Small Embed Cross-validated JPEG Steganalysis in Spatial and Transform Domain Using SVM

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dc.contributor.authorD. Shankar,Deepa
dc.contributor.authorAzhakath ,Adresya Suresh
dc.date.accessioned2024-06-06T06:41:11Z
dc.date.available2024-06-06T06:41:11Z
dc.date.issued2019
dc.description.abstractSteganalysis recognizes the manifestation of a hidden message in an artefact. In this paper, the analysis is done statistically, by extracting features that show a change during an embedding. Machine learning approach is employed here by using a classifier to identify the stego image and cover image. SVM is used as a classifier in this paper. A comparative study is done by using steganographic schemes from spatial plus transform domain. The two steganographic schemes are LSB matching and F5. Six unlike kernel functions and four diverse samplings are used for classification. The percentage embedding is as low as 10%. Keywords: Machine learning ,LSB matching, Steganographic
dc.identifier.citationShankar, D. D., & Azhakath, A. S. (2021). Small embed cross-validated jpeg steganalysis in spatial and transform domain using SVM. In Advances in Machine Learning and Computational Intelligence: Proceedings of ICMLCI 2019 (pp. 283-291). Springer Singapore.‏
dc.identifier.doihttps://doi.org/10.1007/978-981-15-5243-4_25
dc.identifier.urihttps://dspace.adu.ac.ae/handle/1/5716
dc.language.isoen
dc.publisherSpringer link
dc.titleSmall Embed Cross-validated JPEG Steganalysis in Spatial and Transform Domain Using SVM
dc.typeConference Paper

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