Steganalysis of GIM-based data hiding using kernel density estimation

ABSTRACT

This paper presents a novel steganalysis technique to attack quantization index modulation (QIM) steganography. Our method is based on the observation that QIM embedding disturbs neighborhood correlation in the transform domain. We estimate the probability density function (pdf) of this statistical change in a systematic manner using a kernel density estimate (KDE) method. The estimated parametric density model is then used for stego message detection. The impact of the choice of kernels on the estimated density is investigated experimentally. Simulation results evaluated on a large dataset of 6000 quantized images indicate that the proposed method is reliable. The impact of the choice of message embedding parameters on the accuracy of the steganalysis detection is also evaluated. Simulation results show that the proposed method can distinguish between the quantized-cover and the QIM-stego with low false alarm rates (i.e. P_fn≤0.03 and P_fp≤0.19). We demonstrate that the proposed steganalysis scheme can successfully attack steganographic tools like Jsteg and JP Hide and Seek as well.

REFERENCES

• 1: http://en.wikipedia.org/wiki/Zero-Day_Attack.
• 2: J. korejwa: Jsteg. available at ftp://ftp.funet.fi/pub/crypt/steganography/.
• 3: Ucid: An uncompressed colour image database. available at http://www-users.aston.ac.uk/schaefeg/datasets/UCID/ucid.html.
• 4: Wafo: Wave analysis for fatigue and oceanography. available at http://www.maths.lth.se/matstat/wafo/.
• 5: R. Chandramouli and K. Subbalakshmi. Current trends in steganalysis: A critical survey. In IEEE Int. Conf. on Control, Automation, Robotics and Vision, ICARCV, volume 2, pages 964--967, December 2004.
• 6: B. Chen and G. Wornell. Quantization index modulation: A class of provably good methods for digital watermarking and information embedding. IEEE Trans. Information Theory, 47(4), May 2001.
• 7: M. Costa. Writing on dirty paper. IEEE Transactions on Information Theory, 29(3):439--441, May 1983.
• 8: T. Duong. Plug-in Bandwidth Selectors for Bivariate Kernel Density Estimation. PhD thesis, Department of Mathematics and Statistics, the University of Western Australia, 2002.
• 9: Joachim Eggers , Bernd Girod, Informed Watermarking, Kluwer Academic Publishers, Norwell, MA, 2002
• 10: P. Guillon, T. Furon, and P. Duhamel. Applied public-key steganography. In Proc. IS&T/SPIE, pages 38--49, 2002.
• 11: P. V. Kerm. Adaptive kernel density estimation. Stata Journal, 3(2), June 2003.
• 12: P. V. Kerm. Adaptive kernel density estimation. Technical report, 2003.
• 13: F. Perez-Gonzalez, F. Balado, and J. R. Hernandez. Performance analysis of existing and new methods for data hiding with known-host information in additive channels. IEEE Transaction on Signal Processing, 51(4), April 2003.
• 14: Khalid Sayood, Introduction to data compression (2nd ed.), Morgan Kaufmann Publishers Inc., San Francisco, CA, 2000
• 15: D. W. Scott. Multivariate Density Estimation: Theory, Practice, and Visualization. John Wiley & Sons, 1992.
• 16: K. Sullivan, Z. Bi, U. Madhow, S. Chandrasekaran, and B. Manjunath. Steganalysis of quantization index modulation data hiding. In IEEE Int. Conf. Image Processing (ICIP), volume 2, pages 1165--1168, 2004.
• 17: X. Zhang, M. L. King, and R. J. Hyndman. Bandwidth selection for multivariate kernel density estimation using mcmc. Monash Econometrics and Business Statistics Working Papers 9/04, Monash University, Department of Econometrics and Business Statistics, 2004.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.m Miscellaneous

General Terms:
Security

Keywords:
gamma density, kernel density estimation, mode, quantization index modulation, skewness, steganalysis, steganography