Publication Type:

Journal Article

Source:

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 7077 LNCS, Number PART 2, Visakhapatnam, Andhra Pradesh, p.294-301 (2011)

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-84555217847&partnerID=40&md5=5ea4c60def5979dbfd97f5f2e8cb5483

Keywords:

Broad spectrum, calibration, Calibration techniques, Classification rates, Cover-image, DCT, DCT domain, Digital image, Digital image storage, Embedding rates, Embedding technique, Feature sets, Feature-based, JPEG image, Markov, Markovian, Secret messages, Steganalysis, Steganography, Stego image, Support vector, Support vector machines, Transform domain, Uncalibrated images

Abstract:

The objective of steganalysis is to detect messages hidden in a cover images, such as digital images. The ultimate goal of a steganalyst is to extract and decipher the secret message. In this paper, we present a powerful new blind steganalytic scheme that can reliably detect hidden data with a relatively small embedding rate in JPEG images as well as using a technique known as calibration. This would increase the success rate of steganalysis by detecting data in transform domain. This scheme is feature based in the sense that features that are sensitive to embedding changes are 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 in calibration technique to eliminate the drawbacks of both(inter and intra block dependency) respectively. For the lesser embedding rate, the features are considered separately to evolve a better classification rate. The blind steganalytic technique has a broad spectrum of analyzing different embedding techniques The feature set contains 274 features by merging both DCT features 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. © 2011 Springer-Verlag.

Notes:

cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@2a76c791 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@5c28165e Through org.apache.xalan.xsltc.dom.DOMAdapter@4bd4a496; Conference Code:87928

Cite this Research Publication

D. D. Shankar, Gireeshkumar, T., and Nath, H. V., “Steganalysis for calibrated and lower embedded uncalibrated images”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7077 LNCS, pp. 294-301, 2011.