## 2 Multi-Feature Detection Method of Image Tampering
Figure 10c,d show examples of the sharpness detection operator classification failure and CCW chroma detection operator and NSS classification success. Figure 10e,f show examples of NSS classification failure, which also, on the other hand, demonstrates the CCW chroma detection operator and the sharpness detection operator classification success. The results show that combining features can obtain chroma, sharpness changes, and texture distortion of color images, and provide a new and more comprehensive feature extraction and information recognition that cannot be obtained by a single feature. It also partially relieves the difficulties of single feature classification. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **(a)** & **(b)** & **(c)** & **(d)** & **(e)** & **(f)** \\ \hline CCW chroma detection operator & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Sharpness detection operator & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\ Salient region detection operator & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) \\ of CCW and NSS & & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The single feature detection results of the image in Figure 10.

Using the random forest classifier and K-fold cross-validation method, the three features can be classified. The number of feature variables selected at random for each decision split is equal to the square root of the total number of variables for classification. The maximum depth of the tree is not limited, and then the nodes are expanded until all leaves are pure or until all leaves contain less than the minimum number of samples required to split an internal node, which is set to 2. The minimum number of observations per tree leaf is 1. The number of decision trees in the random forest affects the classification performance of the algorithm. Figure 11 shows the out-of-bag error over the number of grown classification trees. Figure 10: The extracted features of the proposed algorithm. From left to right are the color images, the chroma detection results of CCW, the results of the sharpness detection operator, and the ROI and RONI given by the CCW salient map. (**a,c,e**) Untampered images; (**b,d,f**) tampered images. See Table 1 for details. It can be seen from Figure 11 that the out-of-bag error trends to decrease and become stable as the number of grown trees increases. Moreover, it is straightforward that the time consumption increases with the number of grown trees. Therefore, considering the stability, accuracy, and time consumption of the classification, the number of decision trees of the random forest is set to 174 during the testing phase. For Tampering ImageNet and CASIA ITDE V2.0, K is set to 10, and for the Columbia dataset, K is set to 7. In the judgment stage, this paper uses three classifiers to determine the output category of a given test sample, and votes to decide whether the picture has been tampered with according to the simple majority rule [34]. Knowing that the majority voting policy [34] can only be performed with an odd number of voters, and since the number of extracted image features is equal to the number of voters (classifiers), this paper does not experiment with two extracted features, as two is an even number, which violates the majority voting policy. If the number of features to be classified is an even number, this implies that the random forest model will also consist of the same even number of classifiers (voters), which will lead to an undesirable classification case where half of the total number classifiers will vote that the input image has been tampered with and the second half will vote that it has not been untampered with. As a result, this is not a majority voting case but an equal voting, and based on this voting, we cannot say whether the image has been tampered with, and thus a final classification decision cannot be made.