Research Article A New Information Hiding Method Based on Improved BPCS Steganography Shuliang Sun 1,2 1 Department of Electronic and Information Engineering, Fuqing Branch of Fujian Normal University, Fuqing 350300, China 2 Innovative Information Industry Research Center, Fuqing Branch of Fujian Normal University, Fuqing 350300, China Correspondence should be addressed to Shuliang Sun; tjussl [email protected] Received 31 December 2014; Revised 15 March 2015; Accepted 16 March 2015 Academic Editor: Deepu Rajan Copyright © 2015 Shuliang Sun. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Bit-plane complexity segmentation (BPCS) steganography is advantageous in its capacity and imperceptibility. e important step of BPCS steganography is how to locate noisy regions in a cover image exactly. e regular method, black-and-white border complexity, is a simple and easy way, but it is not always useful, especially for periodical patterns. Run-length irregularity and border noisiness are introduced in this paper to work out this problem. Canonical Cray coding (CGC) is also used to replace pure binary coding (PBC), because CGC makes use of characteristic of human vision system. Conjugation operation is applied to convert simple blocks into complex ones. In order to contradict BPCS steganalysis, improved BPCS steganography algorithm adopted different bit-planes with different complexity. e higher the bit-plane is, the smaller the complexity is. It is proven that the improved BPCS steganography is superior to BPCS steganography by experiment. 1. Introduction Steganography is an art and science of invisible communica- tion. It comes from Greek for covered writing and essentially means “to hide in plain sight” [1]. Embedding capacity, security (imperceptibility), and robustness are the most important three features in steganographic system. Gener- ally speaking, there is a fundamental compromise between embedding capacity and security in information hiding scheme. Steganography methods can be divided into two categories: original (spatial) domain methods and transform domain methods. Cover image is transformed into frequency domain first, and then the secret messages are embedded in the coefficients of transformed cover image. Singh et al. [2] proposed a method to combine two techniques. One was image enhancement and the other was image steganography based on DFT. Kaur and Kochhar [3] provided a technique for image steganography based on DCT. e algorithm had good invisibility and security. A three-level DWT decomposition was done on host image and secret information was hidden in approximation coefficients of the decomposed image in [4]. e stegoimage is subjected to image processing attacks. Abu et al. [5] discussed a robust algorithm, which was based on integer Haar wavelet transform and pixel value difference. Ramezani et al. [6] gave a steganography method in contourlet domain. e optimal pixel adjustment process and genetic algorithm are also used. Alsaif and Saalih [7] presented a data hiding technique in NSCT domain. Secret data was embedded in high frequency directional band pass of contourlet transform. In spatial domain methods, the processing is applied on the image pixel values directly. Least significant bits (LSB) substitution [8] is the most well- known spatial steganographic system. Although this method is simple and easy, it is weak in robustness and compression, such as JPEG compression. Soumi et al. [9] presented a mosaic image steganography based on genetic algorithms for enhanced security. Mandal and Das [10] proposed a color image steganography method based on pixel value differenc- ing. Secret data was hiding in the component of a pixel in a color image. Arora and Anand [11] gave an approach for image steganography using edge detection method. In their study, edges of RGB image were detected by scanning method using 3∗3 window. Since human eyes are not sensitive to tiny alterations of noisy data, it will not be noticed when the data Hindawi Publishing Corporation Advances in Multimedia Volume 2015, Article ID 698492, 7 pages http://dx.doi.org/10.1155/2015/698492
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Research ArticleA New Information Hiding Method Based onImproved BPCS Steganography
Shuliang Sun12
1Department of Electronic and Information Engineering Fuqing Branch of Fujian Normal University Fuqing 350300 China2Innovative Information Industry Research Center Fuqing Branch of Fujian Normal University Fuqing 350300 China
Correspondence should be addressed to Shuliang Sun tjussl 07126com
Received 31 December 2014 Revised 15 March 2015 Accepted 16 March 2015
Academic Editor Deepu Rajan
Copyright copy 2015 Shuliang SunThis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
Bit-plane complexity segmentation (BPCS) steganography is advantageous in its capacity and imperceptibility The important stepof BPCS steganography is how to locate noisy regions in a cover image exactly The regular method black-and-white bordercomplexity is a simple and easy way but it is not always useful especially for periodical patterns Run-length irregularity andborder noisiness are introduced in this paper to work out this problem Canonical Cray coding (CGC) is also used to replacepure binary coding (PBC) because CGC makes use of characteristic of human vision system Conjugation operation is appliedto convert simple blocks into complex ones In order to contradict BPCS steganalysis improved BPCS steganography algorithmadopted different bit-planes with different complexity The higher the bit-plane is the smaller the complexity is It is proven thatthe improved BPCS steganography is superior to BPCS steganography by experiment
1 Introduction
Steganography is an art and science of invisible communica-tion It comes from Greek for covered writing and essentiallymeans ldquoto hide in plain sightrdquo [1] Embedding capacitysecurity (imperceptibility) and robustness are the mostimportant three features in steganographic system Gener-ally speaking there is a fundamental compromise betweenembedding capacity and security in information hidingscheme Steganography methods can be divided into twocategories original (spatial) domain methods and transformdomainmethods Cover image is transformed into frequencydomain first and then the secret messages are embedded inthe coefficients of transformed cover image Singh et al [2]proposed a method to combine two techniques One wasimage enhancement and the other was image steganographybased onDFTKaur andKochhar [3] provided a technique forimage steganography based onDCTThe algorithm had goodinvisibility and security A three-level DWT decompositionwas done on host image and secret information was hiddenin approximation coefficients of the decomposed image in[4] The stegoimage is subjected to image processing attacks
Abu et al [5] discussed a robust algorithm which wasbased on integer Haar wavelet transform and pixel valuedifference Ramezani et al [6] gave a steganography methodin contourlet domain The optimal pixel adjustment processand genetic algorithm are also used Alsaif and Saalih [7]presented a data hiding technique in NSCT domain Secretdata was embedded in high frequency directional band passof contourlet transform In spatial domain methods theprocessing is applied on the image pixel values directlyLeast significant bits (LSB) substitution [8] is the most well-known spatial steganographic system Although this methodis simple and easy it is weak in robustness and compressionsuch as JPEG compression Soumi et al [9] presented amosaic image steganography based on genetic algorithms forenhanced security Mandal and Das [10] proposed a colorimage steganography method based on pixel value differenc-ing Secret data was hiding in the component of a pixel ina color image Arora and Anand [11] gave an approach forimage steganography using edge detection method In theirstudy edges of RGB imagewere detected by scanningmethodusing 3lowast3 window Since human eyes are not sensitive to tinyalterations of noisy data it will not be noticed when the data
Hindawi Publishing CorporationAdvances in MultimediaVolume 2015 Article ID 698492 7 pageshttpdxdoiorg1011552015698492
2 Advances in Multimedia
Binary pixel block
Bit 7 Bit 6 Bit 5 Bit 4 Bit 3 Bit 2 Bit 1 Bit 0
8-bit image 1-bit image times 8
Figure 1 Binary pixel blocks on bit-planes
in noisy regions is replaced with another noisy data Thatis bit-plane complexity segmentation steganography (BPCSsteganography) [12]
2 The Principle of BPCS Steganography
BPCS steganography was first put forward by Kawaguchi andEason [13] The basic principle is that firstly cover imageis divided into ldquoinformative regionrdquo and ldquonoise-like regionrdquoThen the secret information is hidden in noise-like blocksof cover image [14] In LSB technique data is hidden inthe lowest bit-plane But in BPCS technique data is hiddenin pixel blocks of all the planes from the highest plane(most significant bit MSB plane) to the lowest plane (LSBplane) which have noisy patterns [15] In BPCS a gray imageconsisting of 119899-bit pixels can be decomposed into 119899-binaryplanes For example 119899 = 8 as shown in Figure 1
For example 119875 is an 119899-bit gray image here 119899 = 8Therefore = [119875
71198756
119875511987541198753 119875211987511198750] where 119875
7is the
MSB bit-plane and 1198750is the LSB bit-plane Each bit-plane
can be segmented into ldquoinformativerdquo region and ldquonoise-likerdquoregion It is simple in informative region and cannot be usedfor hiding information However it is complex in noise-likeregion and each noise-like region could be replaced withanother noise-like pattern in BPCS As a result it will notchange the overall quality of image after embedding Themost important step in BPCS is how to locate noisy regionsin a cover image correctly The regular method is to divideeach bit-plane of the cover image into small square binarypixel blocksThe blocks are considered as noisy regions thosehave complex black-and-white patterns Often 120572 is defined asa criterion to judge whether the block is complex or not [16]
120572 =
119896
2 times 119898 times (119898 minus 1)
