An adaptive stochastic-resonance-based detector and its application in watermark extraction

Authors:
Gencheng Guo;Mrinal Mandal
Affiliations:
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
Venue:
WSEAS Transactions on Signal Processing
Year:
2011

Citing 7
Cited 1

On the use of stochastic resonance in sine detection

Signal Processing
Design of detectors based on stochastic resonance

Signal Processing
Detection of weak signals using adaptive stochastic resonance

ICASSP '95 Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02
Digital watermarking based on stochastic resonance signal processor

PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Theory of the Stochastic Resonance Effect in Signal Detection—Part II: Variable Detectors

IEEE Transactions on Signal Processing - Part II
Theory of the Stochastic Resonance Effect in Signal Detection: Part I—Fixed Detectors

IEEE Transactions on Signal Processing - Part I
Locally optimum nonlinearities for DCT watermark detection

IEEE Transactions on Image Processing

A robust detector of known signal in non-Gaussian noise using threshold systems

Signal Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we explore a stochastic resonance (SR) based detector using bistable system (BS) to detect a binary pulse amplitude modulated (PAM) signal embedded in non-Gaussian noise. Through the example of BS based watermark extraction, we show that a reliable performance cannot be obtained if the BS parameters are determined by traditional tuning technique. The key observation is that the BS parameters are not sensitive to the pdf of the noise but to the variance of the noise and the amplitude of the signal. That makes it possible to determine the BS parameters in advance and an adaptive BS can be constructed based on the estimated amplitude of the watermark (signal) and the variance of the DCT coefficients (noise). Experimental results show that the performance obtained from the proposed adaptive stochastic-resonator-based detector is stable and provides superior performance compared to the existing BS based watermark schemes and the Gaussian based maximum likelihood (ML) detector.