Least Significant Bit Matching Steganography Research Articles

Visual Secret Sharing Using Cryptography

The Visual Cryptography Scheme (VCS) is a kind of secret sharing scheme that focuses on sharing secret im- ages. The basic idea of the visual cryptography scheme is to split a secret image into number of random shares (printed on transparencies) which separately reveals no information about the secret image other than the size of the secret image. The secret image can be reconstructed by stacking the shares. The underlying operation of this scheme is logical OR operation. The Embedded Extended Visual Cryptography Scheme (EEVCS) uses meaningful covering shares. The scheme EEVCS can be implemented by embedding random shares of secret image into meaningful cover images.The halftoning method is used to convert the gray scale image into binary im- age. After Studying existing Dithering technique of half ton- ing, Otsus Thresholding method is proposed. Least Significant Bit (LSB) matching steganography technique is used to hide the secret pixel information into the covering images.Related works on visual cryptography scheme are also investigated and it was observed that visual quality of recovered secret images are low by using existing dithering halftone based EEVCS techniques.Including Otsus Thresholding method for halfton- ing of an image can produce bright image in a better way. LSB matching steganography is a useful method to hide the secret image. This research aims at employing LSB matching stega- nography with Otsu's method to develop an efficient EEVCS system. Index Terms - Embedded extended visual cryptogra- phy scheme, Otsu's Method, Dithering technique, Least Sig- nificant bit, Visual cryptography Scheme. IntroductionTHE basic principle of the visual cryptography scheme (VCS) was first introduced by Naor and Shamir. VCS is a kind of secret sharing scheme (1), (2) that focuses on sharing secret images. The idea of the visual cryptography model proposed in (3) is to split a secret image into two random shares (printed on transpa- rencies) which separately reveals no information about the secret image other than the size of the secret image. The secret image can be reconstructed by stacking the two shares. The underlying operation of this scheme is logical operation OR. In this paper, we call a VCS with random shares the traditional VCS or simply the VCS. In general, a traditional VCS takes a secret image as input, and outputs shares that satisfy two conditions: 1) any qual- ified subset of shares can recover the secret image; 2) any for- bidden subset of shares cannot obtain any information of the secret image other than the size of the secret image. An example of traditional (2,2)-VCS is shown in Fig. 1.In the scheme of Fig. 1, shares (a) and (b) are distributed to two participants secretly, and each participant cannot get any information about the secret image, but after stacking shares (a) and (b), the secret image can be observed visually by the par- ticipants. VCS has many special applications, for example,

LSB matching steganalysis based on second-order differential-based Markov feature

In this paper,the author put forward a Least Significant Bit(LSB) matching steganalytic algorithm.Through calculating the second-order differential of the image pixels on horizontal and vertical directions,the differential matrix was obtained and it was used as sensitive feature extracting source and the second-order Markov transformation matrix of the differential matrix was extracted as the features,according to the LSB matching steganography with high security.According to the experimental result shows that the algorithm proposed in the paper speedups the detection process in a large extent and enhances the performance and practicability of the steganalytic algorithm while maintaining high detection accuracy,compared with the algorithms based on first-order differential Markov transition probability matrix.

Steganalysis of LSB matching based on statistical modeling of pixel difference distributions

This paper presents a least significant bit (LSB) matching steganography detection method based on statistical modeling of pixel difference distributions. Previous research indicates that natural images are highly correlated in a local neighborhood and that the value zero appears most frequently in intensity differences between adjacent pixels. The statistical model of the distribution of pixel difference can be established using the Laplace distribution. LSB matching steganography randomly increases or decreases the pixel value by 1 when the message is embedded; thus, the frequency of occurrence of the value zero in pixel differences changes most dramatically during message embedding. Based on the Laplacian model of pixel difference distributions, this paper proposes a method to estimate the number of the zero difference value using the number of non-zero difference values from stego-images and uses the relative estimation error between the estimated and actual values of the number of the zero difference value as the classification feature. Experimental results indicate that the proposed algorithm is effective in detecting LSB matching steganography and can achieve better detection performance than the local extreme method under most circumstances.

Image complexity and feature mining for steganalysis of least significant bit matching steganography

The information-hiding ratio is a well-known metric for evaluating steganalysis performance. In this paper, we introduce a new metric of image complexity to enhance the evaluation of steganalysis performance. In addition, we also present a scheme of steganalysis of least significant bit (LSB) matching steganography, based on feature mining and pattern recognition techniques. Compared to other well-known methods of steganalysis of LSB matching steganography, our method performs the best. Results also indicate that the significance of features and the detection performance depend not only on the information-hiding ratio, but also on the image complexity.