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 T 1. During watermark detection, all the high pass coefficients above another threshold T 2 (T 2 ≥ T 1) 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.
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Elbasi, E., Eskicioglu, A.M. (2006). Naïve Bayes Classifier Based Watermark Detection in Wavelet Transform. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11848035_32
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DOI: https://doi.org/10.1007/11848035_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39392-4
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