A Covert Communication Model-Based on Image Steganography

A Covert Communication Model-Based on Image Steganography

Mamta Juneja (UIET, Panjab University, Chandigarh, India)
Copyright: © 2014 |Pages: 19
DOI: 10.4018/ijisp.2014010102
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

With the more and more advancement in technology, internet has become the most important medium for all kinds of confidential as well as non-confidential communications. Security is the major issue for such communications and steganography is most widely accepted tool for information security. An effort has been made in the present paper to propose a secured model for communication using image steganography. It presents two components based LSB steganography method, adaptive LSB based steganography method for embedding data in high and low transition parts of an image respectively. Hybrid edge detection filter is proposed to divide an image in low and high transition areas. AES (Advanced Encryption Standard) and Randomization is incorporated to provide two-tier security. Comparison analysis of output results with other existing techniques on basis of capacity, imperceptibility is giving the proposed approach an edge over others. The proposed approach has been thoroughly tested for various steganalysis attacks like visual analysis, histogram analysis, chi-square, and RS analysis and could sustain all these attacks very well.
Article Preview

1. Introduction

Markus Kahn (1995) defines Steganography as an art and science of communicating in a way which hides the existence of the communication. In contrast to cryptography, where the enemy is allowed to detect, intercept and modify messages without being able to violate certain security premises guaranteed by a cryptosystem, the goal of steganography is to hide messages inside other innocent messages in a way that does not allow any enemy to even detect that there is a second message present.

Initial work on LSB steganography was on LSB Substitution and was explored by Chang et al. (2002), THIEN et al. (2003), Wang et al. (2000, 2001), Chang et al. (2003, 2006), Chan et al. (2001, 2004), which substitutes the same number of bits of each and every pixel of input host image for hiding the secret text or message so give rise to pair of values (PoVs) and are easily attacked by Chi-square Test given by West fled et al. (1999), Provos et al. (2002), Stanley (2005). LSB Matching introduced by Ker et al. (2004) and researched by Mielikainen (2006), LI (2009), Luo et al. (2010), and Kumar et al. (2012) was attacked by Ker (2005) based on the Center of Mass (COM) of the Histogram Characteristic Function (HCF).Adaptive LSB was worked on by Lie et al. (2000), Liu et al. (2004), and Kekre et al. (2008) is based on variable number bits substitution but are unable to utilize HVS masking characteristics completely and are affected by edge masking effect. PVD methods are among the most popular method which was explored by Wu and Tsai (2003), Park et al. (2005), Wu et al (2005), Yang et al (2006), Jung et al. (2008), Liu et al. (2008), Wang et al. (2008), Yang et al. (2008), Maleki et al (2011), Liao et al. (2011), and Mandal et al. (2011). It follows the principle that the edge areas being high in contrast, intensity, transitions can tolerate more changes than smooth areas .But techniques based on these are unable to mark the difference in edge features and texture features so embed data in both. Moreover were complex to work and were easily attacked by Zhang et al. (2004). Edge detection Filter based technique was utilized by Alwan et al. (2005), Negi et al. (2006), Hempstalk (2006), Singh et al. (2007), Chen et al. (2010), Hussain (2011), and Bassil et al. (2011) for steganography in Gray images. But the advancement in image technology to RGB leads to steganography application for color images. Pixel indicator techniques introduced by Gutub et al. (2008), 2009), and Gandharba et al. (2011) for color images had a major drawback of treating all color components (red, green, blue) equally contradicting Hecht principle, which reveals that the visual perception of intensely red objects is highest and then of intensely Green objects and is least for intensely blue objects i.e. red plays the most significant and Blue plays a least significant role in color formulation. So, we can integrate maximum changes in Blue component and average changes in green component and least change in red component without making much difference in color image. Color Component based Techniques researched by Imran et al. (2007), Chang et al. (2008), Roque et al. (2009), and Mandal et al. (2012) were not fully tested fully for all types of attacks like targeted and universal and were focused on single component. They didn’t utilize cryptography and Random Sequence generator techniques to make these techniques better resistant to attacks. Chen et al. (2010) proposed steganography technique using hybrid filter but tested it for gray images moreover didn’t test it for targeted and universal attacks. Capacity of 2.8 bpp (bits per pixel)is good but highest PSNR value attained is 28.6 which is very low. Mandal (2011) achieved 49% PSNR but didn’t even mention capacity factor and Hussain et al. (2011) achieved highest PSNR for very small text messages. Liao et al. (2011) achieved 39% PSNR with good capacity but also explicitly mentioned this in their research paper that they targeted quality and capacity from tradeoff between capacity, quality and robustness.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 12: 4 Issues (2018): 1 Released, 3 Forthcoming
Volume 11: 4 Issues (2017)
Volume 10: 4 Issues (2016)
Volume 9: 4 Issues (2015)
Volume 8: 4 Issues (2014)
Volume 7: 4 Issues (2013)
Volume 6: 4 Issues (2012)
Volume 5: 4 Issues (2011)
Volume 4: 4 Issues (2010)
Volume 3: 4 Issues (2009)
Volume 2: 4 Issues (2008)
Volume 1: 4 Issues (2007)
View Complete Journal Contents Listing