(1)
where 119896 is the total length of border in a block 119898 is the rowor column of the block and 120572 is between 0 and 1 If 120572 is higherthan the given threshold value then the block is regarded ascomplex
3 Improved BPCS Steganography
The black-and-white border complexity is a simple and easymethod to judge whether the blocks are complex or not
However it is not always useful For example the blocks thathave periodical patterns such as chessboard or stripes arerecognized as complex ones in this way That is because asshown in Figure 2 120572
119886= 1 and 120572
119887= 12
But these blocks cannot be used for embedding dataotherwise the cover image will deteriorate obviously Thereare two new techniques to differentiate complex blocks fromsimple ones in this paper run-length irregularity and bordernoisiness
31 Run-Length Irregularity Run-length irregularity is thehistogram which consists of the run-lengths of both black-and-white pixels in a row or in a column
Suppose that ℎ[119894] is the frequency of runs of 119894 pixels eitherin black or in white and 119899 is the length of the pixel sequencethen ℎ
119904is used to measure the irregularity of a binary pixel
sequence
119901119894=
ℎ [119894]
sum119899
119895=1ℎ [119895]
ℎ119904= minus
119899
sum
119894=1
ℎ [119894] log2119901119894
(2)
The value of ℎ119904is often normalized to [0 1] and denoted by
ℎ119904If the size of block is 119899 times 119899 and 119903
119894and 119888119895are the 119894th row
and119895th column of a block then the run-length irregularity 120573of a block is defined as follows
120573 = min 119867119904(119903)
119867119904(119888) (3)
where119867119904(119903) =
ℎ119904(1199030)
ℎ119904(119903119899minus1
)
119867119904(119888) =
ℎ119904(1198880)
ℎ119904(119888119899minus1
)
(4)
And119883 is the mean of all the elements of119883According to the definition the smaller row and column
averages are taken as the value of the run-length irregularity120573 As seen in Figure 3 they are both periodic in row orcolumn As a result every run-length irregularity 120573 is 0 sothey are simple and cannot be used for embedding
The run-length irregularity 120573 is only useful in row orcolumn If the block is regular in other directions 120573will havenothing to do with it as shown in Figure 3
32 Border Noisiness If data is embedded on the boundary ofnoisy regions and informative regions of cover image blocksthen the noisy regions will be expended As a result the coverimage will be changed clearly
The border noisiness is based on the differences betweenadjacent binary pixel sequences in a block Similarly if theblock size is 119899times119899 and 119903
119894and 119888119895are the 119894th row and 119895th column
of a block then the border noisiness 120574 of a block is defined asfollows
120574 =
1
119899
min 119864119891[119875119909(119903)] 119864
119891[119875119909(119888)] (5)
Advances in Multimedia 3
(a) Chessboard (b) Stripes
Figure 2 Blocks with large 120572 while not complex
(a) 120573 = 0745 (b) 120573 = 0694
Figure 3 Blocks with large 120573 but those are not complex
where119875119909(119903) = 120588 (119903
0oplus 1199031) 120588 (119903
119899minus2oplus 119903119899minus1
)
119875119909(119888) = 120588 (119888
0oplus 1198881) 120588 (119888
119899minus2oplus 119888119899minus1
)
(6)
oplusmeans bitwise exclusive OR 120588(119909) is the number of ones ina binary sequence 119909 and
119864119891(119883) =
1 minus 119881 (119883)
max119883119881 (119883)
sdot 119883 (7)
where119883 = 1199090 119909
119898minus1119881(119883) = the variance of119883 and119883 =
the average of119883Theborder noisiness 120574 is used to check ifmany black-and-
white pixel borders are well distributed over a block alongboth the horizontal and the vertical directions
Though the blocks in Figures 3(a) and 3(b) both have largerun-length irregularity 120573 (120573
119886= 0745 120573
119887= 0694) their
border noisiness 120574 is as little as 0294 and 0048 respectivelySo they are not complex according to the border noisiness 120574and are not suitable for embedding
33 Default Threshold Values In a word a block 119861 is recog-nized as complex only if it satisfies the following conditions
120572 (119861) ge 120572119905 120573 (119861) ge 120573
119905 120574 (119861) ge 120574
119905 (8)
Here 120572119905 120573119905 and 120574
119905are threshold values Normally the default
threshold values are
120572119894
119905=
120572 minus 119894 sdot Δ120572 0 le 119894 le 5
0 6 le 119894 le 7
120573119894
119905=
120573 minus 119894 sdot Δ120573 0 le 119894 le 5
0 6 le 119894 le 7
120574119894
119905=
120574 minus 119894 sdot Δ120574 0 le 119894 le 5
0 6 le 119894 le 7
(9)
From the equation above it can be concluded that thebit-plane is higher and the threshold value is larger Thatis because it will raise noticeable changes for excessiveembedding data in high bit-plane and will freely get enoughembedding capacity in low bit-plane There is a fact thatthese threshold values are default and not always optimalThey could be adjusted manually according to the actualconditions
34 Canonical Gray Coding Normally the pixel value of animage is represented by pure binary code (PBC) Canonical
4 Advances in Multimedia
Gray coding (CGC) [17] is superior to pure binary code(PBC) in BPCS steganography because PBC will encounterldquoHamming cliffrdquo which means that a small change in pixelvalue will affect many bits of value [13] CGC makes full useof characteristic of human vision system and works out thisproblem
Suppose that 119887119894and 119892
119894are the 119894th bit of the pure binary
code and canonical Gray code with the same 119899-bit The twocodes are given by
11988701198871sdot sdot sdot 119887119899minus1
11989201198921sdot sdot sdot 119892119899minus1
(10)
The relationship between 119887119894and 119892
119894is given
119892119894=
1198870
119894 = 0
119887119894minus1
oplus 119887119894
119894 gt 0
119887119894=
1198920
119894 = 0
119887119894minus1
oplus 119892119894= 1198920oplus sdot sdot sdot oplus 119892
119894119894 gt 0
(11)
Here oplusmeans exclusive OR operationIt consists of informative and noise-like regions in
binary image Informative regions are simple while noise-like regions are complex If secret information is noise-likethen it is embedded in noise-like regions of the cover imagedirectly However if secret data is informative then it will beconjugated firstly so as to convert it into complex pattern
35 Conjugation Operation of a Binary Image Conjugation isused to convert simple blocks into complex ones In order toextract original data embedded in the cover image it shouldrecord which blocks of the original data are conjugated andwhich are not This information is stored in a conjugationmap It should be embedded along with the resource file asblocks Note that the blocks of the conjugation map shouldbe also conjugated if they are not complex enough
If 119875 is a binary image with size of 119899 times 119899 and black pixel isits foreground while white pixel is its background119882 and 119861respectively represent all-white and all-black patterns Nowtwo checkerboard patterns 119882
119888and 119861
119888are put forward In
particular119882119888has a white pixel at the upper-left position and
119861119888is its complement In binary image black-and-white pixels
particularly mean a logical value of 1 and 0Now 119875
lowast is defined as the conjugate of 119875 which satisfies
119875lowast= 119875 oplus119882
119888 (12)
The most important property about conjugation is
120572 (119875lowast) = 1 minus 120572 (119875) (13)
This property could transform informative pattern to com-plex one so both informative and noise-like regions could beused for embedding by conjugation operation
36 Statistical Analysis Though BPCS steganography algo-rithm is advantageous in imperceptibility it would change
8000
6000
4000
2000
0
minus2000
minus4000
minus60000 50 100 150 200 250 300
h1 minus h0
h2 minus h0
Figure 4 Two kinds of differences histograms
the statistic characteristics of bit-plane block complexityBecause adjacent pixels in the high bit-planes have a strongcorrelation the higher the bit-plane is the stronger thecorrelation is The method of steganalysis is proposed byZhang and Wang [17] Suppose the following (1) ℎ
0(119888) is
the complexity histogram of all the bit-plane blocks of coverimage and 119888 is the complexity of a block whose thresholdvalue is 120579 sdot 119862max (2) ℎ119868(119888) is the complexity histogram ofall the bit-plane blocks of secret image If 119888 is not as big asthe threshold value 120579 sdot 119862max the blocks should be done byconjugation first Finally ℎ
119904(119888) is the complexity histogram of
all the bit-plane blocks of cover image in which secret datahas been embedded and is defined as follows
ℎ119904(119888)
=
ℎ0(119888) 119888 le 120579 sdot 119862max
ℎ119868(119888) 120579 sdot 119862max lt 119888 lt (1 minus 120579) sdot 119862max
ℎ119868(119888) + ℎ
119868(119862max minus 119888) 119888 ge (1 minus 120579) sdot 119862max
(14)
From (14) it can be concluded that ℎ119904(119888) is composed of
three parts of complexity histograms each of them is smoothconnection but there is a sharp jump at the joint of twoadjacent histograms such as 119888 = 120579sdot119862max and 119888 = (1minus120579)sdot119862maxThe way of steganalysis is shown as follows
119889 (119888) = [ℎ (119888 minus 1) minus ℎ (119888)]
+ [ℎ (119862max minus 119888 + 1) minus ℎ (119862max minus 119888)]
0 lt 119888 lt 05119862max
(15)
119889(119888) is defined to measure the discontinuity of the histogramIf there are obvious peak values which are larger than 0 in the119889(119888) then the cover image will be considered as embeddingsecret information
Advances in Multimedia 5
Original image
(a)
BPCS steganography
(b)
Improved BPCS steganography
(c)
Original image
(d)
BPCS steganography
(e)
Improved BPCS steganography
(f)
Figure 5 Comparison of BPCS and improved BPCS
In order to resist BPCS statistical analysis the basic prin-ciple of improved BPCS steganography algorithm adopteddifferent bit-planewith different complexity which the higheris the complexity smaller is The threshold value of bit-planecomplexity is computed in the same way as in Section 33 (seeTable 2)
