Open AccessHybrid Algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors
Author(s) -
Jimmy Alexander Cortés Osorio,
José Andrés Chaves Osorio,
Cristian David López Robayo
Publication year - 2021
Publication title -
revista facultad de ingeniería universidad de antioquia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.16
H-Index - 12
eISSN - 2422-2844
pISSN - 0120-6230
DOI - 10.17533/udea.redin.20211165
Subject(s) - computer science , resampling , pixel , artificial intelligence , digital image , robustness (evolution) , computer vision , digital forensics , pattern recognition (psychology) , image (mathematics) , image processing , computer security , biochemistry , chemistry , gene
Today, image forgery is common due to the massification of low-cost/high-resolution digital cameras, along with the accessibility of computer programs for image processing. All media is affected by this issue, which makes the public doubt the news. Though image modification is a typical process in entertainment, when images are taken as evidence in a legal process, modification cannot be considered trivial. Digital forensics has the challenge of ensuring the accuracy and integrity of digital images to overcome this issue. This investigation introduces an algorithm to detect the main types of pixel-based alterations such as copy-move forgery, resampling, and splicing in digital images. For the evaluation of the algorithm, CVLAB, CASIA V1, Columbia, and Columbia Uncompressed datasets were used. Of 7100 images evaluated, 3666 were unaltered, 791 had resampling, 2213 had splicing, and 430 had copy-move forgeries. The algorithm detected all proposed forgery pixel methods with an accuracy of 91%. The main novelties of the proposal are the reduced number of features needed for identification and its robustness for the file format and image size.