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dc.contributor.authorBoato, Giulia
dc.contributor.authorDang Nguyen, Duc Tien
dc.contributor.authorDe Natale, Francesco G.B.
dc.date.accessioned2021-05-05T07:55:42Z
dc.date.available2021-05-05T07:55:42Z
dc.date.created2021-01-21T13:49:26Z
dc.date.issued2020
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/11250/2753605
dc.description.abstractMathematical morphology provides a large set of powerful non-linear image operators, widely used for feature extraction, noise removal or image enhancement. Although morphological filters might be used to remove artifacts produced by image manipulations, both on binary and gray level documents, little effort has been spent towards their forensic identification. In this paper we propose a non-trivial extension of a deterministic approach originally detecting erosion and dilation of binary images. The proposed approach operates on grayscale images and is robust to image compression and other typical attacks. When the image is attacked the method looses its deterministic nature and uses a properly trained SVM classifier, using the original detector as a feature extractor. Extensive tests demonstrate that the proposed method guarantees very high accuracy in filtering detection, providing 100% accuracy in discriminating the presence and the type of morphological filter in raw images of three different datasets. The achieved accuracy is also good after JPEG compression, equal or above 76.8% on all datasets for quality factors above 80. The proposed approach is also able to determine the adopted structuring element for moderate compression factors. Finally, it is robust against noise addition and it can distinguish morphological filter from other filters.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleMorphological filter detector for image forensics applicationsen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright the authors.en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.1109/ACCESS.2020.2965745
dc.identifier.cristin1876605
dc.source.journalIEEE Accessen_US
dc.source.pagenumber13549 - 13560en_US
dc.identifier.citationIEEE Access. 2020, 8, 13549 - 13560.en_US
dc.source.volume8en_US


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