Abstract
An improved model for additive spread spectrum (SS) watermark detection on compressed sensing (CS) reconstructed images is presented in this paper. Mathematical form of detection threshold in log-likelihood ratio model is derived first and it is seen that detection probability depends on the embedding strength, watermark power, host signal variance on CS along with noise variance in observation/measurements. An optimization framework is then developed to minimize the visual distortion that includes reconstruction and embedding distortion while satisfying certain detection reliability constraint. An approximate closed form solution to the optimization problem in terms of embedding strength and set of appropriate host samples selection for a given number of CS measurements is derived and validated by a large set of simulations.
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