# Structural Correlation Based Method for Image Forgery Classification and Localization
## 2 Related Works  
In the literature, image splicing forgery detection problem has been addressed efficiently [2; 3; 4]. In recent years, a substantial attention has been paid to deep learning based approaches [5; 6] for localizing image splicing [7; 8; 9; 10; 11; 12; 13; 14; 15] wherein convolutional neural network (CNN) has been widely used [8; 9; 10; 11; 12]. [PERSON] et al. [8] extracted and employed features capturing characteristic traces from different camera models to localize a tampered mask by an iterative clustering algorithm. Region proposal network and condition random field are the main components of the model developed in [PERSON] et al. [10]. The noise levels difference between spliced and original regions was utilized to find the splicing traces [11; 13]. Non-linear camera response function was used in [PERSON] et al. [11] and was combined with noise level function to exploit the strong relationship between two functions to localize the forged edges using a CNN. [PERSON] et al. [12] used a similarity network and a CNN-based feature extractor to determine whether image patches contain different traces or being captured by different camera models. [PERSON] et al. [13] estimated the noise levels using the principal component analysis and then clustered using \(k\)-means algorithm to localize the spliced regions. [PERSON] et al. [15] utilized the gradient-based illumination descriptor to detect the illumination inconsistency and object color change, which helped localize image splicing traces. [PERSON] et al. [16] used gamma transformation to detect splicing forgery and localize spliced region by estimating the probabilities of sliding window based overlapping blocks being gamma transformed.  
CMF detection is the problem of detecting the tampered regions in copy-move images, is called CMF localization (CMFL) in this paper, to distinguish from CMF classification. CMFL has also been actively studied in many researches, which can be divided into three categories: block-based methods [17; 18; 19; 20; 21; 22], keypoints-based methods [23; 24; 25; 26; 27; 28], and segmentation-based methods [29; 30; 31; 32].  
Figure 1: Examples of image forgery in the top row, the corresponding host images are in the middle row, and the groundtruth images of forged regions are in the bottom row. Copy-move images are shown in (**a**,**b**), while spliced images are shown in (**c**,**d**).  
[PERSON] et al. [17] introduced the upsampled log-polar Fourier descriptor, which is invariant to rotation and scaling, to robustly detect various types of tampering attacks. [PERSON] et al. [18] proposed a two-branch deep neural network to detect potential manipulation via visual artifacts and visually similar regions, which helps specify the copied and pasted regions. [PERSON] et al. [19] used the scale space representation of scale-invariant feature transform (SIFT) to handle different geometric transformation. PatchMatch, an algorithm used to search for approximate nearest neighbors, was combined with Zernike moments to detect copy-move attacks in [23; 24] whereas SIFT was utilized in [25; 26; 27]. In segmentation-based approaches, the input image was semantically segmented into non-overlapped regions [29; 30; 31; 32], [PERSON] et al. [29] developed two stages of matching to detect the copy-move regions. Firstly the affine transformation matrix between segmented regions was roughly estimated and then iteratively refined by using an expectation-maximization algorithm-based probability model. However, the major disadvantage of this method is its high computational complexity. [PERSON] et al. [30] classified smooth regions and non-smooth regions (keypoint regions) to be apply two different techniques. On the one hand, SIFT was used in a keypoint-based method to detect forgery in non-smooth regions. On the other hand, Zernike moments were extracted in a block-based method to handle smooth regions. The CMFL was effectively performed by the fusion of above-mentioned techniques.