Conference Paper

JPEG image steganalysis utilizing both intrablock and interblock correlations

Dept. of Electr. & Comput. Eng., New Jersey Inst. of Technol., Newark, NJ
DOI: 10.1109/ISCAS.2008.4542096 Conference: Circuits and Systems, 2008. ISCAS 2008. IEEE International Symposium on
Source: IEEE Xplore


JPEG image steganalysis has attracted increasing attention recently. In this paper, we present an effective Markov process (MP) based JPEG steganalysis scheme, which utilizes both the intrablock and interblock correlations among JPEG coefficients. We compute transition probability matrix for each difference JPEG 2-D array to utilize the intrablock correlation, and "averaged" transition probability matrices for those difference mode 2-D arrays to utilize the interblock correlation. All the elements of these matrices are used as features for steganalysis. Experimental works over an image database of 7,560 JPEG images have demonstrated that this new approach has greatly improved JPEG steganalysis capability and outperforms the prior arts.

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    • "Then we compress the same raw image using the 100 perturbed quantization tables and convert them to stego images to simulate the testing images. Steganalysis features are extracted from these 101 images using Chen's features [13]. For visualization of the similarity among JPEG images, each JPEG image is represented by MPEG-7 low level features. "
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    ABSTRACT: Owing to the ever proliferation of digital cameras and image editing software, a large variety of JPEG quantization tables are used to compress JPEG images. As a result, learning-based steganalysis methods using a pre-selected quantization table for training images degrade significantly when the quantization table of testing images is different from the one used for training. Recognizing that it would be undesirable and not practical to train a steganalysis classifier with all possible quantization tables, we propose an approach that the differences in features extracted from images with different quantization tables are formulated as perturbations of those features. Then we define a stochastic sensitivity by the expected square of classifier output changes with respect to these feature perturbations to compute the robustness of classifiers with respect to perturbations. A Radial Basis Function Neural Network based steganalysis classifier trained by minimizing the sensitivity is proposed. Experimental results show that the proposed method outperforms learning methods such as Support Vector Machine and Radial Basis Function Neural Network without considering feature perturbations.
    Information Sciences 10/2014; 281:211–224. DOI:10.1016/j.ins.2014.05.028 · 4.04 Impact Factor
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    • "To demonstrate the efficiency of our new channel selection rule, four universal JPEG steganalyzers presented in [13]–[16] are employed, denoted by ClbJFMP-274 [13], MP-486 [14], ClbMP-324 [15] and POMM-98 [16], respectively, where the numbers 274, 486, 324 and 98 denote the total number of features utilized, Clb stands for calibration technique [17], JF stands JPEG features [18], MP for Markov features [19], and POMM represents the partially ordered Markov models [16]. To the best of our knowledge, these four steganalyzers are among the most effective universal JPEG steganalyzers in detecting today's JPEG steganographic schemes. "
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    ABSTRACT: In this paper, we present a new channel selection rule for joint photographic experts group (JPEG) steganography, which can be utilized to find the discrete cosine transform (DCT) coefficients that may introduce minimal detectable distortion for data hiding. Three factors are considered in our proposed channel selection rule, i.e., the perturbation error (PE), the quantization step (QS), and the magnitude of quantized DCT coefficient to be modified (MQ). Experimental results demonstrate that higher security performance can be obtained in JPEG steganography via our new channel selection rule.
    IEEE Transactions on Information Forensics and Security 08/2012; 7(4):1181-1191. DOI:10.1109/TIFS.2012.2198213 · 2.41 Impact Factor
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    • "Modern feature-based steganalysis starts with adopting an image model (a low-dimensional representation) within which steganalyzers are built using machine learning tools. The model is usually determined not only by the characteristics of the cover source but also by the effects of embedding [4], [6], [14], [18], [21], [33], [35], [38]. For example , the SPAM feature vector [33], which was seemingly proposed from a pure cover model, is in reality, too, driven by a specific case of steganography. "
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    ABSTRACT: We describe a novel general strategy for building steganography detectors for digital images. The process starts with assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters. In contrast to previous approaches, we make the model assembly a part of the training process driven by samples drawn from the corresponding cover- and stego-sources. Ensemble classifiers are used to assemble the model as well as the final steganalyzer due to their low computational complexity and ability to efficiently work with high-dimensional feature spaces and large training sets. We demonstrate the proposed framework on three steganographic algorithms designed to hide messages in images represented in the spatial domain: HUGO, edge-adaptive algorithm by Luo , and optimally coded ternary $\pm {\hbox{1}}$ embedding. For each algorithm, we apply a simple submodel-selection technique to increase the detection accuracy per model dimensionality and show how the detection saturates with increasing complexity of the rich model. By observing the differences between how different submodels engage in detection, an interesting interplay between the embedding and detection is revealed. Steganalysis built around rich image models combined with ensemble classifiers is a promising direction towards automatizing steganalysis for a wide spectrum of steganographic schemes.
    IEEE Transactions on Information Forensics and Security 06/2012; 7(3):868-882. DOI:10.1109/TIFS.2012.2190402 · 2.41 Impact Factor
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