4 Improved BPCS Steganography Algorithm
(1) Convert the cover image and the secret image frompure binary code (PBC) into canonical Gray code(CGC)
(2) Calculate complexity measures 120572 120573 and 120574 for eachblock of each bit-plane of cover image and secretimage
(3) Perform conjugation operation on the ldquosimplerdquo orldquoinformativerdquo blocks of the secret image
(4) In order to contradict statistical steganalysis thethreshold value 120579 is calculated
(5) Perform embedding operation to embed secret imagein cover image
(6) Convert the embedded image from CGC to PBC
5 Experiment
In this paper the experiment is done with MATLAB 7 Theblock size is 8 times 8 and the peak signal-to-noise ratio (PSNR)and capacity are used to evaluate the quality of the coverimage after embedding
PSNR = 20log10
255
radic(1119873)sum119873minus1
119894=0(1198681015840
119894minus 119868119894)
2
(16)
119868119894is the value of a pixel in the original image and 119868
1015840
119894is the
value of the same pixel after embedding respectively119873 is thenumber of pixels
Capacity means the number of bits that could be used forembedding
This paper adopts three different images as samples Thesize of image is 512 times 512
From Table 1 it can be shown that though capacity isalmost the same between BPCS and the proposed methodthe PSNR value is much different It also can be concludedthat the proposed approach is better in PSNR value andcapacity than existing techniques
As shown in Figure 4 ℎ0 ℎ1 and ℎ
2are the histograms of
original image BPCS and improved BPCS
6 Advances in Multimedia
Table 1 Comparison of PSNR value and capacity with differentmethods
ImageLena Baboon Jet
Capacitybit
BPCS (120572 = 04) 951783 984782 942457Proposed method 954140 982769 943296Mandal and Das [10] 145787 144916 145648Juneja and Sandhu [8] 561345 830546 505658LSB 467004 720785 463758
PSNRdb
BPCS (120572 = 04) 4286 4085 4137Proposed method 4571 4276 4439Mandal and Das [10] 4226 3844 4260Juneja and Sandhu [8] 4459 3636 4250LSB 4101 3399 3938
Table 2 Threshold values 120573 120574 and 120579 of each bit-plane
119894 0 1 2 3 4 5 6 7120573119905
0632 0583 0534 0485 0436 0387 0 0120574119905
0394 0351 0308 0265 0222 0179 0 0120579119894
0562 0448 0334 0220 0106 0 0 0
The PSNR of the image obtained by BPCS (Figure 5(b))and improved BPCS (Figure 5(c)) was 4571 dB and 4286 dBFigures 5(d) 5(e) and 5(f) show the part areas of the imagesin Figures 5(a) 5(b) and 5(c) respectively It can be foundthat there is deterioration in the image obtained by BPCS(Figure 5(e)) but not in the image obtained by improvedBPCS (Figure 5(f))The experiment also shows that improvedBPCS can not only embed more data but also keep the imagequality higher In a word improved BPCS is superior to BPCSin steganography
6 Conclusion
It is shown that the final embedded image with the proposedmethod seems to be the same as the original image andlocates noise-like regions in a cover image more exactly Italso shows that the algorithm in this paper can withstandstatistics analysis while the way of BPCS cannot Experimentsalso prove that the proposed approach is better in PSNR valueand capacity than existing techniques
In this paper some parameters and thresholds are gotby experiment How to determine these values automaticallyneeds to be researched in the future
Conflict of Interests
The author declares that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
This research was financially supported by a grant from theNational Natural Science Foundation of China (no 61473329)
and the Special Research Foundation of the Fujian Province(Grant no JK2013062) and was also supported by FuqingBranch of Fujian Normal University of China (Grant nosKY2014028 and KY2014029)
References
[1] S Channalli and A Jadhav ldquoSteganography An art of hidingdatardquo International Journal on Computer Science and Engineer-ing vol 1 no 3 pp 137ndash141 2009
[2] I Singh S Khullar and S C Laroiya ldquoDFT based imageenhancement and steganographyrdquo International Journal ofComputer Science and Communication Engineering vol 2 no1 pp 5ndash7 2013
[3] G Kaur and A Kochhar ldquoA steganography implementationbased on LSB amp DCTrdquo International Journal for Science andEmerging Technologies with Latest Trends vol 4 no 1 pp 35ndash412012
[4] T Narasimmalou and R A Joseph ldquoRobust discrete wavelettransform based steganographyrdquo International Journal of PowerControl Signal and Computation vol 4 no 2 pp 102ndash108 2012
[5] N A Abu P W Adi and O Mohd ldquoRobust digital imagesteganography within coefficient difference on integer haarwavelet transformrdquo International Journal of Video amp ImageProcessing and Network Security vol 14 no 2 pp 1ndash8 2014
[6] H Ramezani F K Nia and M J S Zadeh ldquoA novel securesteganography in contourlet domainrdquo International Journal ofTrends in Economics Management amp Technology vol 2 no 5pp 27ndash29 2013
[7] K I Alsaif and M M Saalih ldquoText embedding based oncontourlet transformation coefficientsrdquo International Journal ofInformation Technology and Business Management vol 12 no 1pp 49ndash56 2013
[8] M Juneja and P S Sandhu ldquoAn improved LSB based steganog-raphy technique for RGB color imagesrdquo International Journalof Computer and Communication Engineering vol 2 no 4 pp513ndash517 2013
[9] C G Soumi J George and J Stephen ldquoGenetic algorithmbased mosaic image steganography for enhanced securityrdquoInternational Journal on Signal and Image Processing vol 5 no1 pp 15ndash26 2014
[10] J KMandal andDDas ldquoColour image steganography based onpixel value differencing in spatial domainrdquo International Journalof Information Sciences and Techniques vol 2 no 4 2012
[11] S Arora and S Anand ldquoA new approach for image steganogra-phy using edge detectionmethodrdquo International Journal of Inno-vative Research in Computer and Communication Engineeringvol 1 no 3 pp 626ndash629 2013
[12] P R Rudramath and M R Madki ldquoImproved BPCS steganog-raphy based novel approach for data embeddingrdquo InternationalJournal of Engineering and Innovative Technology vol 1 no 3pp 156ndash159 2012
[13] E Kawaguchi and R O Eason ldquoPrinciple and applications ofBPCS-steganographyrdquo inMultimedia Systems and Applicationsvol 3528 of Proceedings of SPIE pp 464ndash473 1998
[14] S S Khaire and S L Nalbalwar ldquoReview steganographymdashbit plane complexity segmentation (BPCS) techniquerdquo Interna-tional Journal of Engineering Science and Technology vol 2 no9 pp 4860ndash4868 2010
[15] H Liu and H D Yuan ldquoModified algorithm of bit-plane com-plexity segmentation steganography based on preprocessingrdquoJournal of Computer Applications vol 32 no 1 pp 89ndash91 2012
Advances in Multimedia 7
[16] A Al-Ataby and F Al-Naima ldquoA modified high capacity imagesteganography technique based on wavelet transformrdquo TheInternational Arab Journal of Information Technology vol 7 no4 pp 358ndash364 2010
[17] X Z Zhang and S Wang ldquoStatistical analysis against spatialBPCS steganographyrdquo Journal of Computer-Aided Design ampComputer Graphics vol 17 no 7 pp 1625ndash1629 2005
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2 Advances in Multimedia
Binary pixel block
Bit 7 Bit 6 Bit 5 Bit 4 Bit 3 Bit 2 Bit 1 Bit 0
8-bit image 1-bit image times 8
Figure 1 Binary pixel blocks on bit-planes
in noisy regions is replaced with another noisy data Thatis bit-plane complexity segmentation steganography (BPCSsteganography) [12]
2 The Principle of BPCS Steganography
BPCS steganography was first put forward by Kawaguchi andEason [13] The basic principle is that firstly cover imageis divided into ldquoinformative regionrdquo and ldquonoise-like regionrdquoThen the secret information is hidden in noise-like blocksof cover image [14] In LSB technique data is hidden inthe lowest bit-plane But in BPCS technique data is hiddenin pixel blocks of all the planes from the highest plane(most significant bit MSB plane) to the lowest plane (LSBplane) which have noisy patterns [15] In BPCS a gray imageconsisting of 119899-bit pixels can be decomposed into 119899-binaryplanes For example 119899 = 8 as shown in Figure 1
For example 119875 is an 119899-bit gray image here 119899 = 8Therefore = [119875
71198756
119875511987541198753 119875211987511198750] where 119875
7is the
MSB bit-plane and 1198750is the LSB bit-plane Each bit-plane
can be segmented into ldquoinformativerdquo region and ldquonoise-likerdquoregion It is simple in informative region and cannot be usedfor hiding information However it is complex in noise-likeregion and each noise-like region could be replaced withanother noise-like pattern in BPCS As a result it will notchange the overall quality of image after embedding Themost important step in BPCS is how to locate noisy regionsin a cover image correctly The regular method is to divideeach bit-plane of the cover image into small square binarypixel blocksThe blocks are considered as noisy regions thosehave complex black-and-white patterns Often 120572 is defined asa criterion to judge whether the block is complex or not [16]
120572 =
119896
2 times 119898 times (119898 minus 1)
(1)
where 119896 is the total length of border in a block 119898 is the rowor column of the block and 120572 is between 0 and 1 If 120572 is higherthan the given threshold value then the block is regarded ascomplex
3 Improved BPCS Steganography
The black-and-white border complexity is a simple and easymethod to judge whether the blocks are complex or not
However it is not always useful For example the blocks thathave periodical patterns such as chessboard or stripes arerecognized as complex ones in this way That is because asshown in Figure 2 120572
119886= 1 and 120572
119887= 12
But these blocks cannot be used for embedding dataotherwise the cover image will deteriorate obviously Thereare two new techniques to differentiate complex blocks fromsimple ones in this paper run-length irregularity and bordernoisiness
31 Run-Length Irregularity Run-length irregularity is thehistogram which consists of the run-lengths of both black-and-white pixels in a row or in a column
