Please use this identifier to cite or link to this item:
http://hdl.handle.net/10553/41417
DC Field | Value | Language |
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dc.contributor.author | Freire-Obregón, David | - |
dc.contributor.author | Narducci, Fabio | - |
dc.contributor.author | Barra, Silvio | - |
dc.contributor.author | Castrillón-Santana, Modesto | - |
dc.date.accessioned | 2018-06-28T09:57:27Z | - |
dc.date.available | 2018-06-28T09:57:27Z | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 0167-8655 | - |
dc.identifier.other | WoS | - |
dc.identifier.uri | http://hdl.handle.net/10553/41417 | - |
dc.description.abstract | In the present paper, we propose a source camera identification (SCI) method for mobile devices based on deep learning. Recently, convolutional neural networks (CNNs) have shown a remarkable performance on several tasks such as image recognition, video analysis or natural language processing. A CNN consists on a set of layers where each layer is composed by a set of high pass filters which are applied all over the input image. This convolution process provides the unique ability to extract features automatically from data and to learn from those features. Our proposal describes a CNN architecture which is able to infer the noise pattern of mobile camera sensors (also known as camera fingerprint) with the aim at detecting and identifying not only the mobile device used to capture an image (with a 98% of accuracy), but also from which embedded camera the image was captured. More specifically, we provide an extensive analysis on the proposed architecture considering different configurations. The experiment has been carried out using the images captured from different mobile device cameras (MICHE-I Dataset) and the obtained results have proved the robustness of the proposed method. | - |
dc.language | eng | - |
dc.relation.ispartof | Pattern Recognition Letters | - |
dc.source | Pattern Recognition Letters [ISSN 0167-8655], v.126, p. 86-91 (2019) | - |
dc.subject | 120325 Diseño de sistemas sensores | - |
dc.subject | 120304 Inteligencia artificial | - |
dc.subject.other | Iris recognition | - |
dc.subject.other | Networks | - |
dc.subject.other | Source camera identification | - |
dc.subject.other | Convolutional neural networks | - |
dc.subject.other | Mobile devices | - |
dc.subject.other | Deep learning | - |
dc.title | Deep learning for source camera identification on mobile devices | - |
dc.type | info:eu-repo/semantics/Article | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.patrec.2018.01.005 | - |
dc.identifier.scopus | 85040338014 | - |
dc.identifier.isi | 000487014900011 | - |
dc.contributor.orcid | 0000-0003-2378-4277 | - |
dc.contributor.orcid | 0000-0003-4879-7138 | - |
dc.contributor.orcid | #NODATA# | - |
dc.contributor.orcid | #NODATA# | - |
dc.contributor.authorscopusid | 23396618800 | - |
dc.contributor.authorscopusid | 55303744900 | - |
dc.contributor.authorscopusid | 56005376300 | - |
dc.contributor.authorscopusid | 57198776493 | - |
dc.identifier.eissn | 1872-7344 | - |
dc.description.lastpage | 91 | - |
dc.description.firstpage | 86 | - |
dc.relation.volume | 126 | - |
dc.investigacion | Ingeniería y Arquitectura | - |
dc.type2 | Artículo | - |
dc.contributor.daisngid | 3754707 | - |
dc.contributor.daisngid | 30416512 | - |
dc.contributor.daisngid | 30900419 | - |
dc.contributor.daisngid | 32145428 | - |
dc.description.numberofpages | 6 | - |
dc.utils.revision | Sí | - |
dc.contributor.wosstandard | WOS:Freire-Obregon, D | - |
dc.contributor.wosstandard | WOS:Narducci, F | - |
dc.contributor.wosstandard | WOS:Barra, S | - |
dc.contributor.wosstandard | WOS:Castrillon-Santana, M | - |
dc.date.coverdate | Septiembre 2019 | - |
dc.identifier.ulpgc | Sí | - |
dc.contributor.buulpgc | BU-INF | - |
dc.description.sjr | 0,662 | - |
dc.description.jcr | 1,954 | - |
dc.description.sjrq | Q1 | - |
dc.description.jcrq | Q2 | - |
dc.description.scie | SCIE | - |
item.fulltext | Con texto completo | - |
item.grantfulltext | open | - |
crisitem.author.dept | GIR SIANI: Inteligencia Artificial, Robótica y Oceanografía Computacional | - |
crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.dept | Departamento de Informática y Sistemas | - |
crisitem.author.dept | GIR SIANI: Inteligencia Artificial, Robótica y Oceanografía Computacional | - |
crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.dept | Departamento de Informática y Sistemas | - |
crisitem.author.orcid | 0000-0003-2378-4277 | - |
crisitem.author.orcid | 0000-0002-8673-2725 | - |
crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.fullName | Freire Obregón, David Sebastián | - |
crisitem.author.fullName | Castrillón Santana, Modesto Fernando | - |
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