Lattice-Based Minimum-Distortion Data Hiding

TL;DR

This paper introduces MD-QIM, a lattice-based data hiding scheme that minimizes distortion by moving data points to Voronoi region boundaries, outperforming traditional QIM in low-distortion applications.

Contribution

The paper proposes MD-QIM, a minimum-distortion variant of QIM, focusing on reducing signal distortion at the expense of robustness to noise.

Findings

MD-QIM significantly reduces mean square error compared to QIM.

MD-QIM achieves higher PSNR and lower PRD in simulations.

The scheme is optimal for applications prioritizing minimal distortion.

Abstract

Lattices have been conceived as a powerful tool for data hiding. While conventional studies and applications focus on achieving the optimal robustness versus distortion tradeoff, in some applications such as data hiding in medical/physiological signals, the primary concern is to achieve a minimum amount of distortion to the cover signal. In this paper, we revisit the celebrated quantization index modulation (QIM) scheme and propose a minimum-distortion version of it, referred to as MD-QIM. The crux of MD-QIM is to move the data point to only the boundary of the Voronoi region of the lattice point indexed by a message, which suffices for subsequent correct decoding. At any fixed code rate, the scheme achieves the minimum amount of distortion by sacrificing the robustness to the additive white Gaussian noise (AWGN) attacks. Simulation results confirm that our scheme significantly outperforms QIM in terms of mean square error (MSE), peak signal to noise ratio (PSNR) and percentage residual difference (PRD).