## 2 Multi-Feature Detection Method of Image Tampering
In the Columbia dataset [17], 117 untampered images and 30 tampered images were selected for detection, and the image size was 1152 \(\times\) 768 or 768 \(\times\) 1152. Part of the images is shown in Figure 5. In CASIA ITDE V2.0 [18], 6427 untampered images and 1330 tampered images were selected for detection, and the image sizes were all 384 \(\times\) 256 or 256 \(\times\) 384. Part of the images is shown in Figure 6. It is worth mentioning that Columbia [17] and CASIA ITDE V2.0 [18] are already existing public benchmark datasets that were specially built for image tampering detection tasks. Since these datasets are also employed by the related methods, to ensure a fair comparison, in this study, they were also adopted without changing their content nor size, and thus keeping their original amount and ratio of untampered vs. tampered images the same. Whereas Tampering ImageNet is a novel dataset built during this research work, and it is named Tampering ImageNet because its entire untampered content was drawn from the existing image classification public ImageNet dataset [16], which was resized and manually prepossessed to have an untampered vs. tampered images ratio of one. As the untampered vs. tampered images ratio and the content size already differ from one existing public tampering detection dataset to another, our newly built dataset is not supposed to have the same size and ratio. In contrast, the introduced novel dataset was built to overcome the shortcomings of the existing public datasets, and it is a well-balanced dataset built with an untampered vs. tampered images ratio equal to one and with the minimum possible content size to achieve the maximum possible tampering detection performance. The experiments were implemented using toolboxes and functions in MATLAB R2018a (Math Works, Inc., Natick, MA, USA). The three-feature extraction operators proposed in Section 2 were applied to untampered and tampered images, respectively. The results obtained by the complementary color detection operator are shown in Figure 7, the results obtained by the sharpness detection operator are shown in Figure 8, and the results obtained by the CCW salient region detection operator are shown in Figure 9.

Figure 4: Examples of the images in Tampering ImageNet. (**a**,**b**) Untampered images; (**c**,**d**) tampered images. Figure 5: Examples of the images in Columbia dataset. (**a**,**b**) Untampered images; (**c**,**d**) tampered images. Figure 6: Examples of the images in CASIA ITDE V2.0. (**a**,**b**) Untampered images; (**c**,**d**) tampered images. Figure 7: Examples of the CCW-based chroma detection results, where (**a**–**d**) are the images with correct classification, and (**e**–**h**) are the images with failed classification. (**a**,**e**) Untampered images; (**b**,**f**) chroma detection results of untampered images using CCW; (**c**,**g**) tampered images; (**d**,**h**) chroma detection results of tampered images using CCW. \[\text{Ac}_{(K)}=\frac{1}{K}\sum_{i=1}^{K}\text{Ac}_{i}\]0 Examples of the CCW salient map results, where (**a–f**) are the images with correct classification, and (**g–i**) are the images with failed classification. (**a**) Untampered image; (**b**) the ROI of the untampered image; (**c**) the RONI of the untampered image; (**d,g**) tampered images; (**e,h**) the ROI of the tampered image; (**f,i**) the RONI of the tampered image. From Figure 7a–d, it can be observed that the CCW chroma detection operator can extract the full edges of chroma changes in the tampered area when the chroma difference between the tampered area and other areas is detected. However, for untampered images with insignificant chroma changes, the detected edges of the chroma changes do not constitute closed shapes.