Suppose that ℎ[119894] is the frequency of runs of 119894 pixels eitherin black or in white and 119899 is the length of the pixel sequencethen ℎ
119904is used to measure the irregularity of a binary pixel
sequence
119901119894=
ℎ [119894]
sum119899
119895=1ℎ [119895]
ℎ119904= minus
119899
sum
119894=1
ℎ [119894] log2119901119894
(2)
The value of ℎ119904is often normalized to [0 1] and denoted by
ℎ119904If the size of block is 119899 times 119899 and 119903
119894and 119888119895are the 119894th row
and119895th column of a block then the run-length irregularity 120573of a block is defined as follows
120573 = min 119867119904(119903)
119867119904(119888) (3)
where119867119904(119903) =
ℎ119904(1199030)
ℎ119904(119903119899minus1
)
119867119904(119888) =
ℎ119904(1198880)
ℎ119904(119888119899minus1
)
(4)
And119883 is the mean of all the elements of119883According to the definition the smaller row and column
averages are taken as the value of the run-length irregularity120573 As seen in Figure 3 they are both periodic in row orcolumn As a result every run-length irregularity 120573 is 0 sothey are simple and cannot be used for embedding
The run-length irregularity 120573 is only useful in row orcolumn If the block is regular in other directions 120573will havenothing to do with it as shown in Figure 3
32 Border Noisiness If data is embedded on the boundary ofnoisy regions and informative regions of cover image blocksthen the noisy regions will be expended As a result the coverimage will be changed clearly
The border noisiness is based on the differences betweenadjacent binary pixel sequences in a block Similarly if theblock size is 119899times119899 and 119903
119894and 119888119895are the 119894th row and 119895th column
of a block then the border noisiness 120574 of a block is defined asfollows
120574 =
1
119899
min 119864119891[119875119909(119903)] 119864
119891[119875119909(119888)] (5)
Advances in Multimedia 3
(a) Chessboard (b) Stripes
Figure 2 Blocks with large 120572 while not complex
(a) 120573 = 0745 (b) 120573 = 0694
Figure 3 Blocks with large 120573 but those are not complex
where119875119909(119903) = 120588 (119903
0oplus 1199031) 120588 (119903
119899minus2oplus 119903119899minus1
)
119875119909(119888) = 120588 (119888
0oplus 1198881) 120588 (119888
119899minus2oplus 119888119899minus1
)
(6)
oplusmeans bitwise exclusive OR 120588(119909) is the number of ones ina binary sequence 119909 and
119864119891(119883) =
1 minus 119881 (119883)
max119883119881 (119883)
sdot 119883 (7)
where119883 = 1199090 119909
119898minus1119881(119883) = the variance of119883 and119883 =
the average of119883Theborder noisiness 120574 is used to check ifmany black-and-
white pixel borders are well distributed over a block alongboth the horizontal and the vertical directions
Though the blocks in Figures 3(a) and 3(b) both have largerun-length irregularity 120573 (120573
119886= 0745 120573
119887= 0694) their
border noisiness 120574 is as little as 0294 and 0048 respectivelySo they are not complex according to the border noisiness 120574and are not suitable for embedding
33 Default Threshold Values In a word a block 119861 is recog-nized as complex only if it satisfies the following conditions
120572 (119861) ge 120572119905 120573 (119861) ge 120573
119905 120574 (119861) ge 120574
119905 (8)
Here 120572119905 120573119905 and 120574
119905are threshold values Normally the default
threshold values are
120572119894
119905=
120572 minus 119894 sdot Δ120572 0 le 119894 le 5
0 6 le 119894 le 7
120573119894
119905=
120573 minus 119894 sdot Δ120573 0 le 119894 le 5
0 6 le 119894 le 7
120574119894
119905=
120574 minus 119894 sdot Δ120574 0 le 119894 le 5
0 6 le 119894 le 7
(9)
From the equation above it can be concluded that thebit-plane is higher and the threshold value is larger Thatis because it will raise noticeable changes for excessiveembedding data in high bit-plane and will freely get enoughembedding capacity in low bit-plane There is a fact thatthese threshold values are default and not always optimalThey could be adjusted manually according to the actualconditions
34 Canonical Gray Coding Normally the pixel value of animage is represented by pure binary code (PBC) Canonical
4 Advances in Multimedia
Gray coding (CGC) [17] is superior to pure binary code(PBC) in BPCS steganography because PBC will encounterldquoHamming cliffrdquo which means that a small change in pixelvalue will affect many bits of value [13] CGC makes full useof characteristic of human vision system and works out thisproblem
Suppose that 119887119894and 119892
119894are the 119894th bit of the pure binary
code and canonical Gray code with the same 119899-bit The twocodes are given by
11988701198871sdot sdot sdot 119887119899minus1
11989201198921sdot sdot sdot 119892119899minus1
(10)
The relationship between 119887119894and 119892
119894is given
119892119894=
1198870
119894 = 0
119887119894minus1
oplus 119887119894
119894 gt 0
119887119894=
1198920
119894 = 0
119887119894minus1
oplus 119892119894= 1198920oplus sdot sdot sdot oplus 119892
119894119894 gt 0
(11)
Here oplusmeans exclusive OR operationIt consists of informative and noise-like regions in
binary image Informative regions are simple while noise-like regions are complex If secret information is noise-likethen it is embedded in noise-like regions of the cover imagedirectly However if secret data is informative then it will beconjugated firstly so as to convert it into complex pattern
35 Conjugation Operation of a Binary Image Conjugation isused to convert simple blocks into complex ones In order toextract original data embedded in the cover image it shouldrecord which blocks of the original data are conjugated andwhich are not This information is stored in a conjugationmap It should be embedded along with the resource file asblocks Note that the blocks of the conjugation map shouldbe also conjugated if they are not complex enough
If 119875 is a binary image with size of 119899 times 119899 and black pixel isits foreground while white pixel is its background119882 and 119861respectively represent all-white and all-black patterns Nowtwo checkerboard patterns 119882
119888and 119861
119888are put forward In
particular119882119888has a white pixel at the upper-left position and
119861119888is its complement In binary image black-and-white pixels
particularly mean a logical value of 1 and 0Now 119875
lowast is defined as the conjugate of 119875 which satisfies
119875lowast= 119875 oplus119882
119888 (12)
The most important property about conjugation is
120572 (119875lowast) = 1 minus 120572 (119875) (13)
This property could transform informative pattern to com-plex one so both informative and noise-like regions could beused for embedding by conjugation operation
36 Statistical Analysis Though BPCS steganography algo-rithm is advantageous in imperceptibility it would change
8000
6000
4000
2000
0
minus2000
minus4000
minus60000 50 100 150 200 250 300
h1 minus h0
h2 minus h0
Figure 4 Two kinds of differences histograms
the statistic characteristics of bit-plane block complexityBecause adjacent pixels in the high bit-planes have a strongcorrelation the higher the bit-plane is the stronger thecorrelation is The method of steganalysis is proposed byZhang and Wang [17] Suppose the following (1) ℎ
0(119888) is
the complexity histogram of all the bit-plane blocks of coverimage and 119888 is the complexity of a block whose thresholdvalue is 120579 sdot 119862max (2) ℎ119868(119888) is the complexity histogram ofall the bit-plane blocks of secret image If 119888 is not as big asthe threshold value 120579 sdot 119862max the blocks should be done byconjugation first Finally ℎ
119904(119888) is the complexity histogram of
all the bit-plane blocks of cover image in which secret datahas been embedded and is defined as follows
ℎ119904(119888)
=
ℎ0(119888) 119888 le 120579 sdot 119862max
ℎ119868(119888) 120579 sdot 119862max lt 119888 lt (1 minus 120579) sdot 119862max
ℎ119868(119888) + ℎ
119868(119862max minus 119888) 119888 ge (1 minus 120579) sdot 119862max
(14)
From (14) it can be concluded that ℎ119904(119888) is composed of
three parts of complexity histograms each of them is smoothconnection but there is a sharp jump at the joint of twoadjacent histograms such as 119888 = 120579sdot119862max and 119888 = (1minus120579)sdot119862maxThe way of steganalysis is shown as follows
119889 (119888) = [ℎ (119888 minus 1) minus ℎ (119888)]
+ [ℎ (119862max minus 119888 + 1) minus ℎ (119862max minus 119888)]
0 lt 119888 lt 05119862max
(15)
119889(119888) is defined to measure the discontinuity of the histogramIf there are obvious peak values which are larger than 0 in the119889(119888) then the cover image will be considered as embeddingsecret information
Advances in Multimedia 5
Original image
(a)
BPCS steganography
(b)
Improved BPCS steganography
(c)
Original image
(d)
BPCS steganography
(e)
Improved BPCS steganography
(f)
Figure 5 Comparison of BPCS and improved BPCS
In order to resist BPCS statistical analysis the basic prin-ciple of improved BPCS steganography algorithm adopteddifferent bit-planewith different complexity which the higheris the complexity smaller is The threshold value of bit-planecomplexity is computed in the same way as in Section 33 (seeTable 2)
4 Improved BPCS Steganography Algorithm
(1) Convert the cover image and the secret image frompure binary code (PBC) into canonical Gray code(CGC)
(2) Calculate complexity measures 120572 120573 and 120574 for eachblock of each bit-plane of cover image and secretimage
(3) Perform conjugation operation on the ldquosimplerdquo orldquoinformativerdquo blocks of the secret image
(4) In order to contradict statistical steganalysis thethreshold value 120579 is calculated
(5) Perform embedding operation to embed secret imagein cover image
(6) Convert the embedded image from CGC to PBC
5 Experiment
In this paper the experiment is done with MATLAB 7 Theblock size is 8 times 8 and the peak signal-to-noise ratio (PSNR)and capacity are used to evaluate the quality of the coverimage after embedding
PSNR = 20log10
255
