1 - Acta Technica Corviniensis

increase in one of the factors leads to the decrease inother [9].Though, there are many data hiding techniques [2, 3,4], in this paper, we consider the spatial domain ofdata hiding. We discuss the various data hidingtechniques using bit manipulation of the lowestsignificant bit (LSB). We take a look at how the bitplanes can be increased by various numberdecomposition methods without compromising onthe three requirements of visibility, robustness andcapacity.The rest of the paper is organized as follows. InSection 2 we take a look at the classical LSB datahiding technique. Here we discuss how secret text canbe embedded in cover image using LSB manipulation.Section 3 of the paper contains the Fibonacci LSB datahiding technique. Section 4 of the paper contains theLSB data hiding technique using decomposition ofprime numbers. The approach for decompositionusing prime numbers is discussed here. Section 5contains LSB data hiding by natural numbers. Here thedecomposition of bit planes by natural numbers isdiscussed. In Section 6 we present a comparativeanalysis between the various LSB data hidingmethods. Finally, in Section 7, we present theconclusion for the paper.CLASSICAL LSB DATA HIDING TECHNIQUEOne of the simplest implementation of data hidingtechnique is the classical least significant bit datahiding technique [18]. The technique is based onmanipulation of the least significant bits of the carrierimage to accommodate the hidden message.The insertion of LSB varies according to the number ofbits in an image. For an 8 bit image, the value of 8 thbit, which is the least significant bit of each pixel,would be modified and the secret message would beembedded [19].Suppose if we wanted to insert the letter ‘A’ into animage. The binary equivalent of ‘A’ is 10000001. Now ifour sample image of 8 bit has the following pixelvalues:10101010 10001010 11111111 01011010110101010 10101010 10001011 10101111So, embedding the binary equivalent of ‘A’ i.e.10000001would change the pixel values of our sampleimage to:101010101 10001010 111111110 01011010010101010 10101010 10001010 0101111In our sample image the least significant bit value ofpixel 1, 3, 4 and 7 have been changed.Similarly, for an image of 24 bit, we need to changethe RGB (Red Blue Green) color values. Suppose oursample image has the following pixel values,[10010101 10101010 11110101][10001001 10001110 10000010]128ACTA TECHNICA CORVINIENSIS – Bulletin of Engineering[11110101 01001010 10001011]So to accommodate our desired character, ‘A’, wehave to make the following modifications:[10010101 10101010 11110100][10001000 10001110 10000010][11110100 01001010 10001011]So, basically we have changed the values of 3 bit toaccommodate our desired character.Though, classical data hiding is simple to implement, itis vulnerable to image manipulation [15].LSB DATA HIDING USING FIBONACCI NUMBERSBattisti et al. [1] proposed a method of embeddingdata into digital media by decomposition of Fibonaccinumber sequence which allowed different bit planedecomposition when compared to the classical LSBscheme.The Fibonacci sequence, named after Leonardo ofPisa, also known as Fibonacci, is a sequence ofnumbers in the following integer sequence:0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89 …The Fibonacci numbers are defined by a recurrencerelation:F p (0) = F p (1) = 1F p (n) = F p (n – 1) + F p (n – p – 1), ∀ n ≥ 2, n∈NThe bit planes are now decomposed based on theFibonacci sequence. The main drawback of thisapproach is that of redundancy and to counter thatand obtain a unique representation, Zeckendorftheorem [20] is used.To embed the intended message in the cover image, itis decomposed into bit planes by using Fibonacci p‐decomposition. The Zeckendorf condition is checkedfor each bit to be modified. If the condition is fulfilled,the bit is inserted otherwise the bit following it isconsidered.LSB DATA HIDING USING PRIME NUMBERSLSB data hiding using prime numbers is a data hidingtechnique proposed by Dey et.al [10] as animprovement over the Fibonacci numbers data hidingtechnique proposed by Battisti et. al. The main idea ofthe work was to use the prime number decompositionand generate new set of bit planes and embedinformation in these newly generated bit planes withminimal distortion.In this approach, the researchers took an image of mbits and increased the number of bit planes to n,where the value of n was equal or greater than orequal to the number of bit planes of the image. Thiswas achieved by converting the bit planes of theimage to another number system using primenumbers as the weighted function. This resulted in theincrease of number of bits and consequently it couldbe used for hiding data in higher bit planes withminimal distortion.2012. Fascicule 3 [July–September]