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dc.contributor.authorXia, Z.
dc.contributor.authorYang, L.
dc.contributor.authorSun, X.
dc.contributor.authorLiang, W.
dc.contributor.authorSun, D.
dc.contributor.authorRuan, Z.
dc.date.accessioned2016-02-26T08:17:26Z
dc.date.available2016-02-26T08:17:26Z
dc.date.issued2011-04cs
dc.identifier.citationRadioengineering. 2011, vol. 20, č. 1, s. 102-109. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/56804
dc.description.abstractThis paper considers the detection of spatial domain least significant bit (LSB) matching steganography in gray images. Natural images hold some inherent properties, such as histogram, dependence between neighboring pixels, and dependence among pixels that are not adjacent to each other. These properties are likely to be disturbed by LSB matching. Firstly, histogram will become smoother after LSB matching. Secondly, the two kinds of dependence will be weakened by the message embedding. Accordingly, three features, which are respectively based on image histogram, neighborhood degree histogram and run-length histogram, are extracted at first. Then, support vector machine is utilized to learn and discriminate the difference of features between cover and stego images. Experimental results prove that the proposed method possesses reliable detection ability and outperforms the two previous state-of-the-art methods. Further more, the conclusions are drawn by analyzing the individual performance of three features and their fused feature.en
dc.formattextcs
dc.format.extent102-109cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2011/11_01_102_109.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectCommunication securityen
dc.subjectsteganalysisen
dc.subjecthistogram gradient energyen
dc.subjectneighborhood degree histogramen
dc.subjectrun-length histogramen
dc.subjectsupport vector machineen
dc.titleA Learning-Based Steganalytic Method against LSB Matching Steganographyen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue1cs
dc.coverage.volume20cs
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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