Hybrid Algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors

Authors

DOI:

https://doi.org/10.17533/udea.redin.20211165

Keywords:

Copy-Move , Markov, Resampling, SIFT, Splicing

Abstract


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.

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Author Biographies

Jimmy alexander Cortés Osorio, Universidad Tecnológica de Pereira

PhD degree in engineering

José Andrés Chaves Osorio, Universidad Tecnológica de Pereira

M.S. degree in Physical instrumentation , PhD in engineering

Cristian David López Robayo, Universidad Tecnológica de Pereira

M.S. degree in Physical instrumentation

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Published

2021-11-02

How to Cite

Cortés Osorio, J. alexander, Chaves Osorio, J. A. ., & López Robayo, C. D. (2021). Hybrid Algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors. Revista Facultad De Ingeniería Universidad De Antioquia. https://doi.org/10.17533/udea.redin.20211165

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