## Appendix A Clean Images  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**CNNs** & \(p\) & **Abacus** & **Acorn** & **Baseball** & **Broom** & **Brown Bear** & **Canoe** & **Hippocampotamus** & **Llama** & **Marca** & **Mountain Bike** \\ \hline \hline \multirow{9}{*}{\(\mathcal{C}_{1}\)} & 1 & 1.000 & 0.994 & 0.997 & 0.982 & 0.996 & 0.987 & 0.999 & 0.998 & 0.481 & 0.941 \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**CNNs** & \(p\) & **Abacus** & **Acorn** & **Baseball** & **Brown** & **Brown Bear** & **Canoe** & **Hippocampamus** & **Llama** & **Marca** & **Mountain Bike** \\ \hline \multirow{9}{*}{DenseNet-121} & 1 & 1.000 & 0.981 & 0.997 & 0.999 & 0.995 & 0.992 & 0.999 & 0.997 & 0.607 & 0.942 \\  & 2 & 1.000 & 0.997 & 0.989 & 1.000 & **0.670** & 0.909 & 0.998 & 0.987 & 0.883 & 0.987 \\  & 3 & 0.998 & 0.845 & 1.000 & 1.000 & 0.996 & 0.836 & 0.987 & 0.997 & 1.000 & 0.891 \\  & 4 & 0.996 & 0.997 & 1.000 & 1.000 & 0.997 & 0.620 & 0.239 & 0.984 & 0.312 & 0.619 \\ \cline{2-11}  & 5 & 1.000 & 0.999 & 1.000 & 0.998 & 0.955 & 0.811 & 1.000 & 1.000 & 0.145 & 0.986 \\ \cline{2-11}  & 6 & 1.000 & 1.000 & 0.957 & 0.998 & 1.000 & 0.990 & 0.997 & 0.916 & 0.692 & 0.999 \\ \cline{2-11}  & 7 & 0.998 & 0.999 & 0.999 & 0.973 & 0.937 & 0.525 & 0.985 & 0.974 & 0.902 & 0.940 \\ \cline{2-11}  & 8 & 1.000 & 0.999 & 0.993 & 0.993 & 0.995 & 0.913 & 1.000 & 1.000 & 0.999 & 0.962 \\ \cline{2-11}  & 9 & 1.000 & 0.998 & 0.981 & 1.000 & 0.997 & 0.820 & 0.999 & 1.000 & 0.999 & 0.992 \\ \cline{2-11}  & 10 & 0.009 & 0.996 & 1.000 & 0.999 & 0.995 & 0.923 & 0.999 & 0.886 & 0.572 & 0.870 \\ \hline \multirow{9}{*}{DenseNet-169} & 1 & 0.999 & 0.978 & 1.000 & 0.999 & 0.999 & 0.997 & 0.997 & 0.999 & 0.952 & 0.873 \\  & 2 & 1.000 & 0.999 & 0.998 & 0.992 & **0.535** & 0.764 & 0.998 & 0.999 & 0.995 & 0.861 \\ \cline{2-11}  & 3 & 1.000 & 0.998 & 1.000 & 1.000 & 0.999 & 0.880 & 1.000 & 0.994 & 1.000 & 0.977 \\ \cline{2-11}  & 4 & 0.990 & 0.996 & 1.000 & 1.000 & 0.900 & 0.549 & 0.553 & 0.981 & 0.583 & 0.973 \\ \cline{2-11}  & 5 & 1.000 & 1.000 & 0.994 & 1.000 & 0.994 & 1.000 & 0.994 & 0.530 & 0.997 \\ \cline{2-11}  & 6 & 1.000 & 1.000 & 0.998 & 1.000 & 1.000 & 0.997 & 0.995 & 0.975 & 0.901 & 0.991 \\ \cline{2-11}  & 7 & 1.000 & 1.000 & 1.000 & 0.954 & 0.827 & 0.996 & 1.000 & 0.964 & 0.945 \\ \cline{2-11}  & 8 & 1.000 & 1.000 & 0.998 & 0.998 & 0.999 & 0.951 & 0.999 & 1.000 & 1.000 & 0.975 \\ \cline{2-11}  & 9 & 1.000 & 0.998 & 0.998 & 0.999 & 0.905 & 1.000 & 1.000 & 0.993 & 0.964 \\ \cline{2-11}  & 10 & 0.970 & 1.000 & 0.999 & 1.000 & 0.997 & 0.952 & 0.999 & 0.998 & 0.608 & **0.507** \\ \hline \multirow{9}{*}{DenseNet-201} & 1 & 0.999 & 0.975 & 0.998 & 1.000 & 0.990 & 0.990 & 0.998 & 0.996 & **0.584** & 0.986 \\  & 2 & 1.000 & 1.000 & 0.984 & 1.000 & 0.844 & 0.957 & 0.996 & 0.996 & 0.993 & 0.997 \\ \cline{2-11}  & 3 & 0.987 & 0.950 & 1.000 & 0.998 & 0.669 & 0.999 & 0.994 & 1.000 & 0.886 \\ \cline{2-11}  & 4 & 0.886 & 0.994 & 1.000 & 1.000 & 0.998 & 0.822 & 0.870 & 1.000 & **0.541** & 0.947 \\ \cline{2-11}  & 5 & 1.000 & 1.000 & 0.999 & 0.983 & 0.980 & 0.586 & 1.000 & 0.998 & 0.141 & 0.980 \\ \cline{2-11} DenseNet-201 & 6 & 1.000 & 1.000 & 0.995 & 1.000 & 1.000 & 0.994 & 0.999 & 0.724 & 0.693 & 0.996 \\ \cline{2-11}  & 7 & 1.000 & 1.000 & 1.000 & 0.998 & 0.865 & 0.997 & 0.970 & 0.973 & 0.917 \\ \cline{2-11}  & 8 & 1.000 & 0.999 & 0.861 & 0.989 & 0.899 & 0.951 & 0.999 & 0.997 & 0.993 \\ \cline{2-11}  & 9 & 1.000 & 0.999 & 0.877 & 1.000 & 0.984 & 0.995 & 1.000 & 0.999 & 0.987 & 0.988 \\ \cline{2-11}  & 10 & 1.000 & 1.000 & 0.998 & 0.999 & 0.978 & 0.984 & 0.987 & 0.963 & 0.983 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: In Step 1 and 2 of Scheme (11), the Nearest degrading interpolation function is employed for resizing images to match the input size of CNNs before they are fed into the CNNs.