Information hiding with maximum likelihood detector for correlated signals |
| |
| Affiliation: | 1. Harvard Medical School;2. VA Boston HealthCare System;3. University of Chicago;4. University of Texas Southwestern Medical Center;5. Rosalind Franklin University;6. Harvard University;7. University of Georgia;8. University of Minnesota;9. Alliance University, Bangalore, India;10. Yale University;11. University of Texas Rio Grande Valley, USA |
| |
| Abstract: | In this paper, a new scaling based information hiding approach with high robustness against noise and gain attack is presented. The host signal is assumed to be stationary Gaussian with first-order autoregressive model. For data embedding, the host signal is divided into two parts, and just one patch is manipulated while the other one is kept unchanged for parameter estimation. A maximum likelihood (ML) decoder is proposed which uses the ratio of samples for decoding the watermarked data. Due to the decorrelating property of the proposed decoder, it is very efficient for watermarking highly correlated signals for which the decoding process is not straightforward. By calculating the distribution of the decision variable, the performance of the decoder is analytically studied. To verify the validity of the proposed algorithm, it is applied to artificial Gaussian autoregressive signals. Simulation results for highly correlated host signals confirm the robustness of our decoder. |
| |
| Keywords: | Maximum likelihood detector Gaussian distribution First-order autoregressive model Information hiding Scaling based rule |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
