# AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks  
[PERSON], [PERSON], [PERSON]  
E-mail: {email: {email: {email: {[PERSON], [PERSON]. ;  
Pattern recognition is essential in image forensics, particularly for the detection of splicing manipulations within images. The development of effective splicing detection techniques and algorithms requires an understanding of the image and the ability to discern introduced image forgery distortions. This process analyzes and identifies specific visual features related to the presence of manipulations, such as inconsistencies in texture, lighting, or perspective. Advanced pattern recognition methods enhance the accuracy and reliability of splicing detection, ensuring the integrity of visual content. The image splicing technique involves duplicating an image region into another image, which has garnered significant attention in image forensics. The detection of this type of forgery has become a relevant area in guaranteeing image authenticity [5; 6; 7]. However, pattern recognition is essential in image forensics, particularly for detecting splicing manipulations within images. The identification of patterns allows for comparison between regions and helps determine the similarities between them. Additionally, using artificial intelligence systems to detect manipulated areas by learning the specific patterns and features of each image region could help identify the tampered areas. In this context, using Siamese neural networks as feature extractors allows image splicing manipulation detection systems to identify forgery areas with variations in scale, rotation, and non-linear transformations applied to the tampered regions [8; 9; 10; 11]. This paper introduces an algorithm that detects manipulated areas from splicing attacks based on the Siamese neural network. The algorithm, when combined with the K-means algorithm, significantly reduces the errors in splicing detection. The Siamese neural network identifies unique features for each image block, enabling the detection of manipulated regions. Using two identical branches for the Siamese neural network model allows the system to detect image manipulations effectively by comparing image blocks to discern similarities and discrepancies. Data augmentation is crucial for Siamese neural network training. This step enhances the pattern learning associated with image manipulation by applying geometric and signal-processing distortions to each block. Additionally, the K-means algorithm is employed for the related image regions detected as manipulated to realize a more precise forgery detection based on region similarities. The main contributions of this algorithm for detecting splicing-based forgery are as follows:  
1. The Siamese neural network extracts inherent features from each image block and facilitates learning complex and specific patterns, enhancing the efficiency of manipulated area detection. 2. Siamese neural networks learn invariant features from geometric and image processing attacks used in data augmentation, increasing the accuracy of splicing detection forgery. 3. Data augmentation enhances the algorithm's robustness against splicing attacks, providing a reliable defense against image manipulation. Data augmentation involves geometric distortions and image processing on each block to generate comprehensive representations of image regions, thus reducing underfitting and overfitting issues. A description of data augmentation is provided in Section 3.2.2, titled \"Siamese Neural Network Training\". 4. The K-means algorithm refines the splicing detection process by clustering image blocks, enabling more efficient forgery region detection. 5. The K-means algorithm is essential to reducing the error in image splicing detection. Pixels are clustered based on similarities; this method enhances the detection of manipulated regions since each region detected as manipulated is divided into smaller blocks to make the detection more precise and reduce the error.