## 2 Multi-Feature Detection Method of Image Tampering  
An overall diagram of the proposed stitching tampering detection algorithm is shown in Figure 1, whereas Algorithm 1 is its descriptive code script. The algorithm consists of three parallel image feature extraction branches followed by a feature classification module. Firstly, the use of super pixel division makes the pixels with similar characteristics become a more representative \"element\" while preserving the edge information, and the complementary color chroma detection operator is applied to obtain the chroma feature of the image. Secondly, through the sharpness detection, the algorithm extracts the sharpness of different areas of the image to obtain the characteristics of the sharpness change of the image. Then, using salient region detection, the algorithm divides the image into two regions, and extracts their statistical features with the help of NSS to obtain the texture difference and distortion characteristics of the image. Finally, the random forest classification algorithm is used to classify the extracted features, and the three extracted features are classified separately. The classification results of untampered and tampered images are obtained by majority voting. ``` Input: Image dataset \(D\) Output: Image classification result \(R\) function Chroma (\(D\)) for each image \(I\)\(\in\)\(D\)do  Perform super pixel division  Calculate the complementary color coefficient \(f_{j}(r,c)\), which is defined in Equation (5) return image chroma coefficient \(C/\) / see Section 2.1 function Sharpness (\(D\)) for each image \(I\)\(\in\)\(D\)do  Compute the gradient \(S(x,y)\) of image \(I\) at point \((x,y)\)  Compute the sharpness Ten as described in Equation (9) return gradient value \(S//\)see Section 2.2 function NSS (\(D\)) for each image \(I\)\(\in\)\(D\)do  Obtain the image model CCNSal of the statistical saliency of complementary color natural scenes as shown in Equation (11)  Extract the statistical features of natural scenes return statistical results \(N\) of NSS//see Section 2.3 procedure RandomForest (\(D\)) \(C\gets Chroma(D)\) \(S\gets Sharpness(D)\) \(N\gets NSS(D)\)  Perform k-fold random forest classification for each feature set \(C\), \(S\), and \(N//\)see Section 2.4  Obtain the classification result \(R\) by majority voting

### Complementary Color Wavelet Chroma Detection Operator

In traditional chroma detection, the R, G, and B channels in a color image are usually processed as individual grayscale images, and then the processing results of each channel are fused [19; 20]. Such approaches ignore the mutual information between the three channels. CCW not only retains the chroma characteristics of each channel of the color image, but also further considers the information among the channels, thus providing a more effective means for chroma detection [15]. CCW is a color image processing tool based on the theory of complementary color [15]. According to the theory of complementary color, the cone cells in human eyes are sensitive to four pairs of complementary colors, namely red-cyan, green-magenta, blue-yellow, and white-black [21; 22]. A common method used to represent complementary colors is the Hue ring. The complementary colors of red-cyan, green-magenta, and blue-fellow form the three color axes of the Hue ring. The corresponding angles of R, G, and B on the Hue ring are 0, \(2\pi/3\), and \(4\pi/3\), respectively, and the phase difference between R, G, B, and their respective complementary color is \(\pi\). In [15], a cluster of CCW \(\psi^{\theta_{0}}\), \(\psi^{\theta_{0}+2\pi/3}\), \(\psi^{\theta_{0}+4\pi/3}\) with a phase difference of \(2\pi/3\) was constructed by imitating the relationship among the R, G, and B axes in the Cartesian coordinate system.