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Digital Video Steganalysis Based on Statistical Features

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dc.contributor.author Htet, Thu Thu
dc.date.accessioned 2019-11-14T05:46:59Z
dc.date.available 2019-11-14T05:46:59Z
dc.date.issued 2012-02-28
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/2403
dc.description.abstract Steganalysis is the art and science of detecting a secret communication. Hiding a message will most likely leave detectable traces in the cover medium. The information hiding process changes the statistical properties of the cover, which is a steganalyst attempts to detect. The process of attempting to detect statistical traces is called statistical steganalysis. This paper presents an improved blind steganalysis technique to detect the presence of hidden messages. In order to identify and classify the two types of statistic texture feature are used. The first type features derive from the average cooccurrence matrices. The second type features is the grey level histogram. Support Vector Machine is considered a state-of-the-art classification algorithm. SVM classifier is utilized as the classifier. Experimental results show that this approach is very successful in detecting the information-hiding in MSU Stego Video steganograms. en_US
dc.language.iso en_US en_US
dc.publisher Tenth International Conference On Computer Applications (ICCA 2012) en_US
dc.subject steganalysis en_US
dc.subject histogram characteristic function en_US
dc.subject co-occurrence matrices en_US
dc.subject SVM classifier en_US
dc.title Digital Video Steganalysis Based on Statistical Features en_US
dc.type Article en_US

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