In the last decade, the development and accessibility of image editing software have significantly increased, with many open-access tools available online. In this context, image-splicing attacks have gained popularity for their ability to manipulate images effectively. Consequently, there is a need to develop technologies capable of detecting splicing attacks using artificial intelligence systems. This paper introduces a method based on neural networks that identifies unique image features for detecting and localizing splicing manipulations by comparing the extracted features. The proposed approach used a neural network model with two identical branches to extract and compare features from the original and tampered images. Subsequently, the detected manipulated regions are divided into smaller blocks, and eigenvalues associated with each region are computed and compared with those of the original image. This process enables more efficient splicing detection. Experimental results demonstrate the method’s effectiveness against various splicing attacks, achieving a precision and efficacy of 90%. This study offers an innovative system for detecting and locating splicing image forgeries supported by comprehensive experimental validation.