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

Autor(es)

Navarro, Luiz C. ^[1] ; Navarro, Alexandre K. W. ^[2] ; Rocha, Anderson ^[1] ; Dahab, Ricardo ^[1]

Afiliação do(s) autor(es)

^[1] Univ Campinas UNICAMP, Inst Comp, Campinas, SP - Brazil

^[2] Univ Cambridge, Engn Dept, Cambridge - England

• 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
• Modalidade de apoio: Auxílio à Pesquisa - Temático