Spread Spectrum Watermarking: Principles and Applications in Fading Channel

ISBN 978-953-51-0618-

Fuzzy-GA Hybridization in M-Band Wavelets for Collusion Resilient Optimized SS Watermarking

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Adaptive Watermark Power Control for Capacity Optimized MC-CDMA System

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Collusion resilient spread spectrum watermarking in M-band wavelets using GA-fuzzy hybridization

International audienceThis paper proposes a collusion resilient optimized spread spectrum (SS) image watermarking scheme using genetic algorithms (GA) and multiband (M-band) wavelets. M-band decomposition of the host image offers advantages of better scale-space tiling and good energy compactness. This bandpass-like decomposition makes watermarking robust against frequency selective fading-like gain (intelligent collusion) attack. On the other hand, GA would determine threshold value of the host coefficients (process gain i.e. the length of spreading code) selection for watermark casting along with the respective embedding strengths compatible to the gain of frequency response. First, a single bit watermark embedding algorithm is developed using independent and identically distributed (i.i.d) Gaussian watermark. This is further modified to design a high payload system for binary watermark image using a set of binary spreading code patterns. Watermark decoding performance is improved by multiple stage detection through cancelation of multiple bit interference (MBI) effect. Fuzzy logic is used to classify decision magnitudes in multiple group combined interference cancelation (MGCIC) used in the intermediate stage(s). Simulation results show convergence of GA and validate relative performance gain achieved in this algorithm compared to the existing works

Fuzzy-GA Hybridization in M-Band Wavelets for Collusion Resilient Optimized SS Watermarking

International audienc

Perceptually Adaptive MC-SS Image Watermarking using GA-NN Hybridization in Fading Gain

International audienceThis paper proposes an optimized multicarrier (MC) spread spectrum (SS) image watermarking scheme using hybridization of genetic algorithms (GA) and neural networks (NN). Data embedding is done in the mutually independent host components using the distinct code patterns that are assigned to the different watermark bits. GA determines the gradient thresholds for the pixel intensities to partition the host image into the edge, the smooth, and the texture regions as well as determines the watermark embedding strengths. The goal is to optimize the imperceptibility and the data hiding capacity. A minimum mean square error combining (MMSEC) decoder is used and the weight factors are calculated using NN through training/learning. Stable decision variables thus obtained for the watermark bit detection are partitioned into the multiple groups to improve decoder performance by canceling out the multiple bit interfering effect. Simulation results show the relative performance gain achieved in this method compared to the existing other works including the biologically inspired approaches

Optimized Spread Spectrum Watermarking for Fading-like Collusion Attack

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Optimized Spread Spectrum Watermarking for Fading-like Collusion Attack

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