Small Embed Cross-validated JPEG Steganalysis in Spatial and Transform Domain 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 show 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 in this paper. 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 and four diverse samplings are used for classification. The percentage embedding is as low as 10%.

Keywords: Machine learning ,LSB matching, Steganographic