# Spatial Domain-Based Nonlinear Residual Feature Extraction for Identification of Image Operations
## 3 Experiments and Discussions
### Parameter Settings  
In the employed CNN architecture, the number of epochs has a large effect on the accuracy of the identification and the computational time. Therefore, in order for the proposed strategy to achieve good performance, it is important to determine how to set the appropriate parameters. Figure 4 shows the relationship between the accuracy of detection and the number of epochs (shown on the left) and the relationship between the computational time and the number of epochs (shown on the right). The results clearly indicate that either the detection accuracy or the computational expense increases with the number of epochs. To achieve a balance between the computational expense and the detection accuracy, we set the number of iterations to be 600, i.e., \(num\_epochs=600\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Operation Categories** & **Operation Type** & **Parameter Setting** \\ \hline \multirow{3}{*}{Spatial enhancement} & Gamma Correction (GC) & \(\gamma\): 1.0, 1.6, 1.8, 2.0 \\ \cline{2-3}  & Histogram Equalization (HE) & n/a \\ \cline{2-3}  & Unsharp Masking Sharpening (UM) & \(\sigma\): 0.5–1.5 \\ \cline{2-3}  & Mean Filtering (MeanF) & Window sizes: \(3\times 3\), \(5\times 5\), \(7\times 7\) \\ \cline{2-3}  & Gaussian Filtering (GF) & Window sizes: \(3\times 3\), \(5\times 5\), \(7\times 7\) \\ \cline{2-3}  & Median Filtering (MedF) & Windows size: \(3\times 3\), \(5\times 5\), \(7\times 7\) \\ \cline{2-3}  & Wiener Filtering (WF) & Windows size: \(3\times 3\), \(5\times 5\), \(7\times 7\) \\ \hline Scaling & Scaling (Sca) & Down-sampling: 40\%, 50\%, 70\% \\ \hline Rotation & Rotation (Rot) & Angle: 30\({}^{\circ}\), 35\({}^{\circ}\), 40\({}^{\circ}\), 45\({}^{\circ}\) \\ \hline \multirow{2}{*}{Lossy compression} & JPEG & Quality factor: 80–90 \\ \cline{2-3}  & JPEG2000 (JP2) & Compression ratio: 4.0–6.0 \\ \hline \multirow{3}{*}{Frequency filtering} & Low-Pass Filtering (LPF) & Cutoff frequency: 80 Hz \\ \cline{2-3}  & High-Pass Filtering (HPF) & Cutoff frequency: 30 Hz \\ \cline{1-1} \cline{2-3}  & Band-Stop Filtering (BF) & Stop band: 35–65 Hz \\ \cline{1-1} \cline{2-3}  & Homomorphic Filtering (HF) & n/a \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tested operation types and the corresponding parameter settings.  
Figure 4: Parameter setting for the number of epochs.  
As explained in Section 2.2, the pooling methods that are frequently used are mean pooling and max pooling. According to the respective results with mean pooling and max pooling, as shown in Figure 4, the max pooling always obtains a better result than the mean pooling. Therefore, we set the pooling type as max pooling in the following experiments. In addition to the number of epochs and the pooling type, the kernel size plays an important role in our method as well. By setting the number of iterations to 600, we tested the detection accuracies with different kernel sizes, \(3\times 3\), \(5\times 5\), and \(7\times 7\), and the detection results are 92.2%, 95.9%, and 91.7%, respectively. Therefore, we set the kernel size to \(5\times 5\) to achieve the highest accuracy.