The goal of this paper is to propose an accurate method for estimating quantization steps from an image that has been previously JPEG-compressed and stored in lossless format. The method is based on the combination of the quantization effect and the statistics of discrete cosine transform (DCT) coefficient characterized by the statistical model that has been proposed in our previous works. The analysis of quantization effect is performed within a mathematical framework, which justifies the relation of local maxima of the number of integer quantized forward coefficients with the true quantization step. From the candidate set of the true quantization step given by the previous analysis, the statistical model of DCT coefficients is used to provide the optimal quantization step candidate. The proposed method can also be exploited to estimate the secondary quantization table in a double-JPEG compressed image stored in lossless format and detect the presence of JPEG compression. Numerical experiments on large image databases with different image sizes and quality factors highlight the high accuracy of the proposed method.

This paper investigates the problem of identifying the source imaging device of the same model for a natural raw image. The approach is based on the Poissonian-Gaussian noise model which can accurately describe the distribution of the given image. This model relies on two parameters considered as unique fingerprint to identify source cameras of the same model. The identification 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 performance is theoretically established. The statistical performance of LRT serves as an upper bound of the detection power. For a practice use, when the image parameters are unknown and camera parameters are known, a detector based on estimation of those parameters is designed. Numerical results on simulated data and real natural raw images highlight the relevance of our proposed approach.

The goal of this paper is to design a statistical test for the camera model identification problem. The approach is based on the state-of-the-art model of Discret Cosine Transform (DCT) coefficients to capture their statistical difference, which jointly results from different sensor noises and in-camera processing algorithms. The noise model parameters are considered as camera 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, this paper studies the optimal detector given by the Likelihood Ratio Test (LRT) and analytically establishes its statistical performances. In practice, a Generalized LRT is designed to deal with the difficulty of unknown parameters such that it can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated database and natural JPEG images highlight the relevance of the proposed approach.

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.

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.

This paper proposes a novel method, based on hypothesis testing theory, to detect data hidden with the LSB matching. When all the image parameters, a test which asymptotically maximizes the detection power and guarantees a false-alarm probability, is presented and its statistical properties are analytically given in a closed-form. This provides an asymptotic upper-bound for the power of any detector for LSB matching. In practice the image parameters are unknown. A Generalized Likelihood Ratio Test (GLRT) is proposed and its statistical properties are also analytically established. Numerical results and comparisons with prior art detectors highlight the relevance of the proposed methodology.

The goal of the paper is to propose an optimal statistical test for the steganalysis of Jsteg algorithm. The test is based on a state-of-the-art statistical model of quantized Discrete Cosine Transform (DCT) coefficients that allows us to reliably detect any small change in a cover image due to hidden information. By formulating the hidden information detection as a hypothesis testing problem, the paper designs the most powerful Likelihood Ratio Test (LRT) assuming that all model parameters are perfectly known. The statistical performance of the LRT is analytically provided. Numerical results and comparison with other detectors highlight the relevance of the proposed approach.