Detalhes bibliográficos
| Ano de defesa: |
2020 |
| Autor(a) principal: |
Ferreira, William Divino
 |
| Orientador(a): |
Cruz Júnior, Gélson da
|
| Banca de defesa: |
Cruz Júnior, Gélson da,
Pedrini, Hélio,
Salvini, Rogério Lopes,
Costa, Ronaldo Martins da,
Lemos, Rodrigo Pinto |
| Tipo de documento: |
Tese
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal de Goiás
|
| Programa de Pós-Graduação: |
Programa de Pós-graduação em Engenharia Elétrica e da Computação (EMC)
|
| Departamento: |
Escola de Engenharia Elétrica, Mecânica e de Computação - EMC (RG)
|
| País: |
Brasil
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://repositorio.bc.ufg.br/tede/handle/tede/10824
|
Resumo: |
Nowadays, digital image transformation has become a widespread activity. Hence, image copying, cloning, and resizing are easily performed, making it challenging to check image integrity and authenticity. Moreover, a criminal investigation from digital images becomes extremely hard, because using those images as proof demands to ensure its legitimately,under a risk to implicate the whole legal process.In this sense, this work develops a model for forged images based on local texture descriptors with convolutional neural networks. Henceforth, in this work, firstly, we evaluated fourteen local texture descriptors in five public image texture datasets, and then we selected descriptors with the best efficacy. Second, the selected descriptors are applied to four public datasets to extract texture features from forged and legit images. Finally, those features are used to train a residual convolutional neural network, and then, classifying images as authentic or forged with a Support Vector Machine Classifier. A result of the proposed model provides enthusiasm, mainly when applied to a dataset with a small number of images. |
