Quantization-based watermarking performance improvement using host statistics: AWGN attack case

ABSTRACT

In this paper we consider the problem of performance improvement of known-host-state (quantization-based) watermarking methods undergo Additive White Gaussian noise (AWGN) attack. The motivation of our research is twofold. The first reason concerns the common belief that any knowledge about the host image taken into account designing quantization-based watermarking algorithms can not improve their performance. The second reason refers to the poor practical performance of this class of methods at low Watermark-to-Noise Ratio (WNR) regime in comparison to the known-host-statistics techniques when AWGN attack is applied. We demonstrate in this paper that bit error probability of Dither Modulation (DM) and Distortion-Compensated Dither Modulation (DC-DM) against AWGN attack can be significantly reduced when the quantizers are designed using the statistics of the host data. For the case when the statistics of the data correspond to i.i.d. Laplacian distribution and using Uniform Deadzone Quantizer (UDQ) we develop close-form analytical models for the analysis of bit error probability of DM and DC-DM. Results of performed experiments demonstrate that significant performance improvement of classical DM and DC-DM with respect to bit error probability can be achieved with the minor increase of design complexity.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.m MISCELLANEOUS

General Terms:
Performance

Keywords:
data hiding, known-host-state watermarking