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

ABSTRACT 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.

1 Follower
  • Source
    [Show abstract] [Hide abstract]
    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 · 3.89 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    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 · 2.07 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Today, the most accurate steganalysis methods for digital media are built as supervised classifiers on feature vectors extracted from the media. The tool of choice for the machine learning seems to be the support vector machine (SVM). In this paper, we propose an alternative and wellknown machine learning tool – ensemble classifiers – and argue that they are ideally suited for steganalysis. Ensemble classifiers scale much more favorably w.r.t. the number of training examples and the feature dimensionality with performance comparable to the much more complex SVMs. The significantly lower training complexity opens up the possibility for the steganalyst to work with rich (high-dimensional) cover models and train on larger training sets – two key elements that appear necessary to reliably detect modern steganographic algorithms. Ensemble classification is portrayed here as a powerful developer tool that allows fast construction of steganography detectors with markedly improved detection accuracy across a wide range of embedding methods. The power of the proposed framework is demonstrated on two steganographic methods that hide messages in JPEG images.
    IEEE Transactions on Information Forensics and Security 04/2012; 7(2-2). DOI:10.1109/TIFS.2011.2175919 · 2.07 Impact Factor