# Image Forensics Using Non-Reducing Convolutional Neural Network for Consecutive Dual Operators
## 4 Experimental Results
### Detection of Dual Operators Sequence for Dissimilar Specification  
Operators have the same parameter settings in training and testing for the experimental analysis above. However, operators may be the same, but the parameters of operators may vary in the real scenario. To assess the robustness of the proposed method against dissimilar specifications of operators, some experiments are performed. As can be seen \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\alpha\) & \(\beta\) & **Compression** & \(\Omega\)**0 & \(\Omega\)**1 & \(\Omega\)**2 & \(\Omega\)**3 & \(\Omega\)**4 &
\begin{tabular}{c} **Average** \\ **Accuracy** \\ \end{tabular} \\ \hline GAU\_1.0 & MF5 & Q1 = 90, Q2 = 70 & 99.13 & 92.56 & 86.89 & 91.96 & 92.80 & 92.67 \\ \hline GAU\_1.0 & MF5 & Q1 = 75, Q2 = 85 & 98.91 & 94.27 & 88.14 & 92.85 & 99.13 & 94.66 \\ \hline GAU\_1.0 & MF5 & Q1 = 85, Q2 = 75 & 96.44 & 90.07 & 89.03 & 90.58 & 96.24 & 92.47 \\ \hline GAU\_1.0 & UP\_1.5 & Q1 = 75, Q2 = 85 & 98.93 & 84.09 & 98.77 & 97.60 & 68.21 & 89.52 \\ \hline GAU\_1.0 & UP\_1.5 & Q1 = 85, Q2 = 85 & 97.29 & 73.14 & 97.83 & 91.58 & 81.52 & 88.27 \\ \hline GAU\_0.9 & UP\_1.2 & Q1 = 70, Q2 = 90 & 99.81 & 89.87 & 97.55 & 93.32 & 66.23 & 89.35 \\ \hline GAU\_0.8 & MF3 & Q1 = 70, Q2 = 90 & 99.59 & 95.76 & 94.67 & 95.72 & 91.45 & 95.44 \\ \hline MF5 & UP\_1.5 & Q1 = 75, Q2 = 85 & 97.28 & 78.22 & 98.35 & 96.29 & 79.21 & 89.87 \\ \hline MF5 & UP\_1.5 & Q1 = 85, Q2 = 75 & 97.39 & 76.70 & 97.97 & 96.79 & 71.89 & 88.15 \\ \hline SH\_2.0 & UP\_1.2 & Q1 = 80, Q2 = 90 & 97,63 & 95.23 & 94.82 & 84.17 & 90.12 & 89.84 \\ \hline SH\_3.0 & UP\_1.5 & Q1 = 80, Q2 = 90 & 98.21 & 96.97 & 94.56 & 83.67 & 89.45 & 92.57 \\ \hline SH\_3.0 & UP\_1.5 & Q1 = 75, Q2 = 85 & 98.22 & 98.23 & 96.25 & 85.45 & 80.41 & 91.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Operator sequence detection on compressed images with similar specifications. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Set**} & **Single Operator** & **Uncompressed** & **Compression Q = 85** \\ \hline \multirow{4}{*}{Set 1} & ORI & 89.15 & 85.93 \\ \cline{2-4}  & SH\_2.0 & 97.01 & 82.14 \\ \cline{2-4}  & UP\_1.2 & 99.91 & 83.37 \\ \cline{2-4}  & MF5 & 99.81 & 98.82 \\ \cline{2-4}  & GAU\_7.0 & 99.56 & 92.84 \\ \cline{2-4}  & Average Accuracy & 97.09 & 88.62 \\ \hline \multirow{4}{*}{Set 2} & ORI & 97.39 & 90.81 \\ \cline{2-4}  & SH\_3.0 & 90.70 & 83.08 \\ \cline{2-4}  & UP\_1.5 & 99.99 & 90.92 \\ \cline{2-4}  & MF3 & 99.81 & 95.11 \\ \cline{2-4}  & GAU\_1.0 & 99.93 & 95.17 \\ \cline{2-4}  & Average Accuracy & 97.56 & 91.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Single operator detection. in the first row of Table 4, four values of standard deviation for Gaussian blurring {0.7, 0.8, 0.9, 1.0} are considered for training. A total of 60,000 images are used in training operated by Gaussian blurring and 15,000 images are operated by the four Gaussian blurring parameters. A total of 40,000 images are used to test operated by Gaussian blurring for 1334 images and operated by 300 Gaussian blurring parameters like the range of the parameters from 0.701 to 0.900. Therefore, a total of 300,000 images are used for training, and 200,000 are used for testing the detection of five-class classification problems of two operator sequence. Similarly, parameters are defined in Table 4 for other operators. The performance of the proposed CNN model is also excellent in dissimilar specifications. One scenario of compressed images with Q1 = 80 and Q2 = 90 is also displayed in Table 4 for operators \(\alpha\) = GAU and \(\beta\) = UP. There is some reduction in the detection accuracy even still it is greater than 94%. Here, some results are discussed for dissimilar compression factors on training and testing images. In the first row of Table 5, the JPEG compression quality factors for training images are Q1 = 85 and Q2 = 75 and Q1 = 80 and Q2 = 75 for testing images. The other operator parameters are the same for training and testing. In that case, there is still 92.40% detection accuracy.