Steganalysis for Calibrated and Lower Embedded Uncalibrated Images
- 1.2k Downloads
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.
KeywordsSteganalysis DCT Markov Calibration Support Vector Machine
Unable to display preview. Download preview PDF.
- 2.Pevn’y, T., Fridrich, J.: Merging Markov and DCT Features for Multiclass JPEG Steganalysis. In: Proceedings SPIE, Electronic Imaging, Security, Steganography and Watermarking of Multimedia Contents IX, San Jose, CA, January 29-February 1, vol. 6505, pp. 301–314 (2007)Google Scholar
- 3.Yadollapour, A., Niami, H.M.: Attack on LSB Steganography in Color and Grayscale Images using Autocorrelation Coefficients. European Journal of Scientific Research 31(2), 172–183 (2009) ISSN 1450-216 XGoogle Scholar
- 5.Kodosky, J., Fridrich, J.: Calibration Revisited. In: ACM Multimedia and Security Workshop, Princeton, NJ, September 7-8, pp. 63–74 (2009)Google Scholar
- 6.Pevny, T., Fridrich, J., Ker, A.D.: From Blind to Quantitative Steganalysis. In: SPIE, Electronic Imaging, Media Forensics and Security XI, San Jose, CA, January 18-22, pp. 0C 1–0C 14 (2009)Google Scholar