & **0.01** & **83.39** & **75.04** & **256** & **0** & **0.00** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results using Section 6.1 case with the direct type of Section 5.2 case.