# Spatial Domain-Based Nonlinear Residual Feature Extraction for Identification of Image Operations
## 3 Experiments and Discussions
### Detection and Classification of Various Image Postprocessing Operations  
In this section, we present the results of our evaluation of the performance of the proposed method, which was conducted by measuring the accuracy of the detection and classification of the various image operations. Table 2 shows the accuracy of the detection of 15 operations using different classifiers. We applied the Ensemble classifier [24], the backpropagation neural network (BPNN) [26], the AlexNet [28], and the proposed CNN as classifiers within the proposed framework to calculate the corresponding detection accuracy. Additionally, to show the superiority of the proposed SDNR feature, we applied the subtractive pixel adjacency matrix (SPAM) [30] for comparison. It is observed that when applying the SPAM feature, the detection accuracy is 96.5% on average when using the Ensemble classifier, 94% on average when using the BPNN, 96.8% on average when using AlexNet, and 97.7% on average when using the employed CNN, while the corresponding detection accuracies are 97.1%, 95.3%, 97.5%, and 98.9%, respectively, when applying the different classifiers with the proposed SDNR. The comparison results demonstrate that the proposed SDNR feature outperforms the SPAM feature when using different classifiers. For the classifiers, the performance of the employed CNN is shown to be better than that of the other tested classifiers. In Table 2, the last row shows the average detection results with different features and classifiers and the results are highlighted in italic. The results demonstrate that the proposed approach performs very well in detecting the various image postprocessing operations. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**Ensemble Classifier**[24]} & \multicolumn{2}{c}{**BPNN**} & \multicolumn{2}{c}{**AlexNet**} & \multirow{2}{*}{**Employed CNN**} \\  & \multicolumn{2}{c}{**Classifier**[24]} & & \multicolumn{2}{c}{**[26]**} & & & **[28]** & \\ \hline \multirow{2}{*}{**Feature**} & **SPAM** & **Proposed** & **SPAM** & **Proposed** & **SPAM** & **Proposed** & **SPAM** & **Proposed** \\  & **[30]** & **SDNR** & **[30]** & **SDNR** & **[30]** & **SDNR** & **[30]** & **SDNR** \\ \hline GC & 96.2 & 96.4 & 92.1 & 93.1 & 95.3 & 96.7 & 96.5 & 97.6 \\ \hline HE & 98.3 & 98.1 & 94.3 & 98.9 & 97.2 & 96.4 & 98.4 & 99.5 \\ \hline UM & 97.2 & 98.3 & 96.4 & 96.2 & 98.6 & 98.7 & 97.6 & 99.3 \\ \hline MeanF & 96.5 & 97.5 & 96.2 & 97.6 & 97.3 & 97.5 & 98.5 & 98.8 \\ \hline MedF & 97.9 & 98.1 & 97.6 & 98.8 & 97.6 & 97.1 & 97.3 & 99.6 \\ \hline WF & 98.8 & 99.6 & 96.2 & 97.1 & 98.5 & 96.5 & 97.1 & 97.6 \\ \hline GF & 99.2 & 98.7 & 97.3 & 98.3 & 98.3 & 98.8 & 99 & 99.8 \\ \hline SCA & 91.3 & 93.4 & 90.3 & 89.1 & 92.2 & 97.5 & 95.3 & 99.5 \\ \hline ROT & 97.2 & 96.3 & 94.5 & 95.6 & 97.6 & 98.6 & 98.3 & 98.9 \\ \hline JPEG & 95.3 & 96 & 96.8 & 97.4 & 98.5 & 99.2 & 97.8 & 98.2 \\ \hline JP2 & 97.4 & 97.8 & 97.6 & 96.9 & 98.7 & 98.9 & 98.1 & 98.3 \\ \hline LPF & 96.3 & 97.3 & 97.3 & 97.2 & 97.8 & 98.8 & 98.7 & 99.6 \\ \hline HPF & 98.2 & 98.1 & 96.3 & 98.3 & 96.3 & 97.8 & 97.6 & 99.7 \\ \hline BF & 93.3 & 95.3 & 84.2 & 86.7 & 94.6 & 95.6 & 97.2 & 98.2 \\ \hline HF & 94.2 & 95.6 & 82.3 & 88.6 & 93.2 & 94.3 & 98 & 99 \\ \hline
**Average** & _96.5_ & _97.1_ & _94_ & _95.3_ & _96.8_ & _97.5_ & _97.7_ & _98.9_ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detection of various image postprocessing operations using different features with different classifiers (%). In addition to detecting whether or not the images have been processed, this approach can also identify a variety of operations. Tables 3-5 show the confusion matrices of the multiclass identification results using the proposed SDNR feature paired with BPNN [26], AlexNet [28], and the employed CNN classifier. In Tables 3-5, the symbol '*' indicates that the predicted percentage is under 0.1%, which means that the classification can be ignored.