Data Hiding Method Based on Inter-Block Difference in Eight Queens Solutions and LSB Substitution

Data Hiding Method Based on Inter-Block Difference in Eight Queens Solutions and LSB Substitution

Vinay Kumar (Vivekananda Institute of Professional Studies, Guru Gobind Singh Indraprastha University, Delhi, India), Abhishek Bansal (Department of Computer Science, Indira Gandhi National Tribal University, Amarkantak, India) and Sunil Kumar Muttoo (Department of Computer Science, University of Delhi, Delhi, India)
Copyright: © 2014 |Pages: 14
DOI: 10.4018/IJISP.2014040104


Data hiding is an emerging field of research for secure data transmission over internet, ensuring ownership identification and copyright protection. A couple of techniques have been proposed based on pixel value differencing (PVD) and eight queens' solutions. In this paper, a new data hiding method based on inter-block difference in eight queen's solutions is presented. The result of inter-block difference is XORED with ASCII code of character from the message and the resultant value is embedded in LSB position. The presented approach is more efficient and it provides a more capacity with good imperceptibility. The approach supports different digital image file formats such as bmp, png and tiff.
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1. Introduction

Digital data hiding technique, also called digital steganography, is a new kind of secret communication approach. While cryptography scrambles the message so that it cannot be understood, steganography hides the data so that it cannot be observed. The major goal of steganography is to enhance communication security by inserting a secret message into the digital image, modifying the nonessential pixels of the image (Petitcolas, Anderson & Kuhn, 1999; Feng, Lin, Tsai & Chu, 2006). Generally, a steganographic system consists of three approaches: keys generation, message embedding algorithm and message extraction algorithm. The key generation method takes security parameter as input and generates a bit string called a stego key as output. The message embedding algorithm takes security parameter, stego key, cover image and message as input and produces stego image as output. And, the extraction algorithm takes the stego image and stego keys as input and retrieves the secret message.

Some techniques of data hiding have been proposed by (Chung, Shen & Chang, 2001; Wang, Lin & Lin, 2000; Wang, Lin & Lin, 2001; Liao, Wen & Zhang, 2011). Most common method is based on modification of least-significant bit (LSB) by directly replacing the LSBs of the cover-image with the corresponding message bits, wherever required. LSB method typically achieves high capacity however it fails to maintain imperceptibility. Wang et al (2000) proposed an algorithm to embed secret messages in moderately significant bit of the cover-image and further proposed a data hiding scheme (Wang, Lin & Lin, 2001) by the optimal LSB (OLSB) substitution which considers an optimal k rightmost LSB substitution method. Chang & Cheng (2004) proposed data hiding scheme by simple LSB substitution with an optimal pixel adjustment process (OPAP). Mielikainen (2006) proposed a data hiding method that uses a binary function between two cover pixels. The value of the function is used to hide information. We presented a data hiding algorithm (Muttoo, Kumar & Bansal, 2012) which is based on eight queen solutions. The 8-queens problem of placing 8 non-attacking queens on an 8x8 chessboard is used to hide message in an image. The method helps in randomizing bit selection in a cover image while hiding. Chang Tsai & Lin (2005) developed an adaptive technique applied to the LSB method. In this method, correlation between neighbouring pixels is used to estimate the degree of smoothness. Li et al (2009) proposed lossless data hiding approach using difference in values of adjacent pixels. Wu & Tsai (2003) proposed another method based on pixel value differencing and difference in values is classified into range intervals. The selection of range intervals is based on human vision’s sensitivity to gray values varying from smoothness to contrast. Another method using graph theoretic approach is discussed in (Vinay & Muttoo, 2009a).

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