An LSB Data Hiding Technique Using Natural Numbers

In this paper, a novel data hiding technique is proposed, as an improvement over the Fibonacci LSB data-hiding technique proposed by Battisti et al,based on decomposition of a number (pixel-value) in sum of natural numbers. This particular representation again generates a different set of (virtual) bit-planes altogether, suitable for embedding purposes. We get more bit-planes than that we get using Prime technique.These bit-planes not only allow one to embed secret message in higher bit-planes but also do it without much distortion, with a much better stego-image quality, and in a reliable and secured manner, guaranteeing efficient retrieval of secret message. A comparative performance study between the classical Least Significant Bit(LSB) method, the Fibonacci LSB data-hiding technique and the proposed schemes indicate that image quality of the stego-image hidden by the technique using the natural decomposition method improves drastically against that using Prime and Fibonacci decomposition technique. Experimental results also illustrate that, the stego-image is visually indistinguishable from the original cover-image. Also we show the optimality of our technique.Comment: 6 Pages, 5 Figures, IEEE Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007, Nov 26-28, 2007, Kaohsiung City, Taiwan, IEEE Computer Society press, USA, ISBN 0-7695-2994-1, pp. 473-476, 2007

Data Hiding Techniques using number decompositions

Data hiding is the art of embedding data into digital media in a way such that the existence of data remains concealed from everyone except the intended recipient. In this paper, we discuss the various Least Significant Bit (LSB) data hiding techniques. We first look at the classical LSB data hiding technique and the method to embed secret data into cover media by bit manipulation. We also take a look at the data hiding technique by bit plane decomposition based on Fibonacci numbers. This method generates more bit planes which allows users to embed more data into the cover image without causing significant distortion. We also discuss the data hiding technique based on bit plane decomposition by prime numbers and natural numbers. These methods are based on mapping the sequence of image bit size to the decomposed bit number to hide the intended information. Finally we present a comparative analysis of these data hiding techniques.Comment: 5 pages, 1 figure, 4 table

A Note On the Bounds for the Generalized Fibonacci-p-Sequence and its Application in Data-Hiding

In this paper, we suggest a lower and an upper bound for the Generalized Fibonacci-p-Sequence, for different values of p. The Fibonacci-p-Sequence is a generalization of the Classical Fibonacci Sequence. We first show that the ratio of two consecutive terms in generalized Fibonacci sequence converges to a p-degree polynomial and then use this result to prove the bounds for generalized Fibonacci-p sequence, thereby generalizing the exponential bounds for classical Fibonacci Sequence. Then we show how these results can be used to prove efficiency for data hiding techniques using generalized Fibonacci sequence. These steganographic techniques use generalized Fibonacci-p-Sequence for increasing number available of bit-planes to hide data, so that more and more data can be hidden into the higher bit-planes of any pixel without causing much distortion of the cover image. This bound can be used as a theoretical proof for efficiency of those techniques, for instance it explains why more and more data can be hidden into the higher bit-planes of a pixel, without causing considerable decrease in PSNR.Comment: 15 Pages, 2 Figures, 2 Table

Hiding Inside HTML and Other Source Codes

Many steganographic techniques were proposed for hiding secret message inside images, the simplest of them being the LSB data hiding. In this paper, we suggest a novel data hiding technique in an HTML Web page and also propose some simple techniques to extend the embedding technique to source codes written in any programming language (both case insensitive like HTML, Pascal and case sensitive languages like C, C++, Java). We basically try to exploit the case-redundancy in case-insensitive language, while we try hiding data with minimal changes in the source code (almost not raising suspicion). HTML Tags are case insensitive and hence an alphabet in lowercase and one in uppercase present inside an HTML tag are interpreted in the same manner by the browser, i.e., change in case in a web page is imperceptible to the browser. We first exploit this redundancy and use it to embed secret data inside an web page, with no changes visible to the user of the web page, so that he can not even suspect about the data hiding. The embedded data can be recovered by viewing the source of the HTML page. This technique can easily be extended to embed secret message inside any piece of source-code where the standard interpreter of that language is case-insensitive. For case-sensitive programming languages we do minimal changes in the source code (e.g., add an extra character in the token identified by the lexical analyzer) without violating the lexical and syntactic notation for that language) and try to make the change almost imperceptible.Comment: 10 Pages, 7 Figures, 2 Algorithm

