## 2 Multi-Feature Detection Method of Image Tampering
By taking all these facts into consideration, the proposed random forest model consists of three binary classifiers (voters) instead of two, where each of them takes one of the three extracted features as an input and delivers a class label as an output, and then the majority voting policy is applied to determine the final classification decision. We first use the accuracy percentage as the evaluation index to objectively evaluate the performance of the different algorithms. Table 2 shows the classification results obtained by exploiting each of the three image features separately, and the classification results achieved by combining them into one batch and feeding them simultaneously into the proposed model. The final classification decision is achieved by applying the majority voting policy on the model three voters' (classifiers) outputs. Table 3 shows the accuracy comparison between the proposed method in this paper and the existing algorithms in the literature. Figure 11: The out-of-bag error over the number of grown classification trees. The results in Table 3 show that the proposed algorithm can greatly improve the accuracy of image tampering detection. This is because the superpixel segmentation and complementary color operator in this algorithm can extract chroma features from the chroma information of the image and distinguish the untampered region from the tampered ones. The sharpness detection operator can extract more detailed sharpness characteristics, to reflect the sharpness change between different regions. In addition, the saliency detection and texture distortion statistics extracted by NSS can make up for the shortcomings of the complementary color and sharpness detection operators. The combination of these three features can further improve the accuracy of image tampering detection. In Tampering ImageNet, Columbia dataset, and CASIA ITDE V2.0, the images that were detected successfully by the algorithm in this paper and the comparison algorithm that failed are shown in Figure 12.

Figure 12 shows that for images with complex textures, the single feature extracted by the comparison algorithm lacks chroma information and location information. However, the algorithm in this paper integrates chroma feature, sharpness feature, and texture distortion statistics, enabling it to detect tampered images that cannot be recognized by relying on a single feature. The images that cannot be correctly classified by any of the algorithms listed in Table 3 are shown in Figure 13.

The results in Figure 13 indicate that when there are multiple factors causing detection errors in untampered or tampered images, e.g., great chroma variations in untampered images or slight chroma changes in tampered images, insignificant differences in the depth of the field, and high overall sharpness with no obvious focusing area, neither the single-feature nor the multi-feature algorithm can provide a correct classification. On the other hand, the accuracy, as the most commonly used indicator, cannot reasonably reflect the classification ability of the model when the number of samples in different categories of the dataset is unbalanced. Therefore, the precision, recall, and F1-score metrics are also employed for efficient model evaluation. Each fold in the cross-validation is calculated separately and the average is taken as the final result. The proposed method's performance is compared to existing related approaches in terms of the other three different evaluation metrics. The quantitative results are shown in Table 4.