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Application of Grubbs' test for outliers to the detection of watermarks

Published: 11 June 2014 Publication History

Abstract

In an era when the protection of intellectual property rights becomes more and more important, providing robust and efficient watermarking techniques is crucial, both in terms of embedding and detection. In this paper, the authors specifically focus on the latter stage. Most often, the detection consists in the comparison of a fixed and non-adaptive decision threshold to a correlation coefficient. This threshold is usually determined either theoretically or experimentally. Here, it is proposed to apply Grubbs? test, a simple statistical test for outliers, on the correlation data in order to take a binary decision about the presence or the absence of the searched watermark. The proposed technique is applied to three algorithms from the literature: the correlation data generated by the detector is fed to Grubbs' test. The obtained results show that Grubbs' test is efficient, robust and reliable. Above all, it automatically adapts to the searched watermark and can be easily applied to most types of watermarking approaches.

References

[1]
Ingemar J. Cox, Matthew L. Miller, Jeffrey A. Bloom, Jessica Fridrich, and Ton Kalker. Digital Watermarking and Steganography. The Morgan Kaufmann Series in Multimedia Information and Systems, San Francisco, CA, USA, 2nd edition, 2007.
[2]
Ante Poljicak, Midija Mandic, and Darko Agic. Discrete Fourier transform-based watermarking method with an optimal implementation radius. Journal of Electronic Imaging, 20(3):033008--1--8, July 2011.
[3]
V Solachidis and I Pitas. Circularly symmetric watermark embedding in 2-D DFT domain. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 10(11):1741--53, January 2001.
[4]
Mauro Barni, Franco Bartolini, and Alessandro Piva. Improved wavelet-based watermarking through pixel-wise masking. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 10(5):783--91, January 2001.
[5]
Wei Liu, Lina Dong, and Wenjun Zeng. Optimum Detection for Spread-Spectrum Watermarking That Employs Self-Masking. IEEE Transactions on Information Forensics and Security, 2(4):645--654, December 2007.
[6]
A. Piva, M. Barni, F. Bartolini, and V. Cappellini. Threshold Selection for Correlation-Based Watermark Detection. In Proc. COST254 Workshop on Intelligent Communications, pages 5--6, 1998.
[7]
Qiang Cheng and T.S. Huang. Robust optimum detection of transform domain multiplicative watermarks. IEEE Transactions on Signal Processing, 51(4):906--924, April 2003.
[8]
W.H. Lin, S.J. Horng, and T.W. Kao. An Efficient Watermarking Method Based on Significant Difference of Wavelet Coefficient Quantization. IEEE Transactions on Multimedia, 10(5):746--757, August 2008.
[9]
Roland Kwitt, Peter Meerwald, and Andreas Uhl. Blind DT-CWT domain additive spread-spectrum watermark detection. In 2009 16th International Conference on Digital Signal Processing, pages 1--8. IEEE, July 2009.
[10]
D. M. Hawkins. Identification of Outliers. Biometrical Journal, 29(2):198--198, 1980.
[11]
Victoria Hodge and Jim Austin. A Survey of Outlier Detection Methodologies. Artificial Intelligence Review, 22(2):85--126, October 2004.
[12]
Frank E. Grubbs. Procedures for detecting outlying observations in samples. Technometrics, 11(1):1--21, 1969.
[13]
B. Rosner. Percentage Points for a Generalized ESD Many-Outlier Procedure. Technometrics, 25(2):165--172, 1983.
[14]
W. J. Dixon. Processing data for Outliers. Biometrics, 9(1):74--89, 1953.
[15]
Gary L. Tietjen and Roger H. Moore. Some Grubbs-Type Statistics for the Detection of Outliers. Technometrics, 14(3):583--597, 1972.
[16]
J. P. Lewis. Fast normalized cross-correlation. In Vision interface, pages 120--123, 1995.
[17]
P. Meerwald, C. Koidl, and A. Uhl. Attack on "Watermarking Method Based on Significant Difference of Wavelet Coefficient Quantization". IEEE Transactions on Multimedia, 11(5):1037--1041, August 2009.
[18]
V. Barnett and T. Lewis. Outliers in Statistical Data. Wiley Series in Probability and Mathematical Statistics, John Wiley & Sons; Chichester, 1994.
[19]
C. Croarkin and P. Tobias. NIST/SEMATECH e-handbook of statistical methods. Retrieved January, 1:2014, 2014.

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cover image ACM Conferences
IH&MMSec '14: Proceedings of the 2nd ACM workshop on Information hiding and multimedia security
June 2014
212 pages
ISBN:9781450326476
DOI:10.1145/2600918
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 June 2014

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Author Tags

  1. detection
  2. grubbs test
  3. watermarking

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IH&MMSec '14 Paper Acceptance Rate 24 of 64 submissions, 38%;
Overall Acceptance Rate 128 of 318 submissions, 40%

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