An Efficient Feature based Classification for JPEG Steganalysis
Deepa D. Shankar
TIFAC Core in Cyber Security, Amrita Vishwa Vidyapeetham, Coimbatore, India.
The objective of steganalysis is to detect messages hidden in
cover images, such as digital images. In this paper, we present a
powerful new blind steganalytic scheme that can reliably detect
hidden data in JPEG images. This scheme is feature based in the
sense that features that are sensitive to embedding changes and
being employed as means of steganalysis. The features are
extracted in DCT domain. DCT domain features have extended
DCT features and Markovian features merged together to
eliminate the drawbacks of both. The blind steganalytic
technique has a broad spectrum of analyzing different
embedding techniques. The feature based steganalytic technique
is used in the DCT domain to extract about 23 functionals and
classify the dataset according to these functionals. The feature
set can be increased to about 274 features by merging both DCT
and Markovian features. The extracted features are being fed to
a classifier which helps to distinguish between a cover and stego
image. Support Vector Machine is used as classifier here.
Keywords: Steganalysis, DCT, Extended DCT feature,
Markovian feature, Feature extraction, Principal component
analysis, Support vector machine.
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