## 1 Introduction  
The internet connectivity revolution, and the prevalence of camera-enabled devices, has resulted in the production of a massive daily amount of digital data. Among these digital data, images are one of the most important data categories that influence people's daily life. Based on its credibility, an image is regarded as one of the most common sources of evidence that plays a vital role in many fields [1]. In general, people or objects are unlikely to appear in the digital image if they do not exist in the scene of the photo. However, stored digital images are vulnerable to tampering, especially when an image editing software is available [2]. It is easy to copy a region in one image to another using the image editing tool. This kind of tampering is known as image stitching. The detection of whether the image content has been tampered with using stitching is significant in the fields of forensic investigation, criminal investigation, news, and surveillance systems [3, 4]. When the image is tampered with stitching, it will inevitably lead to changes in the chroma, illuminance, and texture of the image [5]. By detecting the changes in these features, stitching tampering can be detected. In terms of chroma feature detection, [PERSON] et al. adopted the gray-level co-occurrence matrix (GLCM) of an edge image in the Cr channel as the ground feature of judgement [6], but its feature extraction was simply carried out for a single channel, and the edge detection algorithm used was only the subtraction of adjacent pixels. [PERSON] et al. used the GLCM of the edge information in both Cb and Cr channels as the decisive feature [7]. The Haar-like features of the integral images of Y, Cb, and Cr channels were used as pivotal features in [8]. These methods are sensitive to chroma changes so that the drastic chroma changes of image edges without tampering will also significantly affect the detection performance of these algorithms. In terms of illuminance feature detection, the shape, illumination, and reflection of an image are used to estimate the light environment from the face region in the image [9]. However, the acquisition of such information requires a priori information in the scene. At the same time, the algorithm cannot detect image tampering if no face can be found in the image. Image tampering detected by the color illuminant features of an image is reported in [10]. Unfortunately, it suffers from significant performance loss in complicated illuminance [5]. In terms of texture feature detection, tampering detection through comparison of the gray-level run length matrix (GLRLM) of tampered and untampered images was conducted in [11], but it ignored the spatial information. Histogram features encoded by the local binary pattern (LBP) to reveal image stitching forgery were employed in [12]. However, it is not robust against image regions with little intensity variation and thus is unstable when noise is present. The 'find gray-level regional maxima' and the 'entropy-based edge' extracted from the local entropy of the median filter residual (MFR) in images for tampering detection are presented in [13]. However, it is known that the texture features obtained by GLRLM, LBP, and MFR local entropy are easily affected by illumination and reflection. Furthermore, all of them ignore the chroma change in an image. Through the literature review above, it can be found that stitching tampering of an image will usually introduce changes in features, such as the chroma, illumination, and texture. In addition, the tampered area will interfere with the smoothness, consistency, continuity, regularity, and periodicity of the original image [14]. Thus, chroma feature detection with chroma channels only or texture detection with gray-scale images only cannot fully reflect the feature changes in the original image caused by stitching tampering.