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dc.contributor.authorSingh, Neetu
dc.contributor.authorGupta, Abhinav
dc.contributor.authorJain, Roop Chand
dc.date.accessioned2018-05-22T07:44:36Z
dc.date.available2018-05-22T07:44:36Z
dc.date.issued2018
dc.identifier.citationAdvances in electrical and electronic engineering. 2018, vol. 16, no. 1, p. 125-134 : ill.cs
dc.identifier.issn1336-1376
dc.identifier.issn1804-3119
dc.identifier.urihttp://hdl.handle.net/10084/127129
dc.description.abstractThe vulnerability of digital images is growing towards manipulation. This motivated an area of research to deal with digital image forgeries. The certifying origin and content of digital images is an open problem in the multimedia world. One of the ways to find the truth of images is finding the presence of any type of contrast enhancement. In this work, novel and simple machine learning tool is proposed to detect the presence of histogram equalization using statistical parameters of DC Discrete Cosine Transform (DCT) coefficients. The statistical parameters of the Gaussian Mixture Model (GMM) fitted to DC DCT coefficients are used as features for classifying original and histogram equalized images. An SVM classifier has been developed to classify original and histogram equalized image which can detect histogram equalized image with accuracy greater than 95 % when false rate is less than 5 %cs
dc.format.extent1661722 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoencs
dc.publisherVysoká škola báňská - Technická univerzita Ostravacs
dc.relation.ispartofseriesAdvances in electrical and electronic engineeringcs
dc.relation.urihttp://dx.doi.org/10.15598/aeee.v16i1.2647cs
dc.rights© Vysoká škola báňská - Technická univerzita Ostrava
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCLAHEcs
dc.subjectDC DCT coefficientscs
dc.subjectGaussian Mixture Modelcs
dc.subjectimage forensicscs
dc.titleAdaptive histogram equalization based image forensics using statistics of DC DCT coefficientscs
dc.typearticlecs
dc.identifier.doi10.15598/aeee.v16i1.2647
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs


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