radic(1119873)sum119873minus1
119894=0(1198681015840
119894minus 119868119894)
2
(16)
119868119894is the value of a pixel in the original image and 119868
1015840
119894is the
value of the same pixel after embedding respectively119873 is thenumber of pixels
Capacity means the number of bits that could be used forembedding
This paper adopts three different images as samples Thesize of image is 512 times 512
From Table 1 it can be shown that though capacity isalmost the same between BPCS and the proposed methodthe PSNR value is much different It also can be concludedthat the proposed approach is better in PSNR value andcapacity than existing techniques
As shown in Figure 4 ℎ0 ℎ1 and ℎ
2are the histograms of
original image BPCS and improved BPCS
6 Advances in Multimedia
Table 1 Comparison of PSNR value and capacity with differentmethods
ImageLena Baboon Jet
Capacitybit
BPCS (120572 = 04) 951783 984782 942457Proposed method 954140 982769 943296Mandal and Das [10] 145787 144916 145648Juneja and Sandhu [8] 561345 830546 505658LSB 467004 720785 463758
PSNRdb
BPCS (120572 = 04) 4286 4085 4137Proposed method 4571 4276 4439Mandal and Das [10] 4226 3844 4260Juneja and Sandhu [8] 4459 3636 4250LSB 4101 3399 3938
Table 2 Threshold values 120573 120574 and 120579 of each bit-plane
119894 0 1 2 3 4 5 6 7120573119905
0632 0583 0534 0485 0436 0387 0 0120574119905
0394 0351 0308 0265 0222 0179 0 0120579119894
0562 0448 0334 0220 0106 0 0 0
The PSNR of the image obtained by BPCS (Figure 5(b))and improved BPCS (Figure 5(c)) was 4571 dB and 4286 dBFigures 5(d) 5(e) and 5(f) show the part areas of the imagesin Figures 5(a) 5(b) and 5(c) respectively It can be foundthat there is deterioration in the image obtained by BPCS(Figure 5(e)) but not in the image obtained by improvedBPCS (Figure 5(f))The experiment also shows that improvedBPCS can not only embed more data but also keep the imagequality higher In a word improved BPCS is superior to BPCSin steganography
6 Conclusion
It is shown that the final embedded image with the proposedmethod seems to be the same as the original image andlocates noise-like regions in a cover image more exactly Italso shows that the algorithm in this paper can withstandstatistics analysis while the way of BPCS cannot Experimentsalso prove that the proposed approach is better in PSNR valueand capacity than existing techniques
In this paper some parameters and thresholds are gotby experiment How to determine these values automaticallyneeds to be researched in the future
Conflict of Interests
The author declares that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
This research was financially supported by a grant from theNational Natural Science Foundation of China (no 61473329)
and the Special Research Foundation of the Fujian Province(Grant no JK2013062) and was also supported by FuqingBranch of Fujian Normal University of China (Grant nosKY2014028 and KY2014029)
References
[1] S Channalli and A Jadhav ldquoSteganography An art of hidingdatardquo International Journal on Computer Science and Engineer-ing vol 1 no 3 pp 137ndash141 2009
[2] I Singh S Khullar and S C Laroiya ldquoDFT based imageenhancement and steganographyrdquo International Journal ofComputer Science and Communication Engineering vol 2 no1 pp 5ndash7 2013
[3] G Kaur and A Kochhar ldquoA steganography implementationbased on LSB amp DCTrdquo International Journal for Science andEmerging Technologies with Latest Trends vol 4 no 1 pp 35ndash412012
[4] T Narasimmalou and R A Joseph ldquoRobust discrete wavelettransform based steganographyrdquo International Journal of PowerControl Signal and Computation vol 4 no 2 pp 102ndash108 2012
[5] N A Abu P W Adi and O Mohd ldquoRobust digital imagesteganography within coefficient difference on integer haarwavelet transformrdquo International Journal of Video amp ImageProcessing and Network Security vol 14 no 2 pp 1ndash8 2014
[6] H Ramezani F K Nia and M J S Zadeh ldquoA novel securesteganography in contourlet domainrdquo International Journal ofTrends in Economics Management amp Technology vol 2 no 5pp 27ndash29 2013
[7] K I Alsaif and M M Saalih ldquoText embedding based oncontourlet transformation coefficientsrdquo International Journal ofInformation Technology and Business Management vol 12 no 1pp 49ndash56 2013
[8] M Juneja and P S Sandhu ldquoAn improved LSB based steganog-raphy technique for RGB color imagesrdquo International Journalof Computer and Communication Engineering vol 2 no 4 pp513ndash517 2013
[9] C G Soumi J George and J Stephen ldquoGenetic algorithmbased mosaic image steganography for enhanced securityrdquoInternational Journal on Signal and Image Processing vol 5 no1 pp 15ndash26 2014
[10] J KMandal andDDas ldquoColour image steganography based onpixel value differencing in spatial domainrdquo International Journalof Information Sciences and Techniques vol 2 no 4 2012
[11] S Arora and S Anand ldquoA new approach for image steganogra-phy using edge detectionmethodrdquo International Journal of Inno-vative Research in Computer and Communication Engineeringvol 1 no 3 pp 626ndash629 2013
[12] P R Rudramath and M R Madki ldquoImproved BPCS steganog-raphy based novel approach for data embeddingrdquo InternationalJournal of Engineering and Innovative Technology vol 1 no 3pp 156ndash159 2012
[13] E Kawaguchi and R O Eason ldquoPrinciple and applications ofBPCS-steganographyrdquo inMultimedia Systems and Applicationsvol 3528 of Proceedings of SPIE pp 464ndash473 1998
[14] S S Khaire and S L Nalbalwar ldquoReview steganographymdashbit plane complexity segmentation (BPCS) techniquerdquo Interna-tional Journal of Engineering Science and Technology vol 2 no9 pp 4860ndash4868 2010
[15] H Liu and H D Yuan ldquoModified algorithm of bit-plane com-plexity segmentation steganography based on preprocessingrdquoJournal of Computer Applications vol 32 no 1 pp 89ndash91 2012
Advances in Multimedia 7
[16] A Al-Ataby and F Al-Naima ldquoA modified high capacity imagesteganography technique based on wavelet transformrdquo TheInternational Arab Journal of Information Technology vol 7 no4 pp 358ndash364 2010
[17] X Z Zhang and S Wang ldquoStatistical analysis against spatialBPCS steganographyrdquo Journal of Computer-Aided Design ampComputer Graphics vol 17 no 7 pp 1625ndash1629 2005
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Active and Passive Electronic Components
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RotatingMachinery
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VLSI Design
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Shock and Vibration
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Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
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Chemical EngineeringInternational Journal of Antennas and
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International Journal of
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Navigation and Observation
International Journal of
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DistributedSensor Networks
International Journal of
Advances in Multimedia 3
(a) Chessboard (b) Stripes
Figure 2 Blocks with large 120572 while not complex
(a) 120573 = 0745 (b) 120573 = 0694
Figure 3 Blocks with large 120573 but those are not complex
where119875119909(119903) = 120588 (119903
0oplus 1199031) 120588 (119903
119899minus2oplus 119903119899minus1
)
119875119909(119888) = 120588 (119888
0oplus 1198881) 120588 (119888
119899minus2oplus 119888119899minus1
)
(6)
oplusmeans bitwise exclusive OR 120588(119909) is the number of ones ina binary sequence 119909 and
119864119891(119883) =
1 minus 119881 (119883)
max119883119881 (119883)
sdot 119883 (7)
where119883 = 1199090 119909
119898minus1119881(119883) = the variance of119883 and119883 =
the average of119883Theborder noisiness 120574 is used to check ifmany black-and-
white pixel borders are well distributed over a block alongboth the horizontal and the vertical directions
Though the blocks in Figures 3(a) and 3(b) both have largerun-length irregularity 120573 (120573
119886= 0745 120573
119887= 0694) their
border noisiness 120574 is as little as 0294 and 0048 respectivelySo they are not complex according to the border noisiness 120574and are not suitable for embedding
33 Default Threshold Values In a word a block 119861 is recog-nized as complex only if it satisfies the following conditions
120572 (119861) ge 120572119905 120573 (119861) ge 120573
119905 120574 (119861) ge 120574
119905 (8)
Here 120572119905 120573119905 and 120574
119905are threshold values Normally the default
threshold values are
120572119894
119905=
120572 minus 119894 sdot Δ120572 0 le 119894 le 5
0 6 le 119894 le 7
120573119894
119905=
120573 minus 119894 sdot Δ120573 0 le 119894 le 5
0 6 le 119894 le 7
120574119894
119905=
120574 minus 119894 sdot Δ120574 0 le 119894 le 5
0 6 le 119894 le 7
(9)
From the equation above it can be concluded that thebit-plane is higher and the threshold value is larger Thatis because it will raise noticeable changes for excessiveembedding data in high bit-plane and will freely get enoughembedding capacity in low bit-plane There is a fact thatthese threshold values are default and not always optimalThey could be adjusted manually according to the actualconditions
34 Canonical Gray Coding Normally the pixel value of animage is represented by pure binary code (PBC) Canonical
4 Advances in Multimedia
Gray coding (CGC) [17] is superior to pure binary code(PBC) in BPCS steganography because PBC will encounterldquoHamming cliffrdquo which means that a small change in pixelvalue will affect many bits of value [13] CGC makes full useof characteristic of human vision system and works out thisproblem
Suppose that 119887119894and 119892
119894are the 119894th bit of the pure binary
code and canonical Gray code with the same 119899-bit The twocodes are given by
11988701198871sdot sdot sdot 119887119899minus1
11989201198921sdot sdot sdot 119892119899minus1
(10)
The relationship between 119887119894and 119892
119894is given
119892119894=
1198870
119894 = 0
119887119894minus1
oplus 119887119894
119894 gt 0
119887119894=
1198920
119894 = 0
119887119894minus1
