Pooled Steganalysis in JPEG: how to deal with the spreading strategy?

International audienceIn image pooled steganalysis, a steganalyst, Eve, aims to detect if a set of images sent by a steganographer, Alice, to a receiver, Bob, contains a hidden message. We can reasonably assess that the steganalyst does not know the strategy used to spread the payload across images. To the best of our knowledge, in this case, the most appropriate solution for pooled steganalysis is to use a Single-Image Detector (SID) to estimate/quantify if an image is cover or stego, and to average the scores obtained on the set of images. In such a scenario, where Eve does not know the spreading strategies, we experimentally show that if Eve can discriminate among few well-known spreading strategies, she can improve her steganalysis performances compared to a simple averaging or maximum pooled approach. Our discriminative approach allows obtaining steganalysis efficiencies comparable to those obtained by a clairvoyant, Eve, who knows the Alice spreading strategy. Another interesting observation is that DeLS spreading strategy behaves really better than all the other spreading strategies. Those observations results in the experimentation with six different spreading strategies made on Jpeg images with J-UNIWARD, a state-of-the-art Single-Image-Detector, and a dis-criminative architecture that is invariant to the individual payload in each image, invariant to the size of the analyzed set of images, and build on a binary detector (for the pooling) that is able to deal with various spreading strategies

Individual camera device identification from JPEG images

International audienceThe goal of this paper is to investigate the problem of source camera device identification for natural images in JPEG format. We propose an improved signal-dependent noise model describing the statistical distribution of pixels from a JPEG image. The noise model relies on the heteroscedastic noise parameters, that relates the variance of pixels’ noise with the expectation considered as unique fingerprints. It is also shown in the present paper that, non-linear response of pixels can be captured by characterizing the linear relation because those heteroscedastic parameters, which are used to identify source camera device. The identification problem is cast within 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 performance is theoretically established. The statistical performance of LRT serves as an upper bound of the detection power. In a practical identification, when the nuisance parameters are unknown, two generalized LRTs based on estimation of those parameters are established. Numerical results on simulated data and real natural images highlight the relevance of our proposed approach. While those results show a first positive proof of concept of the method, it still requires to be extended for a relevant comparison with PRNU-based approaches that benefit from years of experience

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

International audienceThe 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 multiclass classification with an appropriate criterion of optimality and an optimal associated decision rule. This is useful when a digital image is tested for 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