Cathel Zitzmann

A cover image model for reliable steganalysis

This paper investigates reliable steganalysis of natural cover images using a local non-linear parametric model. In the framework of hypothesis testing theory, the use of the model permits to warrant predictable results under false alarm constraint.

Statistical decision methods in hidden information detection

The goal of this paper is to show how the statistical decision theory based on the parametric statistical model of the cover media can be useful in theory and practice of hidden information detection.

Hidden information detection based on quantized Laplacian distribution

The goal of this paper is to propose the optimal statistical test based on the modeling of discrete cosine transform (DCT) coefficients with a quantified Laplacian distribution. This paper focuses on the detection of hidden information embedded in bits of the DCT coefficients of a JPEG image. This problem is difficult, in terms of statistical decision, for two main reasons: first, the number of DCT coefficients used to conceal the hidden bits is random; second, the JPEG image compression induces a strong quantization of DCT coefficients. The proposed test explicitly takes into account the randomness of the number of DCT coefficients used. It maximizes the probability of hidden information detection by ensuring a prescribed level of false alarm.

Statistical decision by using quantized observations

In the last two decades substantial progress has been made in the detection of hidden information or hidden communication channels in media files or streams. Typically, it is necessary to reliably detect in a huge set of files (image, audio, and video) which of these files contain the hidden information. The goal of this paper is to study the problem of hypothesis testing based on quantized observations by using a parametric statistical model with nuisance parameters and to apply the obtained tests to the hidden information detection.

A local adaptive model of natural images for almost optimal detection of hidden data

This paper proposes a novel methodology to detect data hidden in the least significant bits of a natural image. The goal is twofold: first, the methodology aims at proposing a test specifically designed for natural images, to this end an original model of images is presented, and, second, the statistical properties of the designed test, probability of false alarm and power function, should be predictable. The problem of hidden data detection is set in the framework of hypothesis testing theory. When inspected image parameters are known, the Likelihood Ratio Test (LRT) is designed and its statistical performance is analytically established. In practice, unknown image parameters have to be estimated. The proposed model of natural images is used to estimate unknown parameters accurately and to design a Generalized Likelihood Ratio Test (GLRT). Finally, the statistical properties of the proposed GLRT are analytically established which permits us, first, to guarantee a prescribed false-alarm probability and, second, to show that the GLRT is almost as powerful as the optimal LRT. Numerical results on natural image databases and comparison with prior art steganalyzers show the relevance of theoretical findings.

Statistical detection of LSB matching using hypothesis testing theory

This paper investigates the detection of information hidden by the Least Significant Bit (LSB) matching scheme. In a theoretical context of known image media parameters, two important results are presented. First, the use of hypothesis testing theory allows us to design the Most Powerful (MP) test. Second, a study of the MP test gives us the opportunity to analytically calculate its statistical performance in order to warrant a given probability of false-alarm. In practice when detecting LSB matching, the unknown image parameters have to be estimated. Based on the local estimator used in the Weighted Stego-image (WS) detector, a practical test is presented. A numerical comparison with state-of-the-art detectors shows the good performance of the proposed tests and highlights the relevance of the proposed methodology.

Reliable detection of hidden information based on a non-linear local model

This paper investigates the reliable detection of information hidden in natural images. It is aimed to design a test with analytically predictable probabilities of error. To this end, the problem of hidden information detection is cast in the framework of hypothesis testing. The optimal test solving the decision problem of steganalysis requires image parameters which are not available in practice. To design a feasible test, a non-linear locally-adapted model of natural images is proposed. This model is linearized to allow an efficient and simple estimation of image parameters which leads to the design of an almost optimal test. Numerical results on a large number of natural images show the relevance of the theoretical findings.

Hidden information detection using decision theory and quantized samples: Methodology, difficulties and results

This paper studies the detection of Least Significant Bits (LSB) steganography in digital media by using hypothesis testing theory. The main goal is threefold: first, it is aimed to design a test whose statistical properties are known, this especially allows the guaranteeing of a false alarm probability. Second, the quantization of samples is studied throughout this paper. Lastly, the use of a linear parametric model of samples is used to estimate unknown parameters and design a test which can be used when no information on cover medium is available. To this end, the steganalysis problem is cast within the framework of hypothesis testing theory and digital media are considered as quantized signals. In a theoretical context where media parameters are assumed to be known, the Likelihood Ratio Test (LRT) is presented. Its statistical performances are analytically established; this highlights the impact of quantization on the most powerful steganalyzer. In a practical situation, when image parameters are unknown, a Generalized LRT (GLRT) is proposed based on a local linear parametric model of samples. The use of such model allows us to establish GLRT statistical properties in order to guarantee a prescribed false-alarm probability. Focusing on digital images, it is shown that the well-known WS (Weighted-Stego) is close to the proposed GLRT using a specific model of cover image. Finally, numerical results on natural images show the relevance of theoretical findings.

Statistical detection of LSB matching in the presence of nuisance parameters

This paper investigates the reliable detection of information embedded with the least significant bits (LSB) matching scheme. It is aimed to design a test with analytically predictable error probabilities. To this end, the problem of hidden information detection is cast in the framework of hypothesis testing theory. In order to deal with nuisance parameters a rejection approach is used with a statistical model of medium. The use of a linear parametric model permits to analytically express statistical performance of proposed test. Numerical simulations and comparisons with state-of-the-art detectors highlight the relevance of proposed approach.

Statistical detection of Jsteg steganography using hypothesis testing theory

This paper investigates the statistical detection of Jsteg steganography. The approach is based on the statistical model of Discrete Cosine Transformation (DCT) coefficients. The hidden information detection problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the Likelihood Ratio Test (LRT) is presented and its performances are theoretically established. The statistical performance of LRT serves as an upper bound of the detection power. For a practical use, when the distribution parameters are unknown, a detector based on estimation of those parameters is designed. The loss of power of the proposed detector, compared with the optimal LRT is small, which shows the relevance of the proposed approach.