Thanh Hai Thai

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.

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.

Statistical detection of data hidden in least significant bits of clipped images

This paper studies the statistical detection of data hidden in the Least Significant Bits (LSB) plan of natural clipped images using the hypothesis testing theory. The main contributions are the following. First, this paper proposes to exploit the heteroscedastic noise model. This model, characterized by only two parameters, explicitly provides the noise variance as a function of pixel expectation. Using this model enhances the noise variance estimation and hence, allows the improving of detection performance of the ensuing test. Second, this paper introduces the clipped phenomenon caused by the limited dynamic range of the imaging device. Overexposed and underexposed pixels are statistically modeled and specifically taken into account to allow the inspecting of images with clipped pixels. While existing methods in the literature fail when the data is embedded in clipped images, the proposed detector still ensures a high detection performance. The statistical properties of the proposed GLRT are analytically established showing that this test is a Constant False Alarm Rate detector: it guarantees a prescribed false alarm probability.

JPEG Quantization Step Estimation and Its Applications to Digital Image Forensics

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.

Camera model identification based on the generalized noise model in natural images

The goal of this paper is to design a statistical test for the camera model identification problem. The approach is based on the generalized noise model that is developed by following the image processing pipeline of the digital camera. More specifically, this model is given by starting from the heteroscedastic noise model that describes the linear relation between the expectation and variance of a RAW pixel and taking into account the non-linear effect of gamma correction. The generalized noise model characterizes more accurately a natural image in TIFF or JPEG format. The present paper is similar to our previous work that was proposed for camera model identification from RAW images based on the heteroscedastic noise model. The parameters that are specified in the generalized noise model are used 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, the Likelihood Ratio Test is presented and its statistical performances are theoretically established. In practice when the model parameters are unknown, two Generalized Likelihood Ratio Tests are designed to deal with this difficulty such that they can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated images and real natural JPEG images highlight the relevance of the proposed approach. Camera model identification is addressed using hypothesis testing theory.The statistical performance of the proposed test is analytically established.The generalized noise model is exploited in the proposed test.A novel camera fingerprint is proposed.

Camera model identification based on DCT coefficient statistics

The goal of this paper is to design a statistical test for the camera model identification problem from JPEG images. The approach focuses on extracting information in Discrete Cosine Transform (DCT) domain. The main motivation is that the statistics of DCT coefficients change with different sensor noises combining with various in-camera processing algorithms. To accurately capture this information, this paper relies on the state-of-the-art model of DCT coefficients proposed in our previous work. The DCT coefficient model is characterized by two parameters ( α , β ) . The parameters ( c , d ) that characterize the simplified relation between these parameters are exploited as camera fingerprint for camera model identification. 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 is presented and its performances are theoretically established. For a practical use, two Generalized Likelihood Ratio Tests are designed to deal with unknown model parameters such that they can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated and real JPEG images highlight the relevance of the proposed approach. Camera model identification is addressed using hypothesis testing theory.The statistical performance of the proposed test is analytically established.The model of DCT coefficients is exploited in the proposed test.A novel camera fingerprint in the DCT domain is proposed.

Steganalysis of Jsteg algorithm based on a novel statistical model of quantized DCT coefficients

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.

Generalized signal-dependent noise model and parameter estimation for natural images

The goal of this paper is to propose a generalized signal-dependent noise model that is more appropriate to describe a natural image acquired by a digital camera than the conventional Additive White Gaussian Noise model widely used in image processing. This non-linear noise model takes into account effects in the image acquisition pipeline of a digital camera. In this paper, an algorithm for estimation of noise model parameters from a single image is designed. Then the proposed noise model is applied with the Local Linear Minimum Mean Square Error filter to design an efficient image denoising method. HighlightsA novel noise model of digital image pixel?s is proposed.The heteroscedastic model for RAW image is extended to rendered images.This model is shown to outperform the state-of-the-art models.A simple yet efficient method for parameters estimation is also proposed.An application for denoising of digital still images in also presented.

Source camera device identification based on raw images

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.

Optimal detector for camera model identification based on an accurate model of DCT coefficients

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.