Luis Perez-Freire

Security of Lattice-Based Data Hiding Against the Known Message Attack

Security of quantization index modulation (QIM) watermarking methods is usually sought through a pseudorandom dither signal which randomizes the codebook. This dither plays the role of the secret key (i.e., a parameter only shared by the watermarking embedder and decoder), which prevents unauthorized embedding and/or decoding. However, if the same dither signal is reused, the observation of several watermarked signals can provide sufficient information for an attacker to estimate the dither signal. This paper focuses on the cases when the embedded messages are either known or constant. In the first part of this paper, a theoretical security analysis of QIM data hiding measures the information leakage about the secret dither as the mutual information between the dither and the watermarked signals. In the second part, we show how set-membership estimation techniques successfully provide accurate estimates of the dither from observed watermarked signals. The conclusion of this twofold study is that current QIM watermarking schemes have a relative low security level against this scenario because a small number of observed watermarked signals yields a sufficiently accurate estimate of the secret dither. The analysis presented in this paper also serves as the basis for more involved scenarios

Detection in quantization-based watermarking: performance and security issues

In this paper, a novel method for detection in quantization-based watermarking is introduced. This method basically works by quantizing a projection of the host signal onto a subspace of smaller dimensionality. A theoretical performance analysis under AWGN and fixed gain attacks is carried out, showing great improvements over traditional spread-spectrum-based methods operating under the same conditions of embedding distortion and attacking noise. A security analysis for oracle-like attacks is also accomplished, proposing a sensitivity attack suited to quantization-based methods for the first time in the literature, and showing a trade-off between security level and performance; anyway, this new method offers significant improvements in security, once again, over spread-spectrum-based methods facing the same kind of attacks.

Modeling Gabor Coefficients via Generalized Gaussian Distributions for Face Recognition

Gabor filters are biologically motivated convolution kernels that have been widely used in the field of computer vision and, specially, in face recognition during the last decade. This paper proposes a statistical model of Gabor coefficients extracted from face images using generalized Gaussian distributions (GGDs). By measuring the Kullback-Leibler distance (KLD) between the pdf of the GGD and the relative frequency of the coefficients, we conclude that GGDs provide an accurate modeling. The underlying statistics allow us to reduce the required amount of data to be stored (i.e. data compression) via Lloyd-Max quantization. Verification experiments on the XM2VTS database show that performance does not drop when, instead of the original data, we use quantized coefficients.

Revealing the true achievable rates of scalar Costa scheme

By abandoning the assumption of an infinite document to watermark ratio, we recompute the achievable rates for Eggers scalar Costa scheme (SCS, also known as scalar distortion compensated dither modulation) and show, as opposed to the results reported by Eggers, that the achievable rates of SCS are always larger than those of spread spectrum (SS). Moreover, we show that for small watermark to noise ratios, SCS equivalent to a two-centroid problem, thus revealing interesting relations with SS and with Malvars improved spread spectrum (ISS). We also show an interesting behavior for the optimal distortion compensation parameter. All these results aim at filling an existing gap in watermarking theory and have important consequences for the design of efficient decoders for data hiding problems.

An accurate analysis of scalar quantization-based data hiding

This paper comes to fill a gap in watermarking theory, analyzing the exact performance of the scalar Costa scheme (SCS) facing additive Gaussian attacks when the usual approximation of high-resolution quantization is not valid, thus taking into account the host statistics. The analysis is focused on the assessment of the probability of error, showing new results, an although it is valid in a general scenario, its practical interest is increased when SCS is used in conjunction with the so-called spread-transform. The accomplished reformulation of the problem also permits to show that the achievable rate of SCS is never worse than that of classical spread-spectrum-based methods, as it was thought so far, and allows to establish interesting links with spread spectrum and the Improved Spread Spectrum method.

The blind Newton sensitivity attack

Until now, the sensitivity attack was considered as a serious threat to the robustness and security of spread spectrum-based schemes, since it provides a practical method of removing watermarks with minimum attacking distortion. Nevertheless, it had not been used to tamper other watermarking algorithms, as those which use side-information. Furthermore the sensitivity attack has never been used to obtain falsely watermarked contents, also known as forgeries. In this paper a new version of the sensitivity attack based on a general formulation is proposed; this method does not require any knowledge about the detection function nor any other system parameter, but just the binary output of the detector, thus being suitable for attacking most known watermarking methods, both for tampering watermarked signals and obtaining forgeries. The soundness of this new approach is tested by empirical results.

Spread-spectrum vs. quantization-based data hiding: misconceptions and implications

The performance of quantization-based data hiding methods is commonly analyzed by assuming a flat probability density function for the host signal, i.e. uniform inside each quantization cell and with its variance large enough to assuming that all the centroids occur with equal probability. This paper comes to fill a gap in watermarking theory, analyzing the exact performance of the Scalar Costa Scheme (SCS) facing additive Gaussian attacks when the former approximation is not valid, thus taking into account the host statistics. The accomplished analysis reveals that the true performance of such a scheme for an optimal selection of its parameters and low watermark to noise ratios (WNR) is never worse than that of classical spread-spectrum-based methods, in terms of achievable rate and probability of error, as it was thought so far. The reduction of SCS to a two-centroid problem allows the derivation of theoretical expressions which characterize its behavior for small WNRs, showing interesting connections with spread-spectrum (SS) and the Improved Spread Spectrum (ISS) method. Furthermore, we show that, in contrast to the results reported until now, the use of pseudorandom dithering in SCS-based schemes can have a negative impact in performance. Performance losses are also reported for the case in which a modulo reduction is undertaken prior to decoding. The usefulness of these results is shown in the computation of the exact performance in projected domains.