## 2 Multi-Feature Detection Method of Image Tampering
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **CCW Chroma** \\ **Detection Operator** \\ \end{tabular} & \begin{tabular}{c} **Sharpness Detection** \\ **Operator** \\ \end{tabular} & \
\begin{tabular}{c} **Salient Region** \\ **Detection Operator of** \\ **CCW and NSS** \\ \end{tabular} &
\begin{tabular}{c} **Combined Features** \\ **CCW** \\ \end{tabular} \\ \hline Tampering ImageNet & & & & & \\ (Ours) & 80.90\% & 80.30\% & 79.10\% & 91.00\% \\ Columbia & 79.59\% & 83.67\% & 88.44\% & 95.24\% \\ CASIA 2.0 & 85.47\% & 84.92\% & 85.03\% & 88.02\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The accuracy of the single feature and combined features. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Ref. [6]** & **Ref. [11]** & \begin{tabular}{c} **Ref. [11]** \\ **SVM** \\ \end{tabular}  & \begin{tabular}{c} **Ch +** \\ **CNN** \\ \end{tabular}  & \begin{tabular}{c} **Ch +** \\ **BPNN** \\ \end{tabular}  & \begin{tabular}{c} **Ch +** \\ **SVM** \\ \end{tabular}  & \begin{tabular}{c} **Cr +** \\ **CNN** \\ \end{tabular}  & \begin{tabular}{c} **Cr +** \\ **CNN** \\ \end{tabular}  & \begin{tabular}{c} **Cr +** \\ **BPNN** \\ \end{tabular}  & \ \begin{tabular}{c} **Ref. [8]** \\ **ResNet** \\ \end{tabular}  & \begin{tabular}{c} **Ref. [13]** \\ **Proposed** \\ \end{tabular}  \\ \hline Tampering & & & & & & & & & & \\ (Ours) & 62.60\% & 73.70\% & 67.20\% & 59.00\% & 72.00\% & 66.80\% & 63.00\% & 71.10\% & 74.30\% & 91.00\% \\ Columbia & 85.71\% & 79.60\% & 93.20\% & 88.40\% & 77.55\% & 84.40\% & 81.60\% & 79.59\% & 81.00\% & 70.10\% &



Table 4 shows that compared with the methods reported in the existing literature, the feature extraction and classification model proposed in this paper performs better in terms of the precision, recall, and F1-score evaluation metrics. It can be seen from Tables 3 and 4 that the algorithm proposed in this paper shows an outstanding performance on the three different public benchmark testing datasets, indicating that the introduced algorithm has promising applicability in various real-life fields related to image tampering detection. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Ref. [12]**} & \multicolumn{6}{c}{**Ref. [11]**} & \multirow{2}{*}{**Ref. [8]**} & \multirow{2}{*}{**Ref. [13]**} & \multirow{2}{*}{**Proposed**} \\ \cline{4-11}  & & & & & **Cb +** & & **Cb +** & & **Cr +** & & **Cr +** & & **Cr +** \\  & & & & **SVM** & **KNN** & **BPNN** & **SVM** & **KNN** & **BPNN** & & & \\ \hline Tampering & Precision & 73.58\% & 41.59\% & 52.48\% & 62.24\% & 57.58\% & 50.49\% & 61.45\% & 50.25\% & 41.89\% & 67.62\% & 86.76\% \\ ImageNet & Recall & 79.43\% & 44.07\% & 54.69\% & 73.80\% & 75.95\% & 49.30\% & 69.08\% & 66.49\% & 35.04\% & 70.15\% & 92.83\% \\ (Ours) & F1 score & 0.76 & 0.41 & 0.53 & 0.67 & 0.64 & 0.49 & 0.65 & 0.56 & 0.37 & 0.69 & 0.90 \\  & Precision & 77.14\% & 26.56\% & 14.85\% & 42.35\% & 39.29\% & 6.55\% & 29.72\% & 21.43\% & 24.52\% & 2.38\% & 90.00\% \\ Columbia & Recall & 49.05\% & 30.27\% & 30.27\% & 52.02\% & 26.43\% & 16.80\% & 39.76\% & 6.43\% & 26.46\% & 3.57\% & 66.67\% \\  & F1 score & 0.67 & 0.26 & 0.19 & 0.45 & 0.30 & 0.09 & 0.31 & 0.10 & 0.25 & 0.03 & 0.80 \\  & Precision & 86.49\% & 18.62\% & 48.09\% & 58.18\% & 28.16\% & 44.54\% & 61.64\% & 24.35\% & 14.39\% & 32.13\% & 76.60\% \\  & Recall & 57.21\% & 69.37\% & 55.14\% & 62.89\% & 31.87\% & 59.07\% & 62.13\% & 32.96\% & 28.17\% & 35.27\% & 61.20\% \\
2.0 & F1 score & 0.67 & 0.29 & 0.50 & 0.60 & 0.32 & 0.50 & 0.62 & 0.25 & 0.19 & 0.34 & 0.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative results of the proposed method in terms of three evaluation metrics, with a comparison to existing related approaches. Figure 12: Images detected successfully by the proposed algorithm but failed to be detected by the comparison algorithm in the dataset.