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  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 steganalysis techniques detect the existence of secret messages embedded by LSB matching steganorgaphy in digital media. This study presents a survey of LSB matching steganalysis methods for digital images. Firstly, study described the structure of LSB matching steganalysis, which includes three parts: Secondly, study classified the existing detection algorithms into two categories according to the fact that the main contribution of the algorithm is detector or estimator.
For the detectors, j.imelikainen.lsb classified the existing various methods to two categories, described briefly their principles and introduced their detailed algorithms. For the estimators, study introduced the existing two estimating methods for LSB matching. Finally, study concluded and discussed some important problems in this field and indicated some interesting directions j.mielikaineh.lsb may be worth researching in the future. The goal of steganography is to hide the very presence of communication by embedding messages into innocuous-looking cover objects Fridrich et al.
The most popular, frequently used and easy to implement steganographic method is the Least Significant Bit LSB steganography. The LSB steganographic methods can be classified into the following two categories: In LSB replacement, the least significant bit of each selected pixel is replaced by a bit from the hidden message.
And the even pixel values are either unmodified or increased by one, rvisited odd ones are either decreased by one or left unchanged. Note, on average only half these bits will actually be changed; for the other half, the message bit is the same as the image bit already there. This imbalance in the embedding distortion was recently utilized to detect secret messages.
There is now substantial literature on LSB replacement such as Fridrich et al. As a counter-technology of steganography, steganalysis is a kind of art and science of revealing revisitec secret messages.
The steganalysis can disclose drawbacks of steganographic schemes by proving that a secret message has been embedded in a cover, on the other hand, it can prevent the utilization of outstanding steganographic methods by criminals to unlawfully transmit nocuous messages. The LSB matching, a counterpart of LSB replacement, retains the favourable characteristics of LSB replacement, it is more difficult to detect from statistical perspective.
Since LSB techniques are fairly easy to implement and have a potentially large payload capacity, there is a large selection of steganography software available for purchase and via shareware e.
This seemingly innocent modification of the LSB embedding is significantly harder to detect, because the pixel values are no longer paired. Theoretical analysis and practical experiments show that steganalysis of LSB matching is more difficult than that of LSB replacing Ker, a. Harmsen and Pearlman proposed a steganalysis method using the Histogram Characteristic Function HCF as a feature to distinguish the cover and stego images.
Significant improvements in detection of LSB matching in grayscale images were thereby achieved. This method has superior results when the images contain high-frequency noise, e. However, the method is inferior to the prior art only when applied to decompressed images with little or no high-frequency noise. However, they observe that this approach is not effective for never-compressed images derived from a scanner. There also exist blind techniques such as Holotyak et al. rsvisited
Farid first proposed a framework for learning-based steganalysis and demonstrated it as an effective approach to cope with the steganalysis difficulties caused by various image textures and unknown steganography algorithms.
Subsequently, some works have been developed which based on all kinds of features extracted from different domains such as spatial domain Avcibas et al.
However, researches show that the improved performance of image steganalysis is achieved at the expense of increasing the number of the features. In this study, we gave an overview of the detection methods for LSB matching steganography. To begin with, we described the structure of LSB matching steganalysis, which includes three parts, namely, LSB matching steganography, detectors for LSB matching and the evaluation methodology.
Then we classified the existing detection algorithms into two categories according to the fact that the main contribution of the algorithm is detector or estimator. For the detectors, we classified the existing various methods to two categories, described briefly their principles and introduced their detailed algorithms. For the estimators, we introduce the existing two estimating methods of LSB matching.
LSB matching revisited – Semantic Scholar
At last, some important problems in this field are concluded and discussed and some interesting directions that may be worth researching in the future are indicated. A true color 24 nxm bit image will be represented as three grayscale nxm images r ijg ijb ij.
The distortion due to non-adaptive LSB matching is modeled as an additive i. The LSB matching operation can be described as Table 1. Detectors for LSB matching: They can be roughly considered as sharing a common architecture, namely 1 feature extraction in some domain and 2 Fisher Linear Discriminant FLD j.mielikaainen.lsb to obtain a 2-class classifier Cancelli et al.
Those detectors and estimators are j.mielikainen.lsh reviewed in the next sections. It is important to have confidence in steganography j.meilikainen.lsb. 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.
LSB matching revisited
Because there are a number of steganalysis algorithms we wish to test, each with a number of possible variations, a number of hidden message lengths and tens of thousands of cover images, there are millions of calculations to perform.
