Performance analysis of existing and new methods for data hiding with known-host information in additive channels

Abstract

A considerable amount of attention has been lately payed to a number of data hiding methods based in quantization, seeking to achieve in practice the results predicted by Costa for a channel with side information at the encoder. With the objective of filling a gap in the literature, this paper supplies a fair comparison between significant representatives of both this family of methods and the former spread-spectrum approaches that make use of near-optimal ML decoding; the comparison is based on measuring their probabilities of decoding error in the presence of channel distortions. Accurate analytical expressions and tight bounds for the probability of decoding error are given and validated by means of Monte Carlo simulations. For Dithered Modulation (DM) a novel technique that allows to obtain tighter bounds to the probability of error is presented. Within the new framework, the strong points and weaknesses of both methods are distinctly displayed. This comparative study allows us to propose a new technique named "Quantized Projection" (QP), which by adequately combining elements of those previous approaches, produces gains in performance.

Logarithmic Adaptive Quantization Projection for Audio Watermarking

“…In paper [12], Perez-Gonzalez et al analyzed the performance of existing quantization algorithms and proposed the quantization projection (QP) method which couple the effectiveness of QIM scheme and spread spectrum methods. In the basic QP case, the projection function computes a weighted cross-correlation between the length-L watermarked signal y and projection vector b; therefore, for a single transmitted bit, the projection r is such that…”

“…For the watermarking purpose, w i is chosen to be proportional to a i . It can be shown that under the perceptual constraints, this choice minimizes the probability of error [12]. Then…”

“…To improve the fidelity of the QIM scheme, Perez-Gonzalez et al developed an algorithm called quantization projection (QP) [12] which combines the QIM methods with a perceptual model. But it has a problem that the perceptual model should be calculated in both embedding and decoding procedures.…”

Asymmetric spread spectrum data-hiding for Laplacian host data

“…Minimum distance decoding corresponds to the maximum likelihood decoder for all the above host pdfs and the specified noise pdf. The bit error probability is calculated as the integral of the equivalent noise Z e = X + Z over the error region R [9]: P e = R f Ze (z e )dz e , where…”

“…1 when the switch S is open, the equivalent noise is distributed as f Ze (z e ) ∼ N (0, σ 2 X + σ 2 Z ). Thus, it is possible to express the bit error probability P G e as a function of the host variance by [9]:…”