Naïve bayes classifier based watermark detection in wavelet transform

  • Authors:

  • Ersin Elbasi;Ahmet M. Eskicioglu

  • Affiliations:

  • The Graduate Center, The City University of New York, New York, NY;Department of Computer and Information Science, Brooklyn College, The City University of New York, Brooklyn, NY

  • Venue:
  • MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
  • Year:
  • 2006

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Abstract

Robustness is the one of the essential properties of watermarking schemes. It is the ability to detect the watermark after attacks. A DWT-based semi-blind image watermarking scheme leaves out the low pass band, and embeds a pseudo random number (PRN) sequence (i.e., the watermark) in the other three bands into the coefficients that are higher than a given threshold T1. During watermark detection, all the high pass coefficients above another threshold T2 (T2 ≥ T1) are used in correlation with the original watermark. In this paper, we embed a PRN sequence using the same procedure. In detection, however, we apply the Naïve Bayes Classifier, which can predict class membership probabilities, such as the probability that a given image belongs to class “Watermark Present” or “Watermark Absent”. Experimental results show that the Naïve Bayes Classifier gives very promising results for gray scale images in the wavelet domain watermark detection.