Robustness in Watermarking and Steganography
This article discusses the crucial property of robustness in watermarking schemes, highlighting the ability to detect watermarks even after various attacks. It specifically examines a DWT-based semi-blind image watermarking approach.
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About this video
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.
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Published
Jul 4, 2020
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