Digital Video Steganalysis Based on Statistical Features

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.