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.

0 Bookmarks
 · 
242 Views
  • 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. · 3.64 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Yet Another Steganography Scheme (YASS), a promising steganographic scheme for JPEG images which can resist blind steganlysis via embedding data in randomized locations. However, the randomization is not sufficient enough, as some positions are possible to hold host blocks and some are definitely not. Meanwhile, the artifacts introduced by quantization index modulation (QIM) embedding led to the weakness for its unsafety. In this paper, to overcome these drawbacks, we present a modified YASS scheme incorporated with further randomized virtual host block selection and the model based embedding which permutes secret bits by a low-density cover probability to minimize differences between the distribution of a cover image and that of the stego. Consequently, the proposed scheme can survive the attack made by the specific steganalyzer for YASS. Experimental results demonstrate that the detection probability achieved by the specific steganalyser on our proposed method is less than 59%, while is about 95% and above on YASS.
    Industrial Electronics and Applications (ICIEA), 2012 7th IEEE Conference on; 01/2012
  • [Show abstract] [Hide abstract]
    ABSTRACT: The quantization artifacts and blocking artifacts are the two significant properties in the JPEG compressed images. Most relative forensic techniques usually use such inherent properties to provide some evidences on how image data is acquired and/or processed. A wise attacker, however, may perform some post-operations to confuse the two artifacts to fool current forensic techniques. Recently, Stamm et al. in [1] propose a novel anti-JPEG compression method via adding anti-forensic dither to the DCT coefficients and further reducing the blocking artifacts. In this paper, we found that the dithering operation will inevitably destroy the statistical correlations among the 8 × 8 intrablock and interblock within an image. In the view of JPEG steganalysis, we employ the transition probability matrix of the DCT coefficients to measure such modifications for identifying the forged images from those original JPEG decompressed images and uncompressed ones. On average, we can obtain a detection accuracy as high as 99% on the image database of UCID [2].
    Image Processing (ICIP), 2012 19th IEEE International Conference on; 01/2012