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Low feature dimension in image steganographic recognition

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dc.contributor.authorAhmed I.T.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorHammad B.T.en_US
dc.contributor.authorid57193324906en_US
dc.contributor.authorid36682671900en_US
dc.contributor.authorid57193327622en_US
dc.date.accessioned2023-05-29T09:36:52Z
dc.date.available2023-05-29T09:36:52Z
dc.date.issued2022
dc.description.abstractSteganalysis aids in the detection of steganographic data without the need to know the embedding algorithm or the "cover" image. The researcher's major goal was to develop a Steganalysis technique that might improve recognition accuracy while utilizing a minimal feature vector dimension. A number of Steganalysis techniques have been developed to detect steganography in images. However, the steganalysis technique's performance is still limited due to their large feature vector dimension, which takes a long time to compute. The variations of texture and properties of an embedded image are clearly seen. Therefore, in this paper, we proposed Steganalysis recognition based on one of the texture features, such as gray level co-occurrence matrix (GLCM). As a classifier, Ada-Boost and Gaussian discriminant analysis (GDA) are used. In order to evaluate the performance of the proposed method, we use a public database in our proposed and applied it using IStego100K datasets. The results of the experiment show that the proposed can improve accuracy greatly. It also indicates that in terms of accuracy, the Ada-Boost classifier surpassed the GDA. The comparative findings show that the proposed method outperforms other current techniques especially in terms of feature size and recognition accuracy. � 2022 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v27.i2.pp885-891
dc.identifier.epage891
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85135020053
dc.identifier.spage885
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85135020053&doi=10.11591%2fijeecs.v27.i2.pp885-891&partnerID=40&md5=b6f37e91bf17d600281ebe7e7ae17d00
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26808
dc.identifier.volume27
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.titleLow feature dimension in image steganographic recognitionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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