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http://infotec.repositorioinstitucional.mx/jspui/handle/1027/523| Copy-move forgery detection technique based on discrete cosine transform blocks features | |
| ESTEBAN ALEJANDRO ARMAS VEGA EDGAR GONZALEZ FERNANDEZ ANA LUCILA SANDOVAL OROZCO Luis Javier García Villalba | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1007/s00521-020-05433-1 | |
| https://link.springer.com/article/10.1007/s00521-020-05433-1#citeas | |
| Reprografía Matemáticas discretas Aplicaciones web | |
| With the increasing number of software applications that allow altering digital images and their ease of use, they weaken the credibility of an image. This problem, together with the ease of distributing information through the Internet (blogs, social networks, etc.), has led to a tendency for information to be accepted as true without its veracity being questioned. Image counterfeiting has become a major threat to the credibility of the information. To deal with this threat, forensic image analysis is aimed at detecting and locating image forgeries using multiple clues that allows it to determine the veracity or otherwise of an image. In this paper, we present a method for the authentication of images. The proposed method performs detection of copy-move alterations within an image, using the discrete cosine transform. The characteristics obtained from these coefficients allow us to obtain transfer vectors, which are grouped together. Through the use of a tolerance threshold, it is possible to determine whether there are regions copied and pasted within the analysed image. The results obtained from the experiments reported in this paper demonstrate the effectiveness of the proposed method. For the evaluation of the proposed methods, experiments were carried out with public databases of falsified images that are widely used in the literature. | |
| Neural Computing and Applications INFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación | |
| 07-10-2021 | |
| Artículo | |
| Armas Vega, E.A., González Fernández, E., Sandoval Orozco, A.L. et al. (2021). Copy-move forgery detection technique based on discrete cosine transform blocks features. Neural Comput & Applic 33, 4713–4727. https://doi.org/10.1007/s00521-020-05433-1 | |
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| ArmasVega2021_Article_Copy-moveForgeryDetectionTechn.pdf | 3.08 MB | Adobe PDF | Visualizar/Abrir |