# AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks
## 6 Conclusions  
This paper proposes a splicing forgery detection algorithm in which the image under analysis is resized and segmented into non-overlapping blocks of 16 \(\times\) 16 pixels. These blocks are then fed into a Siamese neural network for feature extraction, which is used in the K-means stage to detect duplicated areas. The proposed algorithm was evaluated using several public databases as well as a database developed by the author during this research. The evaluation results show that the proposed scheme provides higher accuracy than other previously proposed algorithms, being able to detect more than just splicing manipulations. The proposed method's primary advantage is its capability to detect and localize manipulated and duplicated areas, unlike many existing methods based on neural networks, which only identify whether an image has been manipulated without providing specific information about the affected areas. The system was assessed with six databases, providing an exhaustive evaluation with more images in different scenarios. Most existing methods are evaluated with a maximum of three databases, which is an advantage of the proposed method. The Realistic Tampering image database also consists of realistic image manipulations that are difficult to detect with the naked eye. In addition, our image database contains images in different conditions and were taken in different places with different cameras and in different environmental conditions, such as changes in lighting, image size, and times. Optimizing the computational complexity of the proposed method based on Siamese neural networks is crucial to improving its performance and efficiency. Some preprocessing techniques could be considered to optimize the performance of the proposed method in future work. PCA reduces the dimensionality of the data while maintaining as much variance as possible, which can decrease the computational processing cost without losing relevant information. Normalizing the data in a specific range, usually [0, 1], can improve the model's stability and performance during training. Using the halftone version of the image to normalize the data can help maintain the image's features of pixel intensity. This technique can generate more efficient learning in the neural network. Additionally, the development of future work related to the proposed method could focus on generalizing neural network training for robust feature learning by implementing algorithms that do not depend on a specific dataset or image, such as integrating image features in neural network feature maps to determine image manipulations. Neural network learning can be achieved through techniques that create more robust feature maps, eliminating the need to compare an altered image with the original image. On the other hand, less computationally complex neural network methodologies that provide high efficiency in detection and classification are necessary, thus improving overall model performance. Applying these techniques and approaches could create efficient and faster detectors of splicing manipulations. **Author Contributions:** Conceptualization, A.E.R.-R. and R.E.A.-A. ; methodology, M.C.-H.; software, M.N.-M.; validation, A.E.R.-R., R.E.A.-A. and H.P.-M.; formal analysis, H.P.-M. and M.N.-M.; investigation, A.E.R.-R. and R.E.A.-A. ; resources, M.N.-M.; data curation, M.C.-H.; writing--original draft preparation, A.E.R.-R. and R.E.A.-A. ; writing--review and editing, H.P.-M.; visualization, M.C.-H.; supervision, M.C.-H. and M.N.-M.; project administration, H.P.-M.; funding acquisition, M.N.-M. and H.P.-M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was founded by the National Council of Science and Technology, CONAHCyT, of Mexico and the National Polytechnic Institute of Mexico. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.