The ultimate steganalysis benchmark?

ABSTRACT

We present a new benchmark for binary steganalysis methods, based on the asymptotic information (in the entropic sense) it gives about the presence of hidden data. The theoretical foundation is quite unlike ad hoc performance measures found in steganalysis literature that are based on false positive and negative rates. It is argued that this new metric is an application-independent long-run measure of true performance. There are some challenges to computing the benchmark empirically, and some suggested methods are presented, but no definitive answer emerges. As a simple case study, some steganalysis methods from the literature are evaluated using these techniques.

REFERENCES

• 1: R. Böhme and A. Ker. A two-factor error model for quantitative steganalysis. In Security, Steganography and Watermarking of Multimedia Contents VIII, volume 6072 of Proc. SPIE, pages 59--74, 2006.
• 2: Christian Cachin, An information-theoretic model for steganography, Information and Computation, v.192 n.1, p.41-56, 1 July 2004 [doi>10.1016/j.ic.2004.02.003]
• 3: H. Cramér. Mathematical Methods of Statistics. Princeton University Press, 1946.
• 4: S. Dumitrescu, X. Wu, and Z. Wang. Detection of LSB steganography via sample pair analysis. IEEE Trans. Signal Processing, 51(7):1995--2007, 2003.
• 5: J. Fridrich and D. Soukal. Matrix embedding for large payloads. IEEE Trans. Information Forensics and Security, 1(3):390--395, 2006.
• 6: J. Harmsen and W. Pearlman. Steganalysis of additive noise modelable information hiding. In Security and Watermarking of Multimedia Contents V, volume 5020 of Proc. SPIE, pages 131--142, 2003.
• 7: A. Ker. A general framework for the structural steganalysis of LSB replacement. In Proc. 7th Information Hiding Workshop, volume 3727 of Springer LNCS, pages 296--311, 2005.
• 8: A. Ker. Steganalysis of LSB matching in grayscale images. IEEE Signal Processing Lett., 12(6):441--444, 2005.
• 9: A. Ker. Batch steganography and pooled steganalysis. In Proc. 8th Information Hiding Workshop, volume 4437 of Springer LNCS, pages 265--281, 2006.
• 10: A. Ker. A capacity result for batch steganography. IEEE Signal Processing Lett., 14(8), 2007.
• 11: A. Ker. Derivation of error distribution in least-squares steganalysis. IEEE Trans. Information Forensics and Security, 2(2):140--148, 2007.
• 12: A. Ker. Optimally weighted least-squares steganalysis. In Security, Steganography, and Watermarking of Multimedia Contents IX, volume 6505 of Proc. SPIE, 2007.
• 13: S. Kullback. Information Theory and Statistics. Dover, New York, 1968.
• 14: P. Lu, X. Luo, Q. Tang, and L. Shen. An improved sample pairs method for detection of LSB embedding. In Proc. 6th Information Hiding Workshop, volume 3200 of Springer LNCS, pages 116--127, 2004.
• 15: C. Stone, M. Hansen, C. Kooperberg, and Y. Truong. The use of polynomial splines and their tensor products in extended linear modeling (with discussion). Annals of Statistics, 25:1371--1470, 1997.
• 16: Q. Wang, S. Kulkarni, and S. Verdu. Divergence estimation of continuous distributions based on data-dependent partitions. IEEE Trans. Information Theory, 51(9):3064--3074, 2005.
• 17: Y. Wang and P. Moulin. Statistical modelling and steganalysis of DFT-based image steganography. In Security, Steganography and Watermarking of Multimedia Contents VIII, volume 6072 of Proc. SPIE, 2006.

INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
        D.2.11 Software Architectures
            Subjects: Information hiding

Additional Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.1 Systems and Information Theory
          Subjects: Information theory

General Terms:
Performance, Security

Keywords:
benchmarking, steganalysis, steganographic capacity