Paper Info
Title
Naïve bayes classifier based watermark detection in wavelet transform
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.
Year
DOI
Venue
2006
10.1007/11848035_32
MRCS
Keywords
Field
DocType
gray scale image,watermark detection,watermark absent,prn sequence,watermark present,bayes classifier,original watermark,dwt-based semi-blind image,wavelet domain watermark detection,class membership probability,low pass,wavelet transform,high pass
Steganography,Digital watermarking,Naive Bayes classifier,Pattern recognition,Computer science,Watermark,Robustness (computer science),Artificial intelligence,Grayscale,Wavelet,Wavelet transform
Conference
Volume
ISSN
ISBN
4105
0302-9743
3-540-39392-7
Citations 
PageRank 
References 
0
0.34
16
Authors
2
Name
Order
Citations
PageRank
Ersin Elbasi1112.40
Ahmet M. Eskicioglu239629.25