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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Connecting the dots: Toward accountable machine-learning printer attribution methods

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Autor(es):
Navarro, Luiz C. [1] ; Navarro, Alexandre K. W. [2] ; Rocha, Anderson [1] ; Dahab, Ricardo [1]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Univ Campinas UNICAMP, Inst Comp, Campinas, SP - Brazil
[2] Univ Cambridge, Engn Dept, Cambridge - England
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION; v. 53, p. 257-272, MAY 2018.
Citações Web of Science: 3
Resumo

Digital forensics is rapidly evolving as a direct consequence of the adoption of machine-learning methods allied with ever-growing amounts of data. Despite the fact that these methods yield more consistent and accurate results, they may face adoption hindrances in practice if their produced results are absent in a human-interpretable form. In this paper, we exemplify how human-interpretable (a.k.a., accountable) extensions can enhance existing algorithms to aid human experts, by introducing a new method for the source printer attribution problem. We leverage the recently proposed Convolutional Texture Gradient Filter (CTGF) algorithm's ability to capture local printing imperfections to introduce a new method that maps and highlights important attribution features directly onto the investigated printed document. Supported by Random Forest classifiers, we isolate and rank features that are pivotal for differentiating a printer from others, and back-project those features onto the investigated document, giving analysts further evidence about the attribution process. (AU)

Processo FAPESP: 17/12646-3 - Déjà vu: coerência temporal, espacial e de caracterização de dados heterogêneos para análise e interpretação de integridade
Beneficiário:Anderson de Rezende Rocha
Linha de fomento: Auxílio à Pesquisa - Temático