@InProceedings{OliveiraNass:2017:DeMoPr,
author = "Oliveira, Thomas Jose Mazon de and {Marco Aurelio Wehrmeister} and
Nassu, Bogdan Tomoyuki",
affiliation = "{Federal University of Technology - Parana} and {Federal
University of Technology - Parana} and {Federal University of
Technology - Parana}",
title = "Detecting modifications in printed circuit boards from fuel pump
controllers",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Image-based detection of modifications in printed circuit boards,
Fraud detection in fuel pumps, Computer vision, image
registration, machine learning, Points of interest.",
abstract = "Frauds involving illegal modifications to the printedcircuit
boards from fuel pump controllers are a serious problem,which not
only harms customers, but also connects to othercrimes, such as
money laundering and tax evasion. The currentstate-of-practice for
inspecting these boards is a visual analysisperformed by a human.
In this paper, we introduce an image-based approach that can
provide support to the human inspectorby automatically detecting
suspicious regions in the boards.The proposed approach aligns a
photograph of the inspectedboard to a reference view, partitions
the image in sub-regions,extracts features using a variation of
the popular Scale-InvariantFeature Transform, classifies the
features against previouslytrained Support Vector Machines, and
integrates the results forpresentation. In experiments performed
on a dataset containing649 images from a board, with and without
modifications, ourapproach achieved a precision of 0.7739, a
recall of 0.9434, andanF-measure 0.8503. These results indicate
that our approachcan effectively identify suspicious regions,
providing invaluablehelp to the human inspector.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.18",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.18",
language = "en",
ibi = "8JMKD3MGPAW/3PF6BJ8",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PF6BJ8",
targetfile = "SibGrapi.pdf",
urlaccessdate = "2024, May 01"
}