Run length based steganalysis for LSB matching steganography

Conference Paper · June 2008with4 Reads
DOI: 10.1109/ICME.2008.4607444 · Source: IEEE Xplore
Conference: Multimedia and Expo, 2008 IEEE International Conference on
Abstract

In this paper, we propose a steganalysis algorithm to detect spatial domain least significant bit (LSB) matching steganography, which is much harder than the detection of LSB replacement. We use the fusion of histogram of run length and histogram characteristic function to detect the LSB matching. Experimental results on two datasets demonstrate that this method has superior results compared with other recently proposed algorithms, and shows that the proposed method is efficient to detect the LSB matching steganography on compressed or uncompressed images.

    • "This paper utilizes the run-length histogram (RLH) [16] to extract the feature which catches dependence among pixels that have the distance larger than one. Scanning the image in a mode, a run is a set of consecutive image pixels with equal intensity, and the run length is defined as the amount of pixels in the run [17]. "
    [Show abstract] [Hide abstract] ABSTRACT: This paper considers the detection of spatial domain least significant bit (LSB) matching steganography in gray images. Natural images hold some inherent properties, such as histogram, dependence between neighboring pixels, and dependence among pixels that are not adjacent to each other. These properties are likely to be disturbed by LSB matching. Firstly, histogram will become smoother after LSB matching. Secondly, the two kinds of dependence will be weakened by the message embedding. Accordingly, three features, which are respec-tively based on image histogram, neighborhood degree histogram and run-length histogram, are extracted at first. Then, support vector machine is utilized to learn and dis-criminate the difference of features between cover and stego images. Experimental results prove that the proposed method possesses reliable detection ability and outperforms the two previous state-of-the-art methods. Further more, the conclusions are drawn by analyzing the individual performance of three features and their fused feature.
    Full-text · Article · Apr 2011 · Radioengineering
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  • [Show abstract] [Hide abstract] ABSTRACT: In this paper, we propose a new method to detect least significant bit (LSB) matching steganography which is based on neighbourhood node degree histogram characteristic function (NDHCF). First we calculate the center of mass (COM) of the NDHCF then embed another random secret message to compute the alteration rate of the NDHCF COM. We select NDHCF COM and the alteration rate as features and use support vector machines as a classifier. Experimental results demonstrate that the proposed method is efficient to detect the LSB matching stegonagraphy on compressed or uncompressed images and has superior results compared with other recently proposed algorithms.
    No preview · Article · Jan 2009
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  • [Show abstract] [Hide abstract] ABSTRACT: In this paper, we propose a steganalysis algorithm to detect spatial domain least significant bit (LSB) matching steganography, which is much harder than the detection of LSB replacement. We use features based on histogram of run length and histogram characteristic function to detect the LSB Matching. Experimental results on two datasets demonstrate that this method has superior results compared with other recently proposed algorithms, and shows that the proposed method is efficient to detect the LSB matching steganography on compressed or uncompressed images.
    No preview · Conference Paper · Oct 2010
    0Comments 1Citation
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