# Image Forensics Using Non-Reducing Convolutional Neural Network for Consecutive Dual Operators
## 5 Conclusions  
Digital images have turned out to be the most accepted representation of information. The latest technologies have empowered naive users to create fake images effortlessly. Though, multiple operations have been performed to construct a real-looking fake image. The detection of manipulation operations has assisted to find the fake image. At present, deep learning approaches have been taken into the place of handcrafted feature extraction methods. A convolutional neural network has been suggested to preserve the authenticity of images. Thus, most techniques have been suggested for single operator detection so far. Very few techniques have been discussed to identify the dual operators and the order of operators. The proposed deep learning model could detect consecutive dual operators on the image and its corresponding order precisely. The bottleneck approach has been applied in the model to increase the layers and reduced the parameters. Unlike previous networks, one single global averaging pooling layer has been utilized to reduce the information loss and overfitting problem. The proposed model has been performed \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Operator**} & \multicolumn{2}{c}{**Classification Error (\%)**} & \multicolumn{2}{c}{**Operator**} & \multicolumn{2}{c}{**Classification Error (\%)**} \\ \hline \(\alpha\) & \(\beta\) & **Proposed** & **[PERSON] et al. [26]** & \(\alpha\) & \(\beta\) & **Proposed** & **[PERSON] et al. [26]** \\ \hline GAU\_1.0 & MF3 & 02.31 & 07.39 & MF3 & SH\_2.0 & 06.11 & 14.24 \\ \hline GAU\_1.0 & MF5 & 02.19 & 05.98 & MF3 & SH\_3.0 & 05.70 & 13.81 \\ \hline GAU\_1.0 & SH\_2.0 & 07.72 & 13.25 & MF3 & UP\_1.2 & 05.15 & 10.83 \\ \hline GAU\_1.0 & SH\_3.0 & 06.40 & 11.49 & MF3 & UP\_1.5 & 01.98 & 07.49 \\ \hline GAU\_1.0 & UP\_1.2 & 04.93 & 08.79 & MF5 & SH\_2.0 & 09.60 & 18.02 \\ \hline GAU\_1.0 & UP\_1.5 & 01.89 & 03.77 & MF5 & SH\_3.0 & 06.92 & 15.49 \\ \hline GAU\_0.7 & MF3 & 03.05 & 08.26 & MF5 & UP\_1.2 & 09.82 & 13.37 \\ \hline GAU\_0.7 & MF5 & 05.57 & 08.01 & MF5 & UP\_1.5 & 06.09 & 08.34 \\ \hline GAU\_0.7 & SH\_2.0 & 06.91 & 13.31 & SH\_2.0 & UP\_1.2 & 05.72 & 14.94 \\ \hline GAU\_0.7 & SH\_3.0 & 06.70 & 12.70 & SH\_2.0 & UP\_1.5 & 07.34 & 11.67 \\ \hline GAU\_0.7 & UP\_1.2 & 02.37 & 06.23 & SH\_3.0 & UP\_1.2 & 05.89 & 13.54 \\ \hline GAU\_0.7 & UP\_1.5 & 01.31 & 05.95 & SH\_3.0 & UP\_1.5 & 06.61 & 10.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis for uncompressed images. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Operator**} & \multicolumn{2}{c}{**Classification Error (\%)**} \\ \hline \(\alpha\) & \(\beta\) & **Proposed** & **[PERSON] et al. [26]** \\ \hline GAU\_1.0 & UP\_1.5 & Q1 = 75, Q2 = 85 & 10.48 & **09.80** \\ \hline GAU\_1.0 & UP\_1.5 & Q1 = 85, Q2 = 85 & **11.73** & 14.68 \\ \hline GAU\_1.0 & MF5 & Q1 = 90, Q2 = 70 & **07.33** & 16.35 \\ \hline GAU\_1.0 & MF5 & Q1 = 75, Q2 = 85 & **05.34** & 11.82 \\ \hline GAU\_1.0 & MF5 & Q1 = 85, Q2 = 75 & **07.53** & 15.93 \\ \hline GAU\_0.9 & UP\_1.2 & Q1 = 70, Q2 = 90 & **10.65** & 14.12 \\ \hline MF5 & UP\_1.5 & Q1 = 75, Q2 = 85 & **10.13** & 13.25 \\ \hline MF5 & UP\_1.5 & Q1 = 85, Q2 = 75 & **11.85** & 21.75 \\ \hline MF3 & GAU\_0.8 & Q1 = 70, Q2 = 90 & **04.56** & 12.70 \\ \hline SH\_2.0 & UP\_1.2 & Q1 = 80, Q2 = 90 & **10.16** & 14.65 \\ \hline SH\_3.0 & UP\_1.5 & Q1 = 80, Q2 = 90 & **07.43** & 13.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative analysis of compressed images. robustly against challenging scenarios like low-resolution images and compression in the exhaustive experimental analysis. Each author discussed the details of the manuscript and contributed equally to its preparation. [PERSON]-[PERSON] designed and wrote the manuscript. [PERSON] implemented the proposed technique and provided the experimental results. [PERSON]-[PERSON] reviewed the article. [PERSON]-[PERSON] drafted and revised the manuscript.