Advertisement

Detectors for Echo Hiding Systems

  • Scott Craver
  • Bede Liu
  • Wayne Wolf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2578)

Abstract

Echo hiding is a method of hiding information in an audio clip by the addition of embedding imperceptible echoes. The echoes are detected by various methods, including autocorrelation and cepstral analysis. We treat echo hiding as general multiplicative embedding in the frequency domain, and derive appropriate detectors based on various statistical models of audio FFT coefficients. This results in several detectors that are both simpler and more powerful than cepstral detection. We also observe that detection by simple correlation in the log-spectral domain performs very well in contrast to much more complicated approaches.

Keywords

False Alarm Rate Audio Signal Frame Size Audio Data Detector Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    R.P. Bogert, M. J. Healy and J.W. Tukey, “The Quefrency Alanysis of Time Series for Echoes: Cepstrum, Pseudo-Autocovariance, Cross-Ceptsrum and Saphe-Cracking,” Proceedings of the Symposium on Time Series Analysis, Brown University, jun 1962. pp. 209–243. 249Google Scholar
  2. [2]
    T.M. Cover and J.A. Thomas, Elements of Information Theory. New York: John Wiley and Sons, Inc., 1991. 255zbMATHGoogle Scholar
  3. [3]
    S. Craver, M. Wuand B. Liu, “What Can We Reasonably Expect From Watermarks?”, Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2001.Google Scholar
  4. [4]
    D. Gruhl, A. Lu and W. Bender, “Echo Hiding,” 1st workshop on Info. Hiding, 1996. 247, 248, 250Google Scholar
  5. [5]
    R. Harris, “Whatever Happened to SDMI?,” Associated Press, 29 Apr. 2002. 248Google Scholar
  6. [6]
    F.A.P. Petitcolas, R. J. Anderson and M. Kuhn “Attacks on Copyright Marking Systems,” 2nd workshop on Info. Hiding, 1998. 248, 254Google Scholar
  7. [7]
    R. Petrovic, J. M. Winograd, K. Jemili and E. Metois, “Apparatus and method for encoding and decoding information in analog signals,” US Patent No 05940135, August 1999. 247, 249Google Scholar
  8. [8]
    H.V. Poor, An Introduction to Signal Detection and Estimation, 2nd ed. Berlin-Heidelberg: Springer-Verlag, 1998. 250Google Scholar
  9. [9]
    J.G. Proakis, Digital Communications, 3rd ed. Boston, MA: McGraw-Hill, 1995. 252Google Scholar
  10. [10]
    Secure Digital Music Initiative, “Call for Proposals for Phase II Screening Technology, Version 1.0,” February 2000. 248Google Scholar
  11. [11]
    J. Stern and J. Boeuf, “An Analysis of One of the SDMI Candidates,” 4th workship Workshop on Info. Hiding, 2001. 248Google Scholar
  12. [12]
    M. Wu, B. Liu, S. Craver, D. Dean, A. Stubblefield, D. Wallach, B. Swartzlander, and E. Felten, “Reading Between the Lines: Lessons Learned from the SDMI Challenge,” Proceedings of the 10th USENIX security symposium, Washington DC, August 2001. 248Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Scott Craver
    • 1
  • Bede Liu
    • 1
  • Wayne Wolf
    • 1
  1. 1.Department of Electrical EngineeringPrinceton UniversityPrincetonUSA

Personalised recommendations