| Full text | |
| Author(s): |
Total Authors: 4
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| Affiliation: | [1] Univ Campinas UNICAMP, Inst Comp, Campinas, SP - Brazil
[2] Univ Cambridge, Engn Dept, Cambridge - England
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION; v. 53, p. 257-272, MAY 2018. |
| Web of Science Citations: | 3 |
| Abstract | |
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) | |
| FAPESP's process: | 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Research Projects - Thematic Grants |