This paper presents a scheme for estimating two-band amplitude scale attack within a quantization-based watermarking context. Quantization-based watermarking schemes comprise a class of watermarking schemes that achieves the channel capacity in terms of additive noise attacks. Unfortunately, Quantization-based watermarking schemes are not robust against Linear Time Invariant (LTI) filtering attacks. We concentrate on a multi-band amplitude scaling attack that modifies the spectrum of the signal using an analysis/synthesis filter bank. First we derive the probability density function (PDF) of the attacked data. Second, using a simplified approximation of the PDF model, we derive a Maximum Likelihood (ML) procedure for estimating two-band amplitude scaling factor. Finally, experiments are performed with synthetic and real audio signals showing the good performance of the proposed estimation technique under realistic conditions.
Quantization-based watermarking schemes comprise a class of watermarking schemes that achieves the channel capacity in terms of additive noise attacks. The existence of good high dimensional lattices that can be efficiently implemented and incorporated into watermarking structures, made quantization-based watermarking schemes
of practical interest. Because of the structure of the lattices, watermarking schemes making use of them are vulnerable to non-additive operations, like amplitude scaling in combination with additive noise. In this paper, we propose a secure Maximum Likelihood (ML) estimation technique for amplitude scaling
factors using subtractive dither. The dither has mainly security purposes and is assumed to be known to the watermark encoder and decoder. We derive the probability density function (PDF) models of the watermarked and attacked data in the presence of subtractive dither. The derivation of these models follows the lines of reference 5, where we derived the PDF models in the absence of dither. We derive conditions for the dither sequence statistics
such that a given security level is achieved using the error probability of the watermarking system as objective function. Based on these conditions we are able to make approximations to the PDF models that are used in the ML estimation procedure. Finally, experiments are performed with real audio and speech signals showing
the good performance of the proposed estimation technique under realistic conditions.
Quantization-based watermarking schemes are vulnerable to
amplitude scaling. Therefore the scaling factor has to be
accounted for either at the encoder, or at the decoder, prior to
watermark decoding. In this paper we derive the marginal
probability density model for the watermarked and attacked data,
when the attack channel consists of amplitude scaling followed by
additive noise. The encoder is Quantization Index Modulation with
Distortion Compensation. Based on this model we obtain two
estimation procedures for the scale parameter. The first approach
is based on Fourier Analysis of the probability density function. The estimation of the
scaling parameter relies on the structure of the received data.
The second approach that we obtain is the Maximum Likelihood
estimator of the scaling factor. We study the performance of the
estimation procedures theoretically and experimentally with real
audio signals, and compare them to other well known approaches for
amplitude scale estimation in the literature.