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.
