Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyOliveiraNass:2017:DeMoPr
TitleDetecting Modifications in Printed Circuit Boardsfrom Fuel Pump Controllers
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size4683 KiB
Context area
Author1 Oliveira, Thomas Jose Mazon de
2 Marco Aurelio Wehrmeister
3 Nassu, Bogdan Tomoyuki
Affiliation1 Federal University of Technology - Parana
2 Federal University of Technology - Parana
3 Federal University of Technology - Parana
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-17 13:40:25 :: -> administrator ::
2020-02-19 02:01:21 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsImage-based detection of modifications in printed circuit boards, Fraud detection in fuel pumps, Computer vision, image registration, machine learning, Points of interest.
AbstractFrauds 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.
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Next Higher Units8JMKD3MGPAW/3PJT9LS
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