# Spatial Domain-Based Nonlinear Residual Feature Extraction for Identification of Image Operations
## 3 Experiments and Discussions
### Detection and Classification of Various Image Postprocessing Operations
The results in the diagonal show the multiclass classification results and are highlighted in bold for easy following. According to the results, the identification accuracy is calculated as 91.3% on average with the proposed SDNR features and BPNN [26], 92.5% on average with the proposed SDNR features and AlexNet [28], and 95.9% on average with the proposed SDNR features and the employed CNN classifier. These very good results indicate the effectiveness of the proposed method. Furthermore, in addition to the comparison of detection of various image postprocessing operations, the comparison of classification of various image postprocessing operations using different features with different classifiers is shown in Table 6. Similar to the results shown in Table 3, to show the superiority of the proposed SDNR feature, we applied the SPAM [30] for comparison, and the BPNN [26], the AlexNet [28], and the employed CNN classifier were respectively applied within the proposed framework to calculate the corresponding classification accuracy. It is observed that when applying SPAM feature, the classification accuracy is 89.7% on average when using BPNN, 90.6% on average when using AlexNet, and 85% on average when using the employed CNN, while the corresponding detection accuracies are 88.89%, 92.2%, and 95.9%, respectively, when applying the different classifiers with the proposed SDNR. The results demonstrate that the proposed method performs the best for classifying a variety of image postprocessing operations. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline
**Actual/Predicted** & **Orig** & **GC** & **HE** & **UM** & **Rot** & **Sca** & **MeanF** & **MedF** & **WF** & **GF** & **JPEG** & **JP2** & **LPF** & **HPF** & **HF** & **BF** \\ \hline Orig & **90.3** & 0.4 & * & * & * & * & * & * & * & * & * & * & * & 1.6 & 3.3 & 1.6 & * & * \\ \hline GC & 2 & **82.9** & 2.4 & 1.4 & 0.8 & * & * & 0.4 & * & * & 0.8 & 0.8 & * & * & * & * \\ \hline HE & * & 0.2 & **99.3** & 0.3 & * & * & * & 0.1 & 0.2 & * & * & * & * & * & * \\ \hline UM & 2.1 & 1.4 & 0.3 & **93.3** & * & * & * & 0.4 & 0.3 & * & * & 1 & * & * & * & * \\ \hline Rot & 2.2 & 1.4 & * & * & **94.1** & 0.5 & * & * & * & * & * & * & 2 & * & 2 & * \\ \hline Sca & 30.5 & 6.5 & * & 0.4 & * & **60.2** & * & * & * & * & 7.7 & * & 0.5 & 3 & * & 3 \\ \hline MeanF & 0.3 & * & * & * & * & * & * & **98.8** & * & * & * & * & * & * & 0 & * & * \\ \hline MedF & * & * & 0.8 & * & 0.4 & 0.4 & * & 97.7 & * & * & * & 0.2 & * & * & * & 0.2 \\ \hline WF & * & * & * & * & * & * & * & * & **99.2** & * & * & * & * & * & * \\ \hline GF & 1 & * & * & * & * & 0.4 & * & * & * & **99.9** & 2 & * & * & * & * & * \\ \hline JPEG & * & 0.4 & * & 0.4 & * & 0.4 & * & 0.4 & * & **92.5** & 0.8 & 0.5 & 0.2 & 2 & 1 \\ \hline JP2 & 1 & 0.4 & * & * & * & 0.4 & * & 0.4 & * & * & 2.4 & **87.4** & * & 0.2 & * & * \\ \hline LPF & 0.3 & * & * & * & * & * & * & * & * & * & * & **98.6** & * & * & * \\ \hline HPF & 0.2 & 0.1 & * & * & * & * & * & 0.1 & * & 0.2 & * & * & **98.4** & 0.1 & * \\ \hline HF & 20.7 & * & * & * & * & 3.2 & 0.5 & 0.7 & 2.5 & 0.3 & * & 2.5 & * & **70.73** & * \\ \hline BF & 30.5 & 10.7 & 4.2 & 1 & 5 & * & * & 0.5 & * & * & * & 3.5 & * & * & 1.6 & 59.4 \\ \hline \hline \end{tabular} Note: symbol * indicates that the predicted percentage is under 0.1%.  
\end{table} 
Table 3: Identification of various image postprocessing operations using the proposed SDNR features and BPNN [26] (%).