Image Watermarking Based on Dual Tree Complex Wavelet Transform and Pulse Coupled Neural Network

2008 ◽  
Author(s):  
Bo Li ◽  
Hailian Lv
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Image Watermarking ◽  
Complex Wavelet Transform ◽  
Pulse Coupled Neural Network ◽  
Complex Wavelet

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

2012 ◽  
pp. 53-65
Author(s):  
Roland Kwitt ◽  
Peter Meerwald ◽  
Andreas Uhl
Keyword(s):  
Wavelet Transform ◽  
Spread Spectrum ◽  
Image Watermarking ◽  
Perceptual Quality ◽  
Ratio Test ◽  
Complex Wavelet Transform ◽  
Detector Performance ◽  
Complex Wavelet ◽  
Perceptual Characteristics ◽  
Host Signal

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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