<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0120-6230</journal-id>
<journal-title><![CDATA[Revista Facultad de Ingeniería Universidad de Antioquia]]></journal-title>
<abbrev-journal-title><![CDATA[Rev.fac.ing.univ. Antioquia]]></abbrev-journal-title>
<issn>0120-6230</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Ingeniería, Universidad de Antioquia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-62302022000400111</article-id>
<article-id pub-id-type="doi">10.17533/udea.redin.20211165</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Hybrid Algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors]]></article-title>
<article-title xml:lang="es"><![CDATA[Algoritmo híbrido mediante descriptores Markov y SIFT para la detección de la falsificación de imágenes]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cortés-Osorio]]></surname>
<given-names><![CDATA[Jimmy Alexander]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Chaves-Osorio]]></surname>
<given-names><![CDATA[José Andrés]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López-Robayo]]></surname>
<given-names><![CDATA[Cristian David]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Tecnológica de Pereira Facultad de Ciencias Básicas ]]></institution>
<addr-line><![CDATA[Pereira Risaralda]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<numero>105</numero>
<fpage>111</fpage>
<lpage>121</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-62302022000400111&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-62302022000400111&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-62302022000400111&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[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.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Hoy en día, la falsificación de imágenes es común debido a la masificación de las cámaras digitales de alta resolución y bajo costo, junto con la accesibilidad de los programas de computadora para el procesamiento de imágenes. Todos los medios de comunicación se ven afectados por este tema, lo que hace que el público dude de la noticia. Aunque la modificación de imágenes es un proceso común en el entretenimiento, cuando las imágenes se toman como evidencia en un proceso legal, la alteración no puede considerarse trivial. La ciencia forense digital tiene el desafío de garantizar la precisión y la integridad de las imágenes digitales para superar este problema. Esta investigación introduce un algoritmo para detectar los principales tipos de alteraciones basadas en píxeles, como copy-move, resamplig y splicing en imágenes digitales. Para la evaluación del algoritmo se utilizaron las bases de datos CVLAB, CASIA V1, Columbia y Uncompressed Columbia. Se evaluaron 7.100 imágenes, de las cuales 3666 eran auténticas, 791 tenían resampling, 2213 tenían splicing y 430 tenían falsificaciones de copy-move. El algoritmo detectó todas las alteraciones basadas en pixeles con una precisión del 91%. Las principales novedades de la propuesta son el reducido número de características necesarias para la identificación y su robustez al formato y tamaño de la imagen.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Copy-Move]]></kwd>
<kwd lng="en"><![CDATA[Markov]]></kwd>
<kwd lng="en"><![CDATA[Resampling]]></kwd>
<kwd lng="en"><![CDATA[SIFT]]></kwd>
<kwd lng="en"><![CDATA[Splicing]]></kwd>
<kwd lng="es"><![CDATA[Copiar-Mover]]></kwd>
<kwd lng="es"><![CDATA[Markov]]></kwd>
<kwd lng="es"><![CDATA[Remuestreo]]></kwd>
<kwd lng="es"><![CDATA[SIFT]]></kwd>
<kwd lng="es"><![CDATA[Empalme]]></kwd>
</kwd-group>
</article-meta>
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