The detection of stego images, used as a carrier for secret messages for nefarious activities,forms the basis for Blind Image Steganalysis. The main issue in Blind Steganalysis is the
non-availability of knowledge about the Steganographic technique applied to the image.Feature extraction approaches best suited for Blind Steganalysis, either dealt with only a
few features or single domain of an image. Moreover, these approaches lead to low detection percentage. The main objective of this paper is to improve the detection percentage.
In this paper, the focus is on Blind Steganalysis of JPEG images through the process of dilation that includes splitting of given image into RGB components followed by
transformation of each component into three domains, viz., frequency, spatial, and wavelet. Extracted features from each domain are given to the Support Vector Machine
(SVM) classifier that classified the image as steg or clean. The proposed process of dilation was tested by experiments with varying embedded text sizes and varying number of
extracted features on the trained SVM classifier. Overall Success Rate (OSR) was chosen as the performance metric of the proposed solution and is found to be effective, compared
with existing solutions, in detecting higher percentage of steg images.