Citation
Salem Hussin, Saleh Hussin
(2005)
Improved Watermarking Algorithm Based on Discrete Wavelet Transform and Fibonacci Permutation.
Masters thesis, Universiti Putra Malaysia.
Abstract
Digital image watermark is an imperceptible, robust, secure message embedded
into the image, which identify one or more owner, distributor, or recipient of the
image, origin or status of the data or transaction dates. Watermarking is also used for
data hiding, content labeling, broadcast monitoring, and integrity control applications.
Digital watermarking resembles communication systems. Watermark is the sent
message. Image is the watermark channel or carrier. Image pixels and possible
attacks on marked image constitute the noise. Only the authorized parties' extracts
the watermark message from the marked image by using detector.
Digital watermarking has three major requirements. Watermark should be robust
against noise and attacks, imperceptible and has the required capacity. These three
requirements conflict with each other. To illustrate, increasing the watermark
strength makes the system more robust but unfortunately decreases the perceptual quality. As a second example, increasing the capacity of the watermark decreases the
robustness.
In this thesis, the goal was to study digital image watermarking and develop
watermarking algorithm that can achieve high imperceptibility, maximum capacity,
and high robustness against image manipulation at the same time. This algorithm is
based on the combination of Fibonacci permutation and discrete wavelet transforms
(DWT). A binary image is used as the watermark and inserted into a mid-frequency
wavelet subband of the permuted image. The watermarked image is reproduced by
taking the inverse DWT and the inverse permutation. In extraction process the
watermark is extracted from the watermarked image directly without using the
original image.
The experimental results have shown that the proposed watermark is invisible to
human eyes and very robust against image manipulation, such as JPEG compression,
median filtering, wiener filtering, and noises.
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