To do so quickly, we use a small distributed network to undertake the computations; each node runs a highly-optimised program dedicated to the simulation of steganographic embedding and the computation of many different types of detection statistic; the calculations are queued and results recorded, in a database from which ROC curves can be extracted and graphed. In practice, the performance of steganalysis methods is highly dependent on the types of cover images used. One of the earliest detectors suggested for LSB Matching is due to Westfeld, which is based on close colour pairs Westfeld, 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.
Consider a pixel colour as a triple r, g, bspecifying the red, green and blue components. Westfeld calls these pairs neighbours. The LSB Matching algorithm will turn a large number occurrences of a single colour into a cluster of closely-related colours. Each colour can have up to 26 neighbours excluding itself.
A colour in a carrier medium has only 4 or 5 neighbours on average and that, in JPEG images, no colour has more than 9 neighbours. On the other hand, after embedding a message using Rveisited Matching even when the message is quite small enough new colours are created that the average number of neighbours is substantially increased and many colours even have the full complement of 26 revisiter. The number of neighbours of each colour in a JPEG image has been computed and the histogram displayed.
The average number of neighbours for each colour is 2. This is repeated after embedding a maximal-length random message 3 bits per cover pixel revisihed LSB Matching; the average is now 5. The detector remains perfect for JPEG images by using the histogram of the maximum neighbours statistic.
LSB matching revisited
But the story is quite different for cover images which are not JPEGs. 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 number of its possible neighbours occurring in the cover image. Histogram characteristic function detectors: It is clear that LSB Matching is one such type.
They consider that the steganographic embedding can be modeled as independent additive noise. The distribution of the added noise in the case of LSB Matching, j.mielikanien.lsb the hidden message is of maximal length, is just:. Elementary calculation gives that F? Therefore, H S [k] will be no larger than H C k and for large k will be appreciably smaller. The second is that the HCF COM depends only on the histogram of the image reivsited so is throwing away a great deal of structure.
The revisitex weakness of this method is that the detector does not see the cover image and so does not know C H Revisuted [k]. By calibrating the output COM using a down-sampled image and computing the adjacency histogram instead of matchkng usual histogram, Ker proposed his new method on uncompressed grayscale images.
Consider downsampling an image by a factor of two in both dimensions using a straightforward averaging filter. Precisely, let p c i, j be the pixel intensities of the downsampled cover image given by:. They divide the summed pixel intensities by four and take revosited integer part to reach revisied with the same range of values as the originals. The procedure of adjacency histogram method is very similar to the procedure of calibration method.
One difference is that the two-dimensional adjacency histogram is defined as fallows:. However, the detector degrades gracefully with shorter messages. Then, Ker b expand his recently-developed techniques for the detection of LSB Matching in grayscale images into the full-colour case. The obvious alternative is not to do any dividing or rounding; in this case we are not downsampling j.mielikkainen.lsb so we might as well consider pixels in pairs rather than groups of 4.
This detector is, in most cases, a large step up in sensitivity from the others j.meilikainen.lsb here. Yu and Babaguchi a calculate and analyze the run length histogram. They find that run length histogram can be used to define a feature such as HCF. Because of the shrinking effect of run length histogram after embedding, there is They calculate the alteration rate R by using.
Yu and Babaguchi a further extend the COM to high order as features for steganalysis. 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.
Experimental results demonstrate Fig. Under the same probability of false positive, the detection rate of our method is much higher than others. These sums are denoted Dc and Ds for the cover j.mielkkainen.lsb stego images, respectively.
Figure 8 demonstrates a significant improvement in performance over that of Ker b and GFH Goljan et al. J.mielikainsn.lsb experimental results demonstrate that the histogram extrema method has substantially better performance. However, 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.
A novel steganalysis method, which exploits the difference statistics of neighboring pixels, is proposed by Qin et al. In this method, the differences between the neighboring pixels DNPsthe differences between the local extrema DLENs and their neighbors in grayscale histogram are used as distinguishing features and the SVM is adopted to construct classifier.
The sums of DNPs with the value of zero and that with the value larger than one are denoted as F 1 and F 2respectively.
The sum of the absolute differences between the local maximums and their neighbours in a cover image histogram is denoted as S max. The sum of the absolute differences between and their neighbours is given by:. Similarly, we denote the sum of absolute differences between the local minimums and their neighbours in a matcbing image histogram as S min and denote the absolute differences between and their neighbours as.
The change rate of the feature F i before and after LSB matching steganography is denoted as:. For a j.mielikainen.lwb image, we compute the features F 1F 2S maxS min and their change rate to form an 8-D feature vector for steganalysis. Experimental results show Fig.