Rémi Cogranne

Selection-channel-aware rich model for Steganalysis of digital images

From the perspective of signal detection theory, it seems obvious that knowing the probabilities with which the individual cover elements are modified during message embedding (the so-called probabilistic selection channel) should improve steganalysis. It is, however, not clear how to incorporate this information into steganalysis features when the detector is built as a classifier. In this paper, we propose a variant of the popular spatial rich model (SRM) that makes use of the selection channel. We demonstrate on three state-of-the-art content-adaptive steganographic schemes that even an imprecise knowledge of the embedding probabilities can substantially increase the detection accuracy in comparison with feature sets that do not consider the selection channel. Overly adaptive embedding schemes seem to be more vulnerable than schemes that spread the embedding changes more evenly throughout the cover.

Content-Adaptive Steganography by Minimizing Statistical Detectability

Most current steganographic schemes embed the secret payload by minimizing a heuristically defined distortion. Similarly, their security is evaluated empirically using classifiers equipped with rich image models. In this paper, we pursue an alternative approach based on a locally estimated multivariate Gaussian cover image model that is sufficiently simple to derive a closed-form expression for the power of the most powerful detector of content-adaptive least significant bit matching but, at the same time, complex enough to capture the non-stationary character of natural images. We show that when the cover model estimator is properly chosen, the state-of-the-art performance can be obtained. The closed-form expression for detectability within the chosen model is used to obtain new fundamental insight regarding the performance limits of empirical steganalysis detectors built as classifiers. In particular, we consider a novel detectability limited sender and estimate the secure payload of individual images.

Camera Model Identification Based on the Heteroscedastic Noise Model

The goal of this paper is to design a statistical test for the camera model identification problem. The approach is based on the heteroscedastic noise model, which more accurately describes a natural raw image. This model is characterized by only two parameters, which are considered as unique fingerprint to identify camera models. The camera model identification 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. For a practical use, two generalized LRTs are designed to deal with unknown model parameters so that they can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated images and real natural raw images highlight the relevance of the proposed approach.

An Asymptotically Uniformly Most Powerful Test for LSB Matching Detection

This paper investigates the detection of information hidden in digital media by the least significant bit (LSB) matching scheme. In a theoretical context of known medium parameters, two important results are presented. First, based on the likelihood ratio test, we present a test that asymptotically maximizes the detection power whatever the embedding rate might be. Second, the statistical properties of this test are analytically calculated; it is particularly shown that the decision threshold which warrants a given probability of false-alarm is independent of inspected medium parameters. This provides an asymptotic upper-bound for the detection power of any test that aims at detecting data hidden with the LSB matching method. In practice, when detecting LSB matching, the unknown medium parameters have to be estimated. Based on a local model of digital media, a generalized likelihood ratio test is presented by replacing the unknown parameters by their estimation. Numerical results on large databases highlight the relevance of the proposed methodology and comparison with state-of-the-art detectors shows that the proposed tests perform well.

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.

Content-adaptive pentary steganography using the multivariate generalized Gaussian cover model

The vast majority of steganographic schemes for digital images stored in the raster format limit the amplitude of embedding changes to the smallest possible value. In this paper, we investigate the possibility to further improve the empirical security by allowing the embedding changes in highly textured areas to have a larger amplitude and thus embedding there a larger payload. Our approach is entirely model driven in the sense that the probabilities with which the cover pixels should be changed by a certain amount are derived from the cover model to minimize the power of an optimal statistical test. The embedding consists of two steps. First, the sender estimates the cover model parameters, the pixel variances, when modeling the pixels as a sequence of independent but not identically distributed generalized Gaussian random variables. Then, the embedding change probabilities for changing each pixel by 1 or 2, which can be transformed to costs for practical embedding using syndrome-trellis codes, are computed by solving a pair of non-linear algebraic equations. Using rich models and selection-channel-aware features, we compare the security of our scheme based on the generalized Gaussian model with pentary versions of two popular embedding algorithms: HILL and S-UNIWARD.

Statistical Model of Quantized DCT Coefficients: Application in the Steganalysis of Jsteg Algorithm

The goal of this paper is to propose a statistical model of quantized discrete cosine transform (DCT) coefficients. It relies on a mathematical framework of studying the image processing pipeline of a typical digital camera instead of fitting empirical data with a variety of popular models proposed in this paper. To highlight the accuracy of the proposed model, this paper exploits it for the detection of hidden information in JPEG images. By formulating the hidden data detection as a hypothesis testing, this paper studies the most powerful likelihood ratio test for the steganalysis of Jsteg algorithm and establishes theoretically its statistical performance. Based on the proposed model of DCT coefficients, a maximum likelihood estimator for embedding rate is also designed. Numerical results on simulated and real images emphasize the accuracy of the proposed model and the performance of the proposed test.

Rich model for Steganalysis of color images

In this paper, we propose an extension of the spatial rich model for steganalysis of color images. The additional features are formed by three-dimensional co-occurrences of residuals computed from all three color channels and their role is to capture dependencies across color channels. These CRMQ1 (color rich model) features are extremely powerful for detection of steganography in images that exhibit traces of color interpolation. Content-adaptive algorithms seem to be hurt much more because of their tendency to modify the same pixels in each channel. The efficiency of the proposed feature set is demonstrated on three different color versions of BOSSbase 1.01 and two steganographic algorithms - the non-adaptive LSB matching and WOW.

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.

Modeling and Extending the Ensemble Classifier for Steganalysis of Digital Images Using Hypothesis Testing Theory

The machine learning paradigm currently predominantly used for steganalysis of digital images works on the principle of fusing the decisions of many weak base learners. In this paper, we employ a statistical model of such an ensemble and replace the majority voting rule with a likelihood ratio test. This allows us to train the ensemble to guarantee desired statistical properties, such as the false-alarm probability and the detection power, while preserving the high detection accuracy of original ensemble classifier. It also turns out the proposed test is linear. Moreover, by replacing the conventional total probability of error with an alternative criterion of optimality, the ensemble can be extended to detect messages of an unknown length to address composite hypotheses. Finally, the proposed well-founded statistical formulation allows us to extend the ensemble to multi-class classification with an appropriate criterion of optimality and an optimal associated decision rule. This is useful when a digital image is tested for the presence of secret data hidden by more than one steganographic method. Numerical results on real images show the sharpness of the theoretically established results and the relevance of the proposed methodology.