Locally optimum detection for additive watermarking in the DCT and DWT domains through non-Gaussian distributions
Antonis Mairgiotis, Lisimachos Paul Kondi, Yongyi Yang · 2013
This work presents a new locally optimal blind detector for the additive transform-based image watermarking problem. Working in non-Gaussian environments, we introduce a new statistical model and its consequent application in the design of a locally optimum detection test. More specifically, we model the marginal distributions of the detail subband coefficients of DWT (Discrete Wavelet Transform) or DCT (Discrete Cosine Transform) with Student-t distribution. Since the watermark signal has low power, locally most powerful (LMP) detector is a valid choice. The experimental results show that the proposed detector has superior performance than alternative LMP detectors based on known state of the art statistical models.