J. Mielikainen, “LSB Matching Revisited,” IEEE Signal Processing Letters, Vol. 13 , No. 5, , pp. doi/LSP LSB Image steganography is highly efficient in storing a large amount of [1] J. Mielikainen, “LSB matching revisited,” IEEE Signal Process. Lett., vol. 13, no. LSB matching revisited. Authors: Mielikainen, J. Publication: IEEE Signal Processing Letters, vol. 13, issue 5, pp. Publication Date: 05/ Origin.

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LSB matching revisited

The LSB matching, a counterpart of LSB replacement, retains the favourable characteristics of LSB replacement, it is more difficult to detect from statistical perspective. Matchung, if the datasets are JPEG compressed with a quality factor of 80, the high frequency noise is removed and the histogram extrema method performs worse.

Citations Publications citing this paper. A feature selection methodology for steganalysis. The procedure of adjacency histogram method revisoted very similar to the procedure of calibration method. May 02, ; Accepted: Principal feature selection and fusion method for image steganalysis.

LSB matching revisited

J.mieilkainen.lsb paper has highly influenced 67 other papers. Steganalysis of LSB encoding matchung color images.

However, if the stego image contains too small amount of hidden data compared with the carrier image size and thus no secret message bit has been embedded into the 5×5 sub region, it is difficult for us to distinguish the cover and stego images using this detector as a discrimination rule. Results presented are obtained using k-fold crossvalidation method using a large set of never compressed grayscale images. For the estimators, study introduced the existing two estimating methods for LSB matching.


How to distinguish the image modified by normal image processing operation or steganography is a new challenge for steganalyzers.

Considering the asymmetry of the co-occurrence matrix, Abolghasemi et al. The second is that the HCF COM depends only on the histogram of the image and so is throwing away a great deal of recisited. The distortion due to non-adaptive LSB matching is modeled as an additive i. In particular, it is false for JPEG images which have been even slightly modified by image processing operations such as re-sizing, because that each colour has a j.miellkainen.lsb of its possible neighbours occurring in the cover image.

However, researches show that the improved performance of image steganalysis is achieved at the expense of increasing the number of the features. In practice, the performance of steganalysis methods is highly dependent on the types of cover images used. These sums are denoted Dc and Ds for the cover and stego images, respectively.

This is repeated after embedding a maximal-length random message 3 bits per cover pixel by LSB Rfvisited the average is now 5.

LSB matching revisited – Semantic Scholar

Steganalysis of LSB matching in grayscale images. Figure 8 demonstrates a significant improvement in performance over that of Ker b and GFH Goljan et al. j.mieikainen.lsb


Statistical correlations and machine learning for steganalysis. Steganalysis of LSB matching based on co-occurrence matrix and removing most significant bit planes. The LSB matching operation can be described as Table 1.

The results of detection are shown in Fig. Looking for new methods of image feature extraction. Experimental results demonstrate Fig. Westfeld calls these pairs neighbours. Steganalysis based on statistical characteristic of adjacent pixels for LSB steganography. Elementary calculation gives that F?

Yu and Babaguchi a calculate and analyze the run length histogram. June 19, ; Published: For a given image, we compute the features C h xR, C 2 h 2 x, y and R 2 twice using 3×3 and 5×5 neighborhood respectively, which form an 8-D feature vector for steganalysis. However, this approach is not effective for never-compressed images derived from a scanner. How to cite this article: The obvious alternative is not to do any dividing or rounding; in this case we are not downsampling and so we might as well consider pixels in pairs rather than groups of 4.

A detector is a discriminating statistic, a function of images which takes certain values in the case of stego images and other values in the case of innocent cover images.