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Fake war crime image detection by reverse image search

dc.contributor.authorAskinadze, Alexander
dc.contributor.editorMitschang, Bernhard
dc.contributor.editorNicklas, Daniela
dc.contributor.editorLeymann, Frank
dc.contributor.editorSchöning, Harald
dc.contributor.editorHerschel, Melanie
dc.contributor.editorTeubner, Jens
dc.contributor.editorHärder, Theo
dc.contributor.editorKopp, Oliver
dc.contributor.editorWieland, Matthias
dc.date.accessioned2017-06-21T11:24:41Z
dc.date.available2017-06-21T11:24:41Z
dc.date.issued2017
dc.description.abstractIn the media, images of war crimes are often shared, which in reality come from other contexts or other war sites. In this paper an approach is proposed to detect duplicate or fake war crime images. For this, the bag of visual words model is used in conjunction with localized soft assignment coding and the k-nn classifier. For evaluation, a data set with 600 images of war crimes was crawled. Different distances and parameters were used for evaluation. Unmodified images can be recognized with this approach with 100% accuracy. Rotated and scaled images can also be detected with nearly 100% accuracy. Modifications like cropping or the combination of scaling and cropping ensure significantly smaller accuracy results. The run time was investigated and it was found that about 3000 images per second can be processed on an Intel Core i5 processor.en
dc.identifier.isbn978-3-88579-660-2
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDatenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-266
dc.subjectNear-duplicate image detection
dc.subjectimage recognition
dc.titleFake war crime image detection by reverse image searchen
dc.typeText/Conference Paper
gi.citation.endPage354
gi.citation.publisherPlaceBonn
gi.citation.startPage345
gi.conference.date6.-10. März 2017
gi.conference.locationStuttgart
gi.conference.sessiontitleStudierendenprogramm

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