LSB Matching and LSB Matching Revisited steganography methods are two general and esiest methods to achieve this aim. Being secured. Fulltext – A Review on Detection of LSB Matching Steganography. LSB matching steganalysis techniques detect the existence of secret messages embedded by LSB matching steganorgaphy in digital media. LSB matching revisited. Least significant bit matching revisited steganography (LSBMR) is a significant improvement of the well-known least significant bit matching algorithm.

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Conclusion And Future Enhancement The proposed scheme addresses two problems that were identified in the existing approach which were Lack of Security and Low Embedding rate. They find that llsb length histogram can be used to define a feature such as HCF. Steganalysis using image quality metrics.

Video Steganography Using LSB Matching Revisited Algorithm | IOSR Journals –

We reshape diagonal elements of co-occurrence matrix as following:. A feature selection methodology for steganalysis. The rest of the paper is arranged as follows, section 2 does Literature survey of the recent steganographic techniques. 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. 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.

Embedding text in video is more secure when compared to an image. 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. Travel the embedding units whose absolute differences are greater than or equal to the threshold T according to pseudorandom order based on the secret key key2, until all the hidden bits are extracted completely.


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. Attack LSB matching steganography by counting alteration rate of the number of neighbourhood gray levels.

Krutz, Hiding in Plain Sight: The cover video is then broken down into frames. There are two parameters in the proposed approach.

Computer Science > Cryptography and Security

An improved steganalysis method of LSB matching. Then the vector is divided into non-overlapping embedding units with every two consecutive pixels, these pixels can be used to generate the pseudorandom number which can be either an even or an odd number.

Theoretical analysis and practical experiments show that steganalysis of LSB matching is more difficult than that of LSB replacing Ker, a. In the future, we will consider these challenging problems as an open field for future investigation as follows.

Detecting hidden messages using higher-order statistical models.

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. Log In Sign Up.

Therefore, H S [k] will be no larger than H C k and for large k will be appreciably smaller. 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. Encoding technique revksited given in section 3.


The experimental results indicate, for the LSB Matching embedding it is shown that by removing 3 significant bit planes detection rates were increased. Moreover, new sophisticated steganographic methods will obviously require more refined detection methods. Nowadays, image blind steganalysis is still challenging in many aspects. In LSB replacement, the least significant bit of each selected pixel is replaced by a bit from the hidden message. Histogram characteristic function detectors: Tseng and Pan [12] presented a data hiding scheme in 2-color images, it embeds the information in any bit where at least one of the adjacent bits is the same as the original unchanged bit.

They divide the summed pixel intensities by four and take the integer part to reach images with the same range of values as the originals. We get an image A xy by combining the least two significant bit-planes as follows:. 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 And Performance Evaluation Any Steganography technique is characterized mainly by two attributes, imperceptibility and capacity.