# Spatial Domain-Based Nonlinear Residual Feature Extraction for Identification of Image Operations
## 4 Conclusions  
In summary, we have proposed the SDNR feature extraction method, which is constructed from locally supported linear filters in the spatial domain. By applying minimum and maximum operators, diversity and nonlinearity are thus introduced, and the construction thus brings nonsymmetry to the distribution of the extracted SDNR features. By applying the proposed SDNR method to the original images and the corresponding images after processing, we can extract the feature sets accordingly. This improves the acceleration of the network convergence. Then, by scanning the extracted SDNR feature sets with a patch-sized sliding window, we have proposed employing the five-layer CNN, and we trained a softmax classifier accordingly to detect and identify a variety of image postprocessing operations. The main contributions of this paper are summarized as follows: (1) We have considered the problems of both binary classification and multiclass identification in our study and solved both of them with the proposed approach. We have conducted extensive experiments on up to 15 various image postprocessing operations to evaluate the proposed approach, and the results indicate the effectiveness of our proposed method. (2) We have extracted SDNR features instead of the pixel values as the input of our deep learning model; in this way, the generalization ability can be enhanced and the network convergence can be promoted. (3) We have proposed employing the five-layer CNN as the classifier, compared with the conventional methods that use a classifier such as SVM and an ensemble classifier, to achieve higher detection accuracy. The experimental results demonstrate that the proposed approach performs well in classifying and identifying of image postprocessing operations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**BPNN [26]**} & \multirow{2}{*}{**AlexNet [28]**} & \multicolumn{3}{c}{**The Employed CNN Classifier**} \\  & & & & & **Classifier** \\ \hline \multirow{2}{*}{**Feature**} & **SPAM** & **Proposed** & **SPAM** & **Proposed** & **SPAM** & **Proposed** \\  & **[30]** & **SDNR** & [30] & **SDNR** & [30] & **SDNR** \\ \hline ORI & 81.1\% & 90.3\% & 82.3\% & 87.5\% & 91.4\% & 94.6\% \\ \hline GC & 82.1\% & 82.9\% & 91.3\% & 92.9\% & 87.5\% & 93.7\% \\ \hline HE & 94.3\% & 99.3\% & 93.2\% & 98.7\% & 94.4\% & 99.1\% \\ \hline UM & 96.4\% & 93.3\% & 91.6\% & 93.5\% & 69.1\% & 93.2\% \\ \hline MeanF & 96.2\% & 97.6\% & 97.3\% & 99.7\% & 89.5\% & 95.0\% \\ \hline MedF & 97.6\% & 98.8\% & 94.6\% & 97.1\% & 77.6\% & 98.7\% \\ \hline WF & 96.2\% & 99.7\% & 96.5\% & 99.6\% & 92.1\% & 100\% \\ \hline GF & 97.3\% & 99.2\% & 93.3\% & 94.8\% & 91.3\% & 99.4\% \\ \hline SCA & 90.3\% & 60.2\% & 70.2\% & 87.8\% & 90.1\% & 92.8\% \\ \hline ROT & 94.5\% & 94.1\% & 92.6\% & 92.2\% & 92.3\% & 93.3\% \\ \hline JPEG & 96.8\% & 92.5\% & 91.5\% & 88.5\% & 80.4\% & 92.5\% \\ \hline JP2 & 97.6\% & 87.4\% & 89.7\% & 89.7\% & 89.9\% & 92.5\% \\ \hline LPF & 97.3\% & 98.6\% & 97.8\% & 97.5\% & 80.6\% & 99.7\% \\ \hline HPF & 82.3\% & 98.3\% & 86.3\% & 98.2\% & 89.9\% & 99.2\% \\ \hline HF & 64.2\% & 59.4\% & 84.6\% & 82.2\% & 61.1\% & 95.3\% \\ \hline BF & 71.3\% & 70.7\% & 88.2\% & 81.1\% & 88.1\% & 94.6\% \\ \hline
**Average** & _89.7\%_ & _88.89\%_ & 90.6\% & 92.2\% & _85\%_ & 95.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of identification of image postprocessing operations using different features with different classifiers (%). **Author Contributions:** Data curation, T.H. ; Funding acquisition, X.Y. ; Investigation, X.Y. ; Methodology, X.Y. and T.H. ; Project administration, X.Y. ; Supervision, X.Y. ; Validation, T.H. ; Visualization, T.H. ; Writing--original draft, X.Y. and T.H. ; Writing--review & editing, X.Y. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China, grant number 61902448.