# Detection of Tampering by Image Resizing Using Local Tchebichef Moments
## 1 Introduction  
As image editing tools and various mobile devices are easily acquired and conveniently used, maximizing the viewing experience of end users on small devices becomes very important. Compared to traditional image re-targeting methods, such as linear scaling and cropping, many content-aware image resizing methods can preserve salient areas, avoiding serious distortions or loss of significant information[1; 2; 3]. Meanwhile, many content-aware resizing algorithms have been adopted using image editing tools, such as photoshop and GIMP. An ordinary user can very easily create tampered images for malicious purposes using image editing tools. Moreover, it is impossible to distinguish those tampered images from authentic images with the naked eye. Therefore, how to detect tampered images is a hot topic in the field of image content security.  
In recent years, a few approaches have existed about the detection of content-aware image re-targeting. Moreover, most of the detection methods are for the seam carved images. [PERSON] et al. adopted a forensic hash to distinguish whether an image is subjected to a seam carving operation [4]. However, it is an active forensics approach; moreover, a falsifier might remove the forensic hash. For passive forensic detection, [PERSON] et al. used 324D Markov features to detect image seam carving [5]. Later,Fillion et al. exploited a series of intrinsic features to expose the trace of seam carved images [6]. In [PERSON] et al. [7], an approach based patch analysis was adopted to distinguish whether an image is original or not. According to noise and energy distribution in seam carved images, [PERSON] et al. [8] exploited the features based on noise and energy bias to detect seam carved images. Local binary pattern (LBP) was adopted to detect seam carved images [9] in our recent work. Inspired by image entropy with the ability of capturing the intrinsic information of an image, we exploited multi-scale spectral and spatial entropies to detect seam carved images with low resizing ratios [10]. Web Local Descriptor (WLD) and LBP were adopted to distinguish whether an image is original or not [11]. In [12], a large feature mining approach was proposed to detect image seam carving under recompression in joint photographic experts group (JPEG) images.  
However, most existing detections of image resizing are designed for a specific content-aware resizing. Much less has been done to distinguish different content-aware resizing approaches in the process of image re-targeting. In practice, the best re-targeting method relies on an image itself. For example, scaling images in horizontal or vertical direction can be performed in real time using interpolation and will preserve the global visual effects and retarget images with medium perceptual quality. However, scaling will introduce some shape deformation into the retarget image. Seam carving [1] supports various visual saliency measures for defining the energy of an image. Nevertheless, seam carving can excessively carve less important parts of an image and result in unwanted visual distortions. scale-and-stretch [3]can preserve the aspect ratios of local objects. However, if there are many quads in the image, the approach will fail to preserve the aspect ratio of the whole image [2]. Therefore, there are different image resizing methods depending on the image content to achieve image change in size while preserving the saliency region. it is necessary to propose a universal detection of image resizing.  
The rest paper is organized as follows: Section 2 summarizes several common methods of image re-targeting and analyzes their artifacts. Section 3 briefly introduces the proposed detection approach. Our experimental results are described and analyzed in detail in Section 4, and conclusions are made in Section 5.