Embedding Secret Data in HTML Web Page

In this paper, we suggest a novel data hiding technique in an HTML Web page. HTML Tags are case insensitive and hence an alphabet in lowercase and one in uppercase present inside an HTML tag are interpreted in the same manner by the browser,i.e., change in case in an web page is imperceptible to the browser. We basically exploit this redundancy and use it to embed secret data inside an web page, with no changes visible to the user of the web page, so that he can not even suspect about the data hiding. The embedded data can be recovered by viewing the source of the HTML page. This technique can easily be extended to embed secret message inside any piece of source-code where the standard interpreter of that language is case-insensitive.Comment: 10 Pages, 6 Figures, 2 Algorithms,1st International Conference on Image Processing and Communications, September 16-18, 2009, Bydgoszcz, Poland

Genetic Algorithm in Audio Steganography

With the advancement of communication technology,data is exchanged digitally over the network. At the other side the technology is also proven as a tool for unauthorized access to attackers. Thus the security of data to be transmitted digitally should get prime focus. Data hiding is the common approach to secure data. In steganography technique, the existence of data is concealed. GA is an emerging component of AI to provide suboptimal solutions. In this paper the use of GA in Steganography is explored to find future scope of research.Comment: 6 pages,2 figures Published with International Journal of Engineering Trends and Technology (IJETT). arXiv admin note: text overlap with arXiv:1003.4084, arXiv:1205.2800 by other authors without attributio

Text Steganography using LSB insertion method along with Chaos Theory

The art of information hiding has been around nearly as long as the need for covert communication. Steganography, the concealing of information, arose early on as an extremely useful method for covert information transmission. Steganography is the art of hiding secret message within a larger image or message such that the hidden message or an image is undetectable; this is in contrast to cryptography, where the existence of the message itself is not disguised, but the content is obscure. The goal of a steganographic method is to minimize the visually apparent and statistical differences between the cover data and a steganogram while maximizing the size of the payload. Current digital image steganography presents the challenge of hiding message in a digital image in a way that is robust to image manipulation and attack. This paper explains about how a secret message can be hidden into an image using least significant bit insertion method along with chaos

On the usefulness of information hiding techniques for wireless sensor networks security

A wireless sensor network (WSN) typically consists of base stations and a large number of wireless sensors. The sensory data gathered from the whole network at a certain time snapshot can be visualized as an image. As a result, information hiding techniques can be applied to this "sensory data image". Steganography refers to the technology of hiding data into digital media without drawing any suspicion, while steganalysis is the art of detecting the presence of steganography. This article provides a brief review of steganography and steganalysis applications for wireless sensor networks (WSNs). Then we show that the steganographic techniques are both related to sensed data authentication in wireless sensor networks, and when considering the attacker point of view, which has not yet been investigated in the literature. Our simulation results show that the sink level is unable to detect an attack carried out by the nsF5 algorithm on sensed data

Data Hiding and Retrieval Using Permutation Index Method

In this paper a novel approach for matrix manipulation and indexing is proposed .Here the elements in a row of matrix are designated by numeric value called permutation index followed by the elements of the row being randomised. This is done for all the rows of the matrix and in the end the set of permutation indices are put in the parent matrix and random locations depending on a pre decided scheme called passkey. This passkey is used to put back the elements of all the rows back in the correct sequence. This approach finds application in data encapsulation and hiding.Comment: 6 pages, 4 figures, 5 table

Public key Steganography Using Discrete Cross-Coupled Chaotic Maps

By cross-coupling two logistic maps a novel method is proposed for the public key steganography in JPEG image. Chaotic maps entail high complexity in the used algorithm for embedding secret data in a medium. In this paper, discrete cross- coupled chaotic maps are used to specifying the location of the different parts of the secret data in the image. Modifying JPEG format during compressing and decompressing, and also using public key enhanced difficulty of the algorithm. Simulation results show that in addition to excessive capacity, this method has high robustness and resistance against hackers and can be applicable in secret communication. Also the PSNR value is high compared to the other works.Comment: 6 pages, 5 figure