oplus 119892119894= 1198920oplus sdot sdot sdot oplus 119892
119894119894 gt 0
(11)
Here oplusmeans exclusive OR operationIt consists of informative and noise-like regions in
binary image Informative regions are simple while noise-like regions are complex If secret information is noise-likethen it is embedded in noise-like regions of the cover imagedirectly However if secret data is informative then it will beconjugated firstly so as to convert it into complex pattern
35 Conjugation Operation of a Binary Image Conjugation isused to convert simple blocks into complex ones In order toextract original data embedded in the cover image it shouldrecord which blocks of the original data are conjugated andwhich are not This information is stored in a conjugationmap It should be embedded along with the resource file asblocks Note that the blocks of the conjugation map shouldbe also conjugated if they are not complex enough
If 119875 is a binary image with size of 119899 times 119899 and black pixel isits foreground while white pixel is its background119882 and 119861respectively represent all-white and all-black patterns Nowtwo checkerboard patterns 119882
119888and 119861
119888are put forward In
particular119882119888has a white pixel at the upper-left position and
119861119888is its complement In binary image black-and-white pixels
particularly mean a logical value of 1 and 0Now 119875
lowast is defined as the conjugate of 119875 which satisfies
119875lowast= 119875 oplus119882
119888 (12)
The most important property about conjugation is
120572 (119875lowast) = 1 minus 120572 (119875) (13)
This property could transform informative pattern to com-plex one so both informative and noise-like regions could beused for embedding by conjugation operation
36 Statistical Analysis Though BPCS steganography algo-rithm is advantageous in imperceptibility it would change
8000
6000
4000
2000
0
minus2000
minus4000
minus60000 50 100 150 200 250 300
h1 minus h0
h2 minus h0
Figure 4 Two kinds of differences histograms
the statistic characteristics of bit-plane block complexityBecause adjacent pixels in the high bit-planes have a strongcorrelation the higher the bit-plane is the stronger thecorrelation is The method of steganalysis is proposed byZhang and Wang [17] Suppose the following (1) ℎ
0(119888) is
the complexity histogram of all the bit-plane blocks of coverimage and 119888 is the complexity of a block whose thresholdvalue is 120579 sdot 119862max (2) ℎ119868(119888) is the complexity histogram ofall the bit-plane blocks of secret image If 119888 is not as big asthe threshold value 120579 sdot 119862max the blocks should be done byconjugation first Finally ℎ
119904(119888) is the complexity histogram of
all the bit-plane blocks of cover image in which secret datahas been embedded and is defined as follows
ℎ119904(119888)
=
ℎ0(119888) 119888 le 120579 sdot 119862max
ℎ119868(119888) 120579 sdot 119862max lt 119888 lt (1 minus 120579) sdot 119862max
ℎ119868(119888) + ℎ
119868(119862max minus 119888) 119888 ge (1 minus 120579) sdot 119862max
(14)
From (14) it can be concluded that ℎ119904(119888) is composed of
three parts of complexity histograms each of them is smoothconnection but there is a sharp jump at the joint of twoadjacent histograms such as 119888 = 120579sdot119862max and 119888 = (1minus120579)sdot119862maxThe way of steganalysis is shown as follows
119889 (119888) = [ℎ (119888 minus 1) minus ℎ (119888)]
+ [ℎ (119862max minus 119888 + 1) minus ℎ (119862max minus 119888)]
0 lt 119888 lt 05119862max
(15)
119889(119888) is defined to measure the discontinuity of the histogramIf there are obvious peak values which are larger than 0 in the119889(119888) then the cover image will be considered as embeddingsecret information
Advances in Multimedia 5
Original image
(a)
BPCS steganography
(b)
Improved BPCS steganography
(c)
Original image
(d)
BPCS steganography
(e)
Improved BPCS steganography
(f)
Figure 5 Comparison of BPCS and improved BPCS
In order to resist BPCS statistical analysis the basic prin-ciple of improved BPCS steganography algorithm adopteddifferent bit-planewith different complexity which the higheris the complexity smaller is The threshold value of bit-planecomplexity is computed in the same way as in Section 33 (seeTable 2)
4 Improved BPCS Steganography Algorithm
(1) Convert the cover image and the secret image frompure binary code (PBC) into canonical Gray code(CGC)
(2) Calculate complexity measures 120572 120573 and 120574 for eachblock of each bit-plane of cover image and secretimage
(3) Perform conjugation operation on the ldquosimplerdquo orldquoinformativerdquo blocks of the secret image
(4) In order to contradict statistical steganalysis thethreshold value 120579 is calculated
(5) Perform embedding operation to embed secret imagein cover image
(6) Convert the embedded image from CGC to PBC
5 Experiment
In this paper the experiment is done with MATLAB 7 Theblock size is 8 times 8 and the peak signal-to-noise ratio (PSNR)and capacity are used to evaluate the quality of the coverimage after embedding
PSNR = 20log10
255
radic(1119873)sum119873minus1
119894=0(1198681015840
119894minus 119868119894)
2
(16)
119868119894is the value of a pixel in the original image and 119868
1015840
119894is the
value of the same pixel after embedding respectively119873 is thenumber of pixels
Capacity means the number of bits that could be used forembedding
This paper adopts three different images as samples Thesize of image is 512 times 512
From Table 1 it can be shown that though capacity isalmost the same between BPCS and the proposed methodthe PSNR value is much different It also can be concludedthat the proposed approach is better in PSNR value andcapacity than existing techniques
As shown in Figure 4 ℎ0 ℎ1 and ℎ
2are the histograms of
original image BPCS and improved BPCS
6 Advances in Multimedia
Table 1 Comparison of PSNR value and capacity with differentmethods
ImageLena Baboon Jet
Capacitybit
BPCS (120572 = 04) 951783 984782 942457Proposed method 954140 982769 943296Mandal and Das [10] 145787 144916 145648Juneja and Sandhu [8] 561345 830546 505658LSB 467004 720785 463758
PSNRdb
BPCS (120572 = 04) 4286 4085 4137Proposed method 4571 4276 4439Mandal and Das [10] 4226 3844 4260Juneja and Sandhu [8] 4459 3636 4250LSB 4101 3399 3938
Table 2 Threshold values 120573 120574 and 120579 of each bit-plane
119894 0 1 2 3 4 5 6 7120573119905
0632 0583 0534 0485 0436 0387 0 0120574119905
0394 0351 0308 0265 0222 0179 0 0120579119894
0562 0448 0334 0220 0106 0 0 0
The PSNR of the image obtained by BPCS (Figure 5(b))and improved BPCS (Figure 5(c)) was 4571 dB and 4286 dBFigures 5(d) 5(e) and 5(f) show the part areas of the imagesin Figures 5(a) 5(b) and 5(c) respectively It can be foundthat there is deterioration in the image obtained by BPCS(Figure 5(e)) but not in the image obtained by improvedBPCS (Figure 5(f))The experiment also shows that improvedBPCS can not only embed more data but also keep the imagequality higher In a word improved BPCS is superior to BPCSin steganography
6 Conclusion
It is shown that the final embedded image with the proposedmethod seems to be the same as the original image andlocates noise-like regions in a cover image more exactly Italso shows that the algorithm in this paper can withstandstatistics analysis while the way of BPCS cannot Experimentsalso prove that the proposed approach is better in PSNR valueand capacity than existing techniques
In this paper some parameters and thresholds are gotby experiment How to determine these values automaticallyneeds to be researched in the future
Conflict of Interests
The author declares that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
This research was financially supported by a grant from theNational Natural Science Foundation of China (no 61473329)
and the Special Research Foundation of the Fujian Province(Grant no JK2013062) and was also supported by FuqingBranch of Fujian Normal University of China (Grant nosKY2014028 and KY2014029)
References
[1] S Channalli and A Jadhav ldquoSteganography An art of hidingdatardquo International Journal on Computer Science and Engineer-ing vol 1 no 3 pp 137ndash141 2009
[2] I Singh S Khullar and S C Laroiya ldquoDFT based imageenhancement and steganographyrdquo International Journal ofComputer Science and Communication Engineering vol 2 no1 pp 5ndash7 2013
[3] G Kaur and A Kochhar ldquoA steganography implementationbased on LSB amp DCTrdquo International Journal for Science andEmerging Technologies with Latest Trends vol 4 no 1 pp 35ndash412012
[4] T Narasimmalou and R A Joseph ldquoRobust discrete wavelettransform based steganographyrdquo International Journal of PowerControl Signal and Computation vol 4 no 2 pp 102ndash108 2012
[5] N A Abu P W Adi and O Mohd ldquoRobust digital imagesteganography within coefficient difference on integer haarwavelet transformrdquo International Journal of Video amp ImageProcessing and Network Security vol 14 no 2 pp 1ndash8 2014
[6] H Ramezani F K Nia and M J S Zadeh ldquoA novel securesteganography in contourlet domainrdquo International Journal ofTrends in Economics Management amp Technology vol 2 no 5pp 27ndash29 2013
[7] K I Alsaif and M M Saalih ldquoText embedding based oncontourlet transformation coefficientsrdquo International Journal ofInformation Technology and Business Management vol 12 no 1pp 49ndash56 2013
[8] M Juneja and P S Sandhu ldquoAn improved LSB based steganog-raphy technique for RGB color imagesrdquo International Journalof Computer and Communication Engineering vol 2 no 4 pp513ndash517 2013
[9] C G Soumi J George and J Stephen ldquoGenetic algorithmbased mosaic image steganography for enhanced securityrdquoInternational Journal on Signal and Image Processing vol 5 no1 pp 15ndash26 2014
[10] J KMandal andDDas ldquoColour image steganography based onpixel value differencing in spatial domainrdquo International Journalof Information Sciences and Techniques vol 2 no 4 2012
[11] S Arora and S Anand ldquoA new approach for image steganogra-phy using edge detectionmethodrdquo International Journal of Inno-vative Research in Computer and Communication Engineeringvol 1 no 3 pp 626ndash629 2013
