# AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks
## 2 Previously Reported Algorithms  
This section presents context related to image splicing manipulation detection through a review of different proposed techniques, highlighting the importance of developing these algorithms in digital image forensics. Traditional techniques for detecting splicing forgery in images are based on comparison regions using image processing techniques and pattern analysis. [PERSON] and [PERSON] [12] proposed a splicing detection method based on superpixel image segmentation and difference assessment using the mean-squared error (MSE) from image denoising. Subsequently, the K-means algorithm clusters the MSE values to detect image manipulations. [PERSON] et al. [13] applied the Discrete Cosine Transform (DCT) to extract global features from the gray-level run length matrix, enabling pixel intensity measurement, and employed a support vector machine (SVM) to detect splicing manipulations. [PERSON] et al. [14] detected homogeneous features using the Histogram of Gaussian (HoG), the Discrete Wavelet Transform (DWT), and the Local Binary Pattern (LBP). These techniques were employed to remove correlated features, and the SVM was utilized to determine manipulated image areas. [PERSON] and [PERSON] [15] estimated the noise level for each block using the SLIC algorithm. Then, they applied the K-means algorithm to cluster similar regions based on this noise level estimation. This approach aims to group regions that exhibit similar features, which can indicate tampering or manipulation in the image. [PERSON] et al. [16] delineated the boundaries of spliced regions using an SVM and precisely localized them. Therefore, they applied Connected Component Labeling (CCL) to further refine and identify spliced regions. [PERSON] et al. [17] applied the SVM, the Twin Support Vectorial Machine, and an Artificial Neural Network to classify image regions and determine manipulated regions. [PERSON] et al. developed a method for detecting image manipulations [18]. They used the characteristics of coefficients related to the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform of the image chrominances, referring to the YCbCr color space. From the DCT and DWT coefficients, they generated statistical moments to correlate the image pixels in their frequency space and determine the existing changes in the image. On the other hand, developing techniques for detecting splicing based on deep learning creates more efficient methods due to the learning of specific image features. [PERSON] et al. [19] proposed a localization and splicing detection algorithm that uses a Deep Matching and Validation Network (DMVN) to carry out both actions simultaneously. The algorithm uses a VGG16 model to detect a possible matching region, using cross-correlation and extracting the potential splicing areas with a deconvolutional module to extract a response map. [PERSON] et al. [20] developed an image splicing and copy-move detection method using recoloring. The authors resized the image to 128 \(\times\) 128 pixels, then created a model with 13 layers (three layers, nine convolutional layers, and a flattering layer) that contains 512 feature maps. In addition, they used an SIFT algorithm to find critical areas in the image and used image clustering to identify the manipulated areas. [PERSON] and [PERSON] [2] present a DCNN model that can locate manipulations using the residual network ResNet-50 to obtain an image feature map to determine the manipulated zones. [PERSON] et al. [21] proposed a block-based method. They realized data augmentation by applying some distortions (scaling, image normalization) to the image in the HSV (Hue, Saturation, Value) color model. Therefore, a filter was applied to remove noise and enhance edges, and the region of interest (manipulated zone) was detected. Subsequently, a convolutional Siamese neural network was used to extract a feature map to classify the detected area. [PERSON] et al. developed a double dual Siamese neural network [22].