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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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. Principal feature selection and fusion method for image steganalysis. One difference is that the two-dimensional adjacency histogram is defined as fallows:. However, they observe that this approach is not effective for never-compressed images derived from a scanner. Exploiting similarities between secret and cover images for improved embedding efficiency and security in digital steganography Alan Anwer Abdulla Citations Publications citing this paper.

The results of detection are shown in Fig. At the same time, Holotyak et al. It is founded on the assumption that cover images contain a relatively small number of different colours, in a very similar way to an early detector for LSB Replacement due to Fridrich et al.

A review on blind detection for image steganography. It is clear that LSB Matching is one such type. June 19, ; Published: Experimental results show Fig.


The sum of the matcging differences between the local maximums and their neighbours in a cover image histogram is denoted as S max. In the LSB matching, the choice of whether to add or subtract one from the matxhing image pixel is random. From This Paper Figures, tables, and topics from this paper.

Through embedding a random sequence by LSB matching and computing the alteration rate of the number of elements in T1, they find that normally the alteration rate is higher in cover image than the value in the corresponding stego image, which is used as the discrimination rule in their detector.

Steganalysis based on difference statistics for LSB matching steganography.

LSB matching revisited

Steganalysis of two least significant bits embedding based on least square method. A diagram for the matchhing SVM is shown in Fig.

Steganalysis using image quality metrics. Meanwhile, the steganalysis of LSB matching steganography in grayscale images is still very challenging in the case of complicated textures or low hiding ratios. Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions.

Statistical correlations and machine learning for steganalysis. Search in Google Scholar. A novel steganalysis of lsb matching based on kernel fda in grayscale images.

A Review on Detection of LSB Matching Steganography

The second is that the HCF COM depends only on the histogram of the image and so is throwing away a great deal of structure. In LSB replacement, the least significant bit of matchint selected pixel is replaced by a bit from the hidden message. Obviously, the detection accuracies of the existing methods are not enough, especially for the case of low embedding ratio.


Optimized feature extraction for learning-based image steganalysis. Then, Ker b expand his recently-developed techniques for the detection of LSB Matching in grayscale images into the full-colour case. Those detectors and estimators are briefly reviewed in the next sections.

Steganalysis of additive noise modelable information hiding.

The distribution of the added noise in the case of LSB Matching, when the hidden message is of maximal length, is just:. The significant weakness of this method is that the detector does not see the cover image and so does not know C H C [k].

Values of C H[k] circles before and crosses after embedding from four different sources. The procedure of adjacency histogram method is very similar to the procedure of calibration method. Topics Discussed in This Paper.