[12] P R Rudramath and M R Madki ldquoImproved BPCS steganog-raphy based novel approach for data embeddingrdquo InternationalJournal of Engineering and Innovative Technology vol 1 no 3pp 156ndash159 2012
[13] E Kawaguchi and R O Eason ldquoPrinciple and applications ofBPCS-steganographyrdquo inMultimedia Systems and Applicationsvol 3528 of Proceedings of SPIE pp 464ndash473 1998
[14] S S Khaire and S L Nalbalwar ldquoReview steganographymdashbit plane complexity segmentation (BPCS) techniquerdquo Interna-tional Journal of Engineering Science and Technology vol 2 no9 pp 4860ndash4868 2010
[15] H Liu and H D Yuan ldquoModified algorithm of bit-plane com-plexity segmentation steganography based on preprocessingrdquoJournal of Computer Applications vol 32 no 1 pp 89ndash91 2012
Advances in Multimedia 7
[16] A Al-Ataby and F Al-Naima ldquoA modified high capacity imagesteganography technique based on wavelet transformrdquo TheInternational Arab Journal of Information Technology vol 7 no4 pp 358ndash364 2010
[17] X Z Zhang and S Wang ldquoStatistical analysis against spatialBPCS steganographyrdquo Journal of Computer-Aided Design ampComputer Graphics vol 17 no 7 pp 1625ndash1629 2005
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
4 Advances in Multimedia
Gray coding (CGC) [17] is superior to pure binary code(PBC) in BPCS steganography because PBC will encounterldquoHamming cliffrdquo which means that a small change in pixelvalue will affect many bits of value [13] CGC makes full useof characteristic of human vision system and works out thisproblem
Suppose that 119887119894and 119892
119894are the 119894th bit of the pure binary
code and canonical Gray code with the same 119899-bit The twocodes are given by
11988701198871sdot sdot sdot 119887119899minus1
11989201198921sdot sdot sdot 119892119899minus1
(10)
The relationship between 119887119894and 119892
119894is given
119892119894=
1198870
119894 = 0
119887119894minus1
oplus 119887119894
119894 gt 0
119887119894=
1198920
119894 = 0
119887119894minus1
oplus 119892119894= 1198920oplus sdot sdot sdot oplus 119892
119894119894 gt 0
(11)
Here oplusmeans exclusive OR operationIt consists of informative and noise-like regions in
binary image Informative regions are simple while noise-like regions are complex If secret information is noise-likethen it is embedded in noise-like regions of the cover imagedirectly However if secret data is informative then it will beconjugated firstly so as to convert it into complex pattern
35 Conjugation Operation of a Binary Image Conjugation isused to convert simple blocks into complex ones In order toextract original data embedded in the cover image it shouldrecord which blocks of the original data are conjugated andwhich are not This information is stored in a conjugationmap It should be embedded along with the resource file asblocks Note that the blocks of the conjugation map shouldbe also conjugated if they are not complex enough
If 119875 is a binary image with size of 119899 times 119899 and black pixel isits foreground while white pixel is its background119882 and 119861respectively represent all-white and all-black patterns Nowtwo checkerboard patterns 119882
119888and 119861
119888are put forward In
particular119882119888has a white pixel at the upper-left position and
119861119888is its complement In binary image black-and-white pixels
particularly mean a logical value of 1 and 0Now 119875
lowast is defined as the conjugate of 119875 which satisfies
119875lowast= 119875 oplus119882
119888 (12)
The most important property about conjugation is
120572 (119875lowast) = 1 minus 120572 (119875) (13)
This property could transform informative pattern to com-plex one so both informative and noise-like regions could beused for embedding by conjugation operation
36 Statistical Analysis Though BPCS steganography algo-rithm is advantageous in imperceptibility it would change
8000
6000
4000
2000
0
minus2000
minus4000
minus60000 50 100 150 200 250 300
h1 minus h0
h2 minus h0
Figure 4 Two kinds of differences histograms
the statistic characteristics of bit-plane block complexityBecause adjacent pixels in the high bit-planes have a strongcorrelation the higher the bit-plane is the stronger thecorrelation is The method of steganalysis is proposed byZhang and Wang [17] Suppose the following (1) ℎ
0(119888) is
the complexity histogram of all the bit-plane blocks of coverimage and 119888 is the complexity of a block whose thresholdvalue is 120579 sdot 119862max (2) ℎ119868(119888) is the complexity histogram ofall the bit-plane blocks of secret image If 119888 is not as big asthe threshold value 120579 sdot 119862max the blocks should be done byconjugation first Finally ℎ
119904(119888) is the complexity histogram of
all the bit-plane blocks of cover image in which secret datahas been embedded and is defined as follows
ℎ119904(119888)
=
ℎ0(119888) 119888 le 120579 sdot 119862max
ℎ119868(119888) 120579 sdot 119862max lt 119888 lt (1 minus 120579) sdot 119862max
ℎ119868(119888) + ℎ
119868(119862max minus 119888) 119888 ge (1 minus 120579) sdot 119862max
(14)
From (14) it can be concluded that ℎ119904(119888) is composed of
three parts of complexity histograms each of them is smoothconnection but there is a sharp jump at the joint of twoadjacent histograms such as 119888 = 120579sdot119862max and 119888 = (1minus120579)sdot119862maxThe way of steganalysis is shown as follows
119889 (119888) = [ℎ (119888 minus 1) minus ℎ (119888)]
+ [ℎ (119862max minus 119888 + 1) minus ℎ (119862max minus 119888)]
0 lt 119888 lt 05119862max
(15)
119889(119888) is defined to measure the discontinuity of the histogramIf there are obvious peak values which are larger than 0 in the119889(119888) then the cover image will be considered as embeddingsecret information
Advances in Multimedia 5
Original image
(a)
BPCS steganography
(b)
Improved BPCS steganography
(c)
Original image
(d)
BPCS steganography
(e)
Improved BPCS steganography
(f)
Figure 5 Comparison of BPCS and improved BPCS
In order to resist BPCS statistical analysis the basic prin-ciple of improved BPCS steganography algorithm adopteddifferent bit-planewith different complexity which the higheris the complexity smaller is The threshold value of bit-planecomplexity is computed in the same way as in Section 33 (seeTable 2)
4 Improved BPCS Steganography Algorithm
(1) Convert the cover image and the secret image frompure binary code (PBC) into canonical Gray code(CGC)
(2) Calculate complexity measures 120572 120573 and 120574 for eachblock of each bit-plane of cover image and secretimage
(3) Perform conjugation operation on the ldquosimplerdquo orldquoinformativerdquo blocks of the secret image
(4) In order to contradict statistical steganalysis thethreshold value 120579 is calculated
(5) Perform embedding operation to embed secret imagein cover image
(6) Convert the embedded image from CGC to PBC
5 Experiment
In this paper the experiment is done with MATLAB 7 Theblock size is 8 times 8 and the peak signal-to-noise ratio (PSNR)and capacity are used to evaluate the quality of the coverimage after embedding
PSNR = 20log10
255
radic(1119873)sum119873minus1
119894=0(1198681015840
119894minus 119868119894)
2
(16)
119868119894is the value of a pixel in the original image and 119868
1015840
119894is the
value of the same pixel after embedding respectively119873 is thenumber of pixels
Capacity means the number of bits that could be used forembedding
This paper adopts three different images as samples Thesize of image is 512 times 512
From Table 1 it can be shown that though capacity isalmost the same between BPCS and the proposed methodthe PSNR value is much different It also can be concludedthat the proposed approach is better in PSNR value andcapacity than existing techniques
As shown in Figure 4 ℎ0 ℎ1 and ℎ
2are the histograms of
original image BPCS and improved BPCS
6 Advances in Multimedia
Table 1 Comparison of PSNR value and capacity with differentmethods
ImageLena Baboon Jet
Capacitybit
BPCS (120572 = 04) 951783 984782 942457Proposed method 954140 982769 943296Mandal and Das [10] 145787 144916 145648Juneja and Sandhu [8] 561345 830546 505658LSB 467004 720785 463758
PSNRdb
BPCS (120572 = 04) 4286 4085 4137Proposed method 4571 4276 4439Mandal and Das [10] 4226 3844 4260Juneja and Sandhu [8] 4459 3636 4250LSB 4101 3399 3938
Table 2 Threshold values 120573 120574 and 120579 of each bit-plane
119894 0 1 2 3 4 5 6 7120573119905
0632 0583 0534 0485 0436 0387 0 0120574119905
0394 0351 0308 0265 0222 0179 0 0120579119894
0562 0448 0334 0220 0106 0 0 0
The PSNR of the image obtained by BPCS (Figure 5(b))and improved BPCS (Figure 5(c)) was 4571 dB and 4286 dBFigures 5(d) 5(e) and 5(f) show the part areas of the imagesin Figures 5(a) 5(b) and 5(c) respectively It can be foundthat there is deterioration in the image obtained by BPCS(Figure 5(e)) but not in the image obtained by improvedBPCS (Figure 5(f))The experiment also shows that improvedBPCS can not only embed more data but also keep the imagequality higher In a word improved BPCS is superior to BPCSin steganography
6 Conclusion
It is shown that the final embedded image with the proposedmethod seems to be the same as the original image andlocates noise-like regions in a cover image more exactly Italso shows that the algorithm in this paper can withstandstatistics analysis while the way of BPCS cannot Experimentsalso prove that the proposed approach is better in PSNR valueand capacity than existing techniques
In this paper some parameters and thresholds are gotby experiment How to determine these values automaticallyneeds to be researched in the future
Conflict of Interests
The author declares that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
This research was financially supported by a grant from theNational Natural Science Foundation of China (no 61473329)
and the Special Research Foundation of the Fujian Province(Grant no JK2013062) and was also supported by FuqingBranch of Fujian Normal University of China (Grant nosKY2014028 and KY2014029)
References
[1] S Channalli and A Jadhav ldquoSteganography An art of hidingdatardquo International Journal on Computer Science and Engineer-ing vol 1 no 3 pp 137ndash141 2009
