Blind Image Steganalysis of JPEG images using feature extraction through the process of dilation |
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| Institution: | 1. Thailand Center of Excellence in Physics, Commission on Higher Education, 328 Si Ayutthaya Road, Bangkok 10400, Thailand;2. Plasma and Beam Physics Research Facility, Department of Physics and Materials Science, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand;3. Molecular Biology Laboratory, Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand;4. Faculty of Science, Maejo University, Chiang Mai 50290, Thailand;5. Institute of Science and Technology Research, Chiang Mai University, Chiang Mai 50200, Thailand;1. Dipartimento di Ingegneria, Università degli Studi di Perugia, Italy;2. Dipartimento di Ingegneria, Università Roma Tre, Italy;1. School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. Department of Mathematics and Statistics, McGill University, Montreal, Quebec, Canada H3A 0B9;3. School of Science, Tianjin Chengjian University, Tianjin 300384, China;4. School of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, China;1. Carnegie Mellon University, Pittsburgh, PA, USA;2. North-West University, Vanderbijlpark, South Africa;3. Telefonica Research, Barcelona, Spain;4. CNRS–IRISA, Rennes, France |
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| Abstract: | 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. |
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| Keywords: | Blind Image Steganalysis Dilation Steganography Feature extraction Frequency Spatial Wavelet |
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