## 2 Multi-Feature Detection Method of Image Tampering
As can be seen from Figure 7e–h, when the chroma of the untampered image changes greatly, the CCW chroma detection operator will obtain a clearer edge of the chroma changes, which is similar to the expected tampered area edge,

\[\text{Ac}_{(K)}=\frac{1}{K}\sum_{i=1}^{K}\text{Ac}_{i}\]1 Examples of the sharpness 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**) the sharpness detection results of the untampered images; (**c,g**) tampered images; (**d,h**) the sharpness detection results of the tampered images. and may cause misjudgment. For the tampered image with no clear chroma difference between the tampered region and other regions, the detection operator failed to detect its tampered edge, so the classification algorithm will classify it as untampered. As shown in Figure 8a-d, when there are focusing and defocusing regions in the untampered image due to the range of the depth of the field, the sharpness detection operator can detect the focusing region, as shown in Figure 8b. For tampered images, when there are multiple focusing regions at different depths of field positions, the results given by the sharpness detection operator will show two clear regions separated by a certain distance, as shown in Figure 8d. It can also be seen from Figure 8e-h that when there is no obvious change in the depth of field in the untampered image and the overall image has high sharpness, or when there are multiple objects on the focal plane, the results given by the sharpness detection operator will be scattered into multiple clear regions, as shown in Figure 8f. At the same time, for different depths of the field, when the background is blurred, a foreground with extreme sharpness will also lead to detection errors. For tampered images, when one or more of the following conditions exist, the results given by the sharpness detection operator will be confused with the detection results of untampered images, namely, the focusing region of the untampered image is not obvious, or the introduced tamper region is not the focusing region of another image, or the tampered area overlaps with the focusing area of the pre-tampered image. As the results obtained by NSS in Section 2.3 are statistical features and cannot be displayed intuitively, only the results of the CCW salient region detection operator are shown in \[\text{Ac}_{(K)}=\frac{1}{K}\sum_{i=1}^{K}\text{Ac}_{i}\]2 Figure 9a-f illustrate the fact that the saliency detection operator can divide the image into ROI and RONI, and the tampered region exists in one of the two regions due to its relatively consistent brightness and structural complexity, thus facilitating subsequent NSS feature extraction. From Figure 9g-i, it can also be seen that there may be brightness changes or structure complexity changes within the tampered area, or there may be multiple tampered areas. As a result, the brightness and structure complexity of some areas in the tampered region are similar to those in the untampered area. At this time, the tampered region will appear in both ROI and RONI, thus eventually leading to incorrect classification results. The above analysis indicates that a single feature may not extract full features or over-extract a single class of details, which may lead to incorrect classification results. Moreover, it is quite common that one feature detection fails while the other two succeed, as shown in \[\text{Ac}_{(K)}=\frac{1}{K}\sum_{i=1}^{K}\text{Ac}_{i}\]3

Table 1 shows the classification results based on the extracted features of the images shown in Figure 10 using the CCW chroma detection operator, sharpness detection operator, salient region detection operator, and NSS. Figure 10a,b show examples of the CCW chroma detection operator classification failure and sharpness detection operator and NSS classification success.