Nowadays, image forgery is frequent due to the massification of digital cameras, their low cost, and their high resolution, along with the accessibility for computer programs for image processing. All media is affected by this issue that makes the public doubt on the news. Although image modification is a typical process in entertainment, when these are taken as evidence in a legal process, they cannot be considered in the same trivial way. Digital forensics has the challenge of ensuring the accuracy and integrity of the digital images to overcome this issue. This research introduces an algorithm to detect the main types of pixel-based alterations such as Copy-move, Resampling, and Splicing in digital images. For the evaluation of the Algorithm, CVLAB, CASIA V1, Columbia, and Uncompressed Columbia databases were used. Seven thousand one hundred images were evaluated, of which 3666 were ground truth, 791 had Resampling, 2213 had Splicing, and 430 had Copy-move forgery. The Algorithm could detect all proposed forgery pixel methods with an accuracy of 91%. The main novelties of the proposal were the reduced number of features needed for identification and its robustness to the file format and image size.