Detecção de imagens falsificadas baseada em descritores locais de textura e rede neural convolucional
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2020-06-30
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Universidade Federal de Goiás
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.
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Citação
FERREIRA, William Divino. Detecção de imagens falsificadas baseada em descritores locais de textura e rede neural convolucional. 2020. 151 f. Tese (Doutorado em Engenharia Elétrica e da Computação) - Universidade Federal de Goiás, Goiânia, 2020.