# A New Method to Detect Splicing Image Forgery Using Convolutional Neural Network
## 4 Experimental Results
### The Results and Comparison over the CASIA 1.0, CASIA 2.0, and CUISDE Datasets  
Through the present study, we computed the F1-score for the proposed model and compared it with the other recently published approaches over the CASIA 1.0 [32], CASIA v2.0 [32], and CUISDE [33] datasets. The obtained results are specified in Table 5.  
Over CASIA 1.0, the proposed approach reveals high superiority in terms of the F1-score, which has achieved an F1-score value of 97.34% for [PERSON] et al. [10], 97.03% for [PERSON] et al. [35], 98.3% for [PERSON] et al. [20], and 61.0% for [PERSON] et al. [30], and the proposed model achieves an F1-score of 99.14%. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Sensitivity \%** & **Specificity \%** \\ \hline CASIA 1.0 & 98.29 & 100 \\ \hline CASIA 2.0 & 100 & 98.31 \\ \hline CUISDE & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sensitivity and specificity of the proposed model over CASIA 1.0, CASIA 2.0, and CUISDE dataset. Figure 4: Samples of datasets CASIA v1.0, CASIA v2.0, and CUISDE. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Classes** & **+** & \(-\) & **Total** \\ \hline \multirow{3}{*}{CASIA 1.0} & + & 115 & 0 & 115 \\ \cline{2-5}  & \(-\) & 2 & 110 & 112 \\ \cline{2-5}  & Total & 117 & 110 & 227 \\ \hline \multirow{3}{*}{CASIA 2.0} & + & 1850 & 22 & 1872 \\ \cline{2-5}  & \(-\) & 0 & 1280 & 1280 \\ \cline{2-5}  & Total & 1850 & 1302 & 3152 \\ \hline \multirow{3}{*}{CUISDE} & + & 45 & 0 & 45 \\ \cline{2-5}  & \(-\) & 0 & 45 & 45 \\ \cline{1-1} \cline{2-5}  & Total & 45 & 45 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrices of the proposed model over CASIA 1.0, CASIA 2.0, and CUISDE dataset. Over CASIA 2.0, our approach reveals high superiority in terms of the F1-score, which has achieved an F1-score value of 97.64% for [PERSON] et al. [10], 98.08% for [PERSON] et al. [35], 97.52% for [PERSON] et al. [20], 86.67% for [PERSON] et al. [29], 95.4% for [PERSON]. [PERSON] et al. [28], and 98% for [PERSON] et al. [22], and the proposed model achieves an F1-score of 99.4%. Figure 5: Feature map for a forgery image from a CASIA 1.0 dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**CASIA 1.0**} & \multicolumn{3}{c}{**CASIA 2.0**} & \multicolumn{3}{c}{**CUISDE**} \\ \cline{2-10}  & **Recall \%** & **Precision \%** & **F1-Score \%** & **Recall \%** & **Precision \%** & **F1-Score \%** & **Recall \%** & **Precision \%** & **F1-Score \%** \\ \hline [PERSON] et al. [10] & 98.2 & 96.75 & 97.34 & 96.84 & 98.45 & 97.64 & 97.07 & 98.3 & 97.68 \\ \hline [PERSON] & 98.99 & 95.14 & 97.03 & 99.03 & 97.14 & 98.08 & - & - & - \\ \hline [PERSON] et al. [20] & 100 & - & 98.3 & 100 & - & 97.52 & - & - & - \\ \hline [PERSON] et al. [30] & 66.0 & 67.0 & 61.0 & - & - & - & 66.0 & 67 & 66.0 \\ \hline [PERSON] et al. [29] & - & - & - & 88.93 & 89.12 & 86.67 & 91.76 & 99.81 & 94.98 \\ \hline [PERSON] et al. [28] & - & - & - & 94.15 & 96.69 & 95.4 & - & - & - \\ \hline [PERSON] et al. [22] & - & - & - & 99 & 97 & 98 & - & - & - \\ \hline [PERSON] et al. [23] & - & - & - & - & - & - & 93.99 & 89.58 & 91.73 \\ \hline Proposed method & 100 & 98.3 & 99.14 & 98.83 & 100 & 99.4 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A comparison of F1-score, precision, and recall in the case of the proposed model and other recently published approaches over the CASIA 1.0, CASIA 2.0, and CUISDE dataset. Over the CUISDE, the presented approach reveals high superiority in terms of the F1-score, which has achieved an F1-score value of 97.68% for [PERSON]. [PERSON] et al. [10], 66.0% for [PERSON] et al. [30], 94.98% for [PERSON] et al. [29], and 91.73% for [PERSON] et al. [23], and the proposed model achieves an F1-score of 100%. Additionally, the time of our model was compared to those in these recently published papers, as shown in Table 6, which achieved a speed of 156 s for [PERSON] et al. [10], 143 s for [PERSON] et al.