In order to achieve high practical security, Natural Steganography (NS) uses cover images captured at ISO sensitivity ISO1and generates stego images mimicking ISO sensitivity ISO2> ISO1. This is achieved by adding a stego signal to the cover that mimics the sensor photonic noise. This paper proposes an embedding mechanism to perform NS in the JPEG domain after linear developments by explicitly computing the correlations between DCT coefficients before quantization. In order to compute the covariance matrix of the photonic noise in the DCT domain, we first develop the matrix representation of demosaicking, luminance averaging, pixel section, and 2D-DCT. A detailed analysis of the resulting covariance matrix is done in order to explain the origins of the correlations between the coefficients of 3 × 3 DCT blocks. An embedding scheme is then presented that takes into account all the correlations. It employs 4 sub-lattices and 64 lattices per sub-lattices. The modification probabilities of each DCT coefficient are then derived by computing conditional probabilities computed from a multivariate Gaussian distribution using the Cholesky decomposition of the covariance matrix. This derivation is also used to compute the embedding capacity of each image. Using a specific database called E1Base, we show that in the JPEG domain NS (J-Cov-NS) enables to achieve high capacity (more than 2 bits per non-zero AC DCT) and with high practical security (PE 40% using DCTR and PE 32% using SRNet) from QF 75 to QF 100).

This paper proposes to use the statistical analysis of the correlation between DCT coefficients to design a new synchronization strategy that can be used for cost-based steganographic schemes in the JPEG domain. First, an analysis is performed on the covariance matrix of DCT coefficients of neighboring blocks after a development pipeline similar to the one used to generate BossBase, and applied on a photonic noise. This analysis exhibits (i) a decomposition into 8 disjoint sets of uncorrelated coefficients (4 sets per block used by 2 disjoint lattices) and (ii) the fact that each DCT coefficient is correlated with 38 other coefficients belonging either to the same block or to connected blocks. Using the uncorrelated groups, an embedding scheme can be designed using only 8 disjoint lattices. The proposed embedding scheme relies on ingredients. Firstly, we convert the empirical costs associated to one each coefficient into a Gaussian distribution whose variance is directly computed from the embedding costs. Secondly we derive conditional Gaussian distributions from a multivariate distribution considering only the correlated coefficients which have been already modified by the embedding scheme. This covariance matrix takes into account both the correlations exhibited by the analysis of the covariance matrix and the variance derived from the costs. This synchronization scheme enables to obtain a gain of $P_E$ of at least $7%$ at $QF95$ for an embedding rate close to 0.3 bnzac coefficient using DCTR feature sets for both UERD and JUniward.

This short paper is an extension of a family of embedding schemes called Natural Steganography, which embeds a message by mimicking heteroscedastic sensor noise in the JPEG domain. Under the assumption that the development from RAW uses linear de- mosaicking, we derive a closed-form for the covariance matrix of DCT coefficients from 3 × 3 JPEG blocks. This computation relies on a matrix formulation of all steps involved in the development pipeline, which includes demosaicking, conversion to luminance, DCT transform, and reordering. This matrix is then used for pseudo-embedding in the JPEG domain on four lattices of 8 × 8 DCT blocks. The results obtained with the computed covariance matrix are contrasted with the results previously obtained with the covariance matrix estimated using Monte Carlo sampling and scaling. The empirical security using DCTR features at JPEG quality 100 increased from PE = 14% using covariance estimation and scaling to PE = 43% using the newly derived analytic form.

This paper combines the principles of statistical estimation and hypothesis testing to analyze the impact of parameter estimation on an authentication system based on graphical codes. The studied authentication system uses the fact that a code, once printed, undergoes a stochastic and non invertible alteration. A statistical test applies a likelihood ratio between the model of the authentic printed and scanned image and the model of the reproduced one, with the particularity here that the later model is unknown. The proposed solution consists in using an optimal estimation of the image model coming from observed fake codes in order to perform the likelihood test. Using a second order expansion, we derive a linear relation between the quadratic error of the estimated parameters and the probability of type II error. We are then able to formulate analytically and practically the error spread region of the Receiver Operating Characteristic (ROC) curves, and to compute the average authentication performance when the receiver has to estimate the opponent print and scan channel.

This paper proposes to investigate the impact of the channel model for authentication systems based on codes that are corrupted by a physically unclonable noise such as the one emitted by a printing process.The core of such a system for the receiver is to perform a statistical test in order to recognize and accept an original code corrupted by noise and reject any illegal copy or a counterfeit.This study highlights the fact that the probability of type I and type II errors can be better approximated, by several orders of magnitude, when using the Cramér-Chernoff theorem instead of a Gaussian approximation.The practical computation of these error probabilities is also possible using Monte Carlo simulations combined with the importance sampling method.By deriving the optimal test within a Neyman-Pearson setup, a first theoretical analysis shows that a thresholding of the received code induces a loss of performance.A second analysis proposes to find the best parameters of the channels involved in the model in order to maximize the authentication performance.This is possible not only when the opponent's channel is identical to the legitimate channel but also when the opponent's channel is different, leading this time to a min-max game between the two players.Finally, we evaluate the impact of an uncertainty for the receiver on the opponent channel, and we show that the authentication is still possible whenever the receiver can observe forged codes and uses them to estimate the parameters of the model.

This paper proposes to investigate the impact of the channel model for authentication systems based on codes that are corrupted by a physically unclonable noise such as the one emitted by a printing process. The core of such a system is the comparison for the receiver between an original binary code, an original corrupted code and a copy of the original code. We analyze two strategies, depending on whether or not the receiver use a binary version of its observation to perform its authentication test. By deriving the optimal test within a Neyman-Pearson setup, a theoretical analysis shows that a thresholding of the code induces a loss of performance. This study also highlights the fact that the probability of the type I and type II errors can be better approximated, by several orders of magnitude, computing Chernoff bounds instead of the Gaussian approximation. Finally we evaluate the impact of an uncertainty for the receiver on the opponent channel and show that the authentication is still possible whenever the receiver can observe forged codes and uses them to estimate the parameters of the model.

In this paper, we present a joint blind channel estimation and symbol detection for decoding a blurred and noisy 1D barcode captured image. From an information transmission point of view, we show that the channel impulse response, the noise power and the symbols can be efficiently estimated by taking into account the signal structure such as the cyclostationary property of the hidden Markov process to estimate. Based on the Expectation-Maximisation method, we show that the new algorithm offers significative performance gain compared to classical ones pushing back the frontiers of the barcode technology.

Recently, wireless technologies have known an increasing success, thus becoming an interesting field for study. One of the topics of research are multiple antenna arrays. Signal recovery in that case is not obvious. Different methods with different performances have been suggested. From a statistical analysis of errors at the output of zero-forcing, we have constructed a method, which we present in this article, that combines zero-forcing and maximum likelihood. We will demonstrate that it provides both a low complexity and nearly optimal results regarding binary error rate (BER). Furthermore, we will show its relevancy compared to the VBLAST algorithm.

In this paper we investigate observed data of 60 Ghz channel impulse responses collected in IEMN. We first investigate the wide sense stationary and uncorrelated scatterers properties of the channel. We then develop a tapped delay line model and MC simulate a DS-CDMA transmission with a RAKE receiver. Results show the good performances of such a scheme and make DS-CDMA a good solution for high rate multi-media transmission at 60 GHz. Besides, the simulation rapidity allows to use this model for channel coding simulation.