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Image forgery analysis technique used for tampering detection.
Fausto Isaac Reyes Gonzalez
RENE ARMANDO CUMPLIDO PARRA
Acceso Abierto
Atribución-NoComercial-SinDerivadas
Superpixels
Splicing
Tampering detection
Images are found everywhere, from advertisements in printed media to the extensive information available over the Internet. However, it is not precisely known how much of this digital content is free of modification; thus it is important to have identification methods. Image Forgery detection techniques aim to identify if the media has any modification without prior information of the source, either identifying the false object or only assessing if the image is authentic or counterfeited. One of the most common alterations done to images is called splicing, which consists in cropping an object or region from an image and attach it to a different image in order to change the information present in the new source. There are several methods that address this attack, by dividing the image into blocks or regions to obtain characteristics in an effort to identify the inconsistencies caused by the attack. This features can be edges, blobs or characteristics in noise changes, illumination changes obtained throughout the image or in sections. In this work, we propose a method that uses a human perception model called Object Saliency Detection, that helps to assess the region where splicing occurs and later determine whether the region is part of the attack. Later, a Superpixel segmentation process helps us to identify the copied object to point out the splicing attack correctly.
Instituto Nacional de Astrofísica, Óptica y Electrónica
2018-06
Tesis de maestría
Inglés
Estudiantes
Investigadores
Público en general
Reyes González, F. I., (2018). Image forgery analysis technique used for tampering detection, Tesis de Maestría, Instituto Nacional de Astrofísica, Óptica y Electrónica
LENGUAJES DE PROGRAMACIÓN
Versión aceptada
acceptedVersion - Versión aceptada
Aparece en las colecciones: Maestría en Ciencias Computacionales

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