Blind Detection of Additive Spread-Spectrum Watermarking in the Dual-Tree Complex Wavelet Transform Domain

Blind Detection of Additive Spread-Spectrum Watermarking in the Dual-Tree Complex Wavelet Transform Domain

Roland Kwitt (University of Salzburg, Austria), Peter Meerwald (University of Salzburg, Austria) and Andreas Uhl (University of Salzburg, Austria)
DOI: 10.4018/978-1-4666-1758-2.ch005
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Abstract

In this paper, the authors adapt two blind detector structures for additive spread-spectrum image watermarking to the host signal characteristics of the Dual-Tree Complex Wavelet Transform (DT-CWT) domain coefficients. The research is motivated by the superior perceptual characteristics of the DT-CWT and its active use in watermarking. To improve the numerous existing watermarking schemes in which the host signal is modeled by a Gaussian distribution, the authors show that the Generalized Gaussian nature of Dual-Tree detail subband statistics can be exploited for better detector performance. This paper finds that the Rao detector is more practical than the likelihood-ratio test for their detection problem. The authors experimentally investigate the robustness of the proposed detectors under JPEG and JPEG2000 attacks and assess the perceptual quality of the watermarked images. The results demonstrate that their alterations allow significantly better blind watermark detection performance in the DT-CWT domain than the widely used linear-correlation detector. As only the detection side has to be modified, the proposed methods can be easily adopted in existing DT-CWT watermarking schemes.
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2. Dt-Cwt Subband Statistics

In order to obtain a good signal detector in noise, i.e. the host signal for blind watermarking in the absence of attacks, we have to find a reasonable noise model first. By employing a J-scale 2-D DT-CWT we obtain six complex subbands per decomposition level, oriented along approximately +/- 15, +/- 45, +/- 75 degree. To visualize the directional selectivity, Figure 1 shows the magnitude of the six complex detail subbands at level two of the decomposed Bridge image (see Figure 1).

Figure 1.

Complex coefficient magnitudes of the 2nd level detail subbands with the MLEs of the GGD's shape parameter fitted to the marginal distributions of concatenated real and imaginary parts

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