Steganalysis of very low embedded JPEG image in spatial and transform domain steganographic scheme using SVM

Abstract

Steganalysis recognizes the manifestation of a hidden message in an artefact. In this paper, the analysis is done statistically, by extracting features that shows 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 and 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, four diverse samplings are used for classification. In this paper, the percentage embedding is kept as low as 10%.

Keywords: kernel functions, SVM , Diverse samplings, Steganographic schemes .