[2] I Singh S Khullar and S C Laroiya ldquoDFT based imageenhancement and steganographyrdquo International Journal ofComputer Science and Communication Engineering vol 2 no1 pp 5ndash7 2013
[3] G Kaur and A Kochhar ldquoA steganography implementationbased on LSB amp DCTrdquo International Journal for Science andEmerging Technologies with Latest Trends vol 4 no 1 pp 35ndash412012
[4] T Narasimmalou and R A Joseph ldquoRobust discrete wavelettransform based steganographyrdquo International Journal of PowerControl Signal and Computation vol 4 no 2 pp 102ndash108 2012
[5] N A Abu P W Adi and O Mohd ldquoRobust digital imagesteganography within coefficient difference on integer haarwavelet transformrdquo International Journal of Video amp ImageProcessing and Network Security vol 14 no 2 pp 1ndash8 2014
[6] H Ramezani F K Nia and M J S Zadeh ldquoA novel securesteganography in contourlet domainrdquo International Journal ofTrends in Economics Management amp Technology vol 2 no 5pp 27ndash29 2013
[7] K I Alsaif and M M Saalih ldquoText embedding based oncontourlet transformation coefficientsrdquo International Journal ofInformation Technology and Business Management vol 12 no 1pp 49ndash56 2013
[8] M Juneja and P S Sandhu ldquoAn improved LSB based steganog-raphy technique for RGB color imagesrdquo International Journalof Computer and Communication Engineering vol 2 no 4 pp513ndash517 2013
[9] C G Soumi J George and J Stephen ldquoGenetic algorithmbased mosaic image steganography for enhanced securityrdquoInternational Journal on Signal and Image Processing vol 5 no1 pp 15ndash26 2014
[10] J KMandal andDDas ldquoColour image steganography based onpixel value differencing in spatial domainrdquo International Journalof Information Sciences and Techniques vol 2 no 4 2012
[11] S Arora and S Anand ldquoA new approach for image steganogra-phy using edge detectionmethodrdquo International Journal of Inno-vative Research in Computer and Communication Engineeringvol 1 no 3 pp 626ndash629 2013
[12] P R Rudramath and M R Madki ldquoImproved BPCS steganog-raphy based novel approach for data embeddingrdquo InternationalJournal of Engineering and Innovative Technology vol 1 no 3pp 156ndash159 2012
[13] E Kawaguchi and R O Eason ldquoPrinciple and applications ofBPCS-steganographyrdquo inMultimedia Systems and Applicationsvol 3528 of Proceedings of SPIE pp 464ndash473 1998
[14] S S Khaire and S L Nalbalwar ldquoReview steganographymdashbit plane complexity segmentation (BPCS) techniquerdquo Interna-tional Journal of Engineering Science and Technology vol 2 no9 pp 4860ndash4868 2010
[15] H Liu and H D Yuan ldquoModified algorithm of bit-plane com-plexity segmentation steganography based on preprocessingrdquoJournal of Computer Applications vol 32 no 1 pp 89ndash91 2012
Advances in Multimedia 7
[16] A Al-Ataby and F Al-Naima ldquoA modified high capacity imagesteganography technique based on wavelet transformrdquo TheInternational Arab Journal of Information Technology vol 7 no4 pp 358ndash364 2010
[17] X Z Zhang and S Wang ldquoStatistical analysis against spatialBPCS steganographyrdquo Journal of Computer-Aided Design ampComputer Graphics vol 17 no 7 pp 1625ndash1629 2005
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
Advances in Multimedia 5
Original image
(a)
BPCS steganography
(b)
Improved BPCS steganography
(c)
Original image
(d)
BPCS steganography
(e)
Improved BPCS steganography
(f)
Figure 5 Comparison of BPCS and improved BPCS
In order to resist BPCS statistical analysis the basic prin-ciple of improved BPCS steganography algorithm adopteddifferent bit-planewith different complexity which the higheris the complexity smaller is The threshold value of bit-planecomplexity is computed in the same way as in Section 33 (seeTable 2)
4 Improved BPCS Steganography Algorithm
(1) Convert the cover image and the secret image frompure binary code (PBC) into canonical Gray code(CGC)
(2) Calculate complexity measures 120572 120573 and 120574 for eachblock of each bit-plane of cover image and secretimage
(3) Perform conjugation operation on the ldquosimplerdquo orldquoinformativerdquo blocks of the secret image
(4) In order to contradict statistical steganalysis thethreshold value 120579 is calculated
(5) Perform embedding operation to embed secret imagein cover image
(6) Convert the embedded image from CGC to PBC
5 Experiment
In this paper the experiment is done with MATLAB 7 Theblock size is 8 times 8 and the peak signal-to-noise ratio (PSNR)and capacity are used to evaluate the quality of the coverimage after embedding
PSNR = 20log10
255
radic(1119873)sum119873minus1
119894=0(1198681015840
119894minus 119868119894)
2
(16)
119868119894is the value of a pixel in the original image and 119868
1015840
119894is the
value of the same pixel after embedding respectively119873 is thenumber of pixels
Capacity means the number of bits that could be used forembedding
This paper adopts three different images as samples Thesize of image is 512 times 512
From Table 1 it can be shown that though capacity isalmost the same between BPCS and the proposed methodthe PSNR value is much different It also can be concludedthat the proposed approach is better in PSNR value andcapacity than existing techniques
As shown in Figure 4 ℎ0 ℎ1 and ℎ
2are the histograms of
original image BPCS and improved BPCS
6 Advances in Multimedia
Table 1 Comparison of PSNR value and capacity with differentmethods
ImageLena Baboon Jet
Capacitybit
BPCS (120572 = 04) 951783 984782 942457Proposed method 954140 982769 943296Mandal and Das [10] 145787 144916 145648Juneja and Sandhu [8] 561345 830546 505658LSB 467004 720785 463758
PSNRdb
BPCS (120572 = 04) 4286 4085 4137Proposed method 4571 4276 4439Mandal and Das [10] 4226 3844 4260Juneja and Sandhu [8] 4459 3636 4250LSB 4101 3399 3938
Table 2 Threshold values 120573 120574 and 120579 of each bit-plane
119894 0 1 2 3 4 5 6 7120573119905
0632 0583 0534 0485 0436 0387 0 0120574119905
0394 0351 0308 0265 0222 0179 0 0120579119894
0562 0448 0334 0220 0106 0 0 0
The PSNR of the image obtained by BPCS (Figure 5(b))and improved BPCS (Figure 5(c)) was 4571 dB and 4286 dBFigures 5(d) 5(e) and 5(f) show the part areas of the imagesin Figures 5(a) 5(b) and 5(c) respectively It can be foundthat there is deterioration in the image obtained by BPCS(Figure 5(e)) but not in the image obtained by improvedBPCS (Figure 5(f))The experiment also shows that improvedBPCS can not only embed more data but also keep the imagequality higher In a word improved BPCS is superior to BPCSin steganography
6 Conclusion
It is shown that the final embedded image with the proposedmethod seems to be the same as the original image andlocates noise-like regions in a cover image more exactly Italso shows that the algorithm in this paper can withstandstatistics analysis while the way of BPCS cannot Experimentsalso prove that the proposed approach is better in PSNR valueand capacity than existing techniques
In this paper some parameters and thresholds are gotby experiment How to determine these values automaticallyneeds to be researched in the future
Conflict of Interests
The author declares that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
This research was financially supported by a grant from theNational Natural Science Foundation of China (no 61473329)
and the Special Research Foundation of the Fujian Province(Grant no JK2013062) and was also supported by FuqingBranch of Fujian Normal University of China (Grant nosKY2014028 and KY2014029)
References
[1] S Channalli and A Jadhav ldquoSteganography An art of hidingdatardquo International Journal on Computer Science and Engineer-ing vol 1 no 3 pp 137ndash141 2009
[2] I Singh S Khullar and S C Laroiya ldquoDFT based imageenhancement and steganographyrdquo International Journal ofComputer Science and Communication Engineering vol 2 no1 pp 5ndash7 2013
[3] G Kaur and A Kochhar ldquoA steganography implementationbased on LSB amp DCTrdquo International Journal for Science andEmerging Technologies with Latest Trends vol 4 no 1 pp 35ndash412012
[4] T Narasimmalou and R A Joseph ldquoRobust discrete wavelettransform based steganographyrdquo International Journal of PowerControl Signal and Computation vol 4 no 2 pp 102ndash108 2012
[5] N A Abu P W Adi and O Mohd ldquoRobust digital imagesteganography within coefficient difference on integer haarwavelet transformrdquo International Journal of Video amp ImageProcessing and Network Security vol 14 no 2 pp 1ndash8 2014
[6] H Ramezani F K Nia and M J S Zadeh ldquoA novel securesteganography in contourlet domainrdquo International Journal ofTrends in Economics Management amp Technology vol 2 no 5pp 27ndash29 2013
[7] K I Alsaif and M M Saalih ldquoText embedding based oncontourlet transformation coefficientsrdquo International Journal ofInformation Technology and Business Management vol 12 no 1pp 49ndash56 2013
[8] M Juneja and P S Sandhu ldquoAn improved LSB based steganog-raphy technique for RGB color imagesrdquo International Journalof Computer and Communication Engineering vol 2 no 4 pp513ndash517 2013
[9] C G Soumi J George and J Stephen ldquoGenetic algorithmbased mosaic image steganography for enhanced securityrdquoInternational Journal on Signal and Image Processing vol 5 no1 pp 15ndash26 2014
[10] J KMandal andDDas ldquoColour image steganography based onpixel value differencing in spatial domainrdquo International Journalof Information Sciences and Techniques vol 2 no 4 2012
[11] S Arora and S Anand ldquoA new approach for image steganogra-phy using edge detectionmethodrdquo International Journal of Inno-vative Research in Computer and Communication Engineeringvol 1 no 3 pp 626ndash629 2013
[12] P R Rudramath and M R Madki ldquoImproved BPCS steganog-raphy based novel approach for data embeddingrdquo InternationalJournal of Engineering and Innovative Technology vol 1 no 3pp 156ndash159 2012
[13] E Kawaguchi and R O Eason ldquoPrinciple and applications ofBPCS-steganographyrdquo inMultimedia Systems and Applicationsvol 3528 of Proceedings of SPIE pp 464ndash473 1998
[14] S S Khaire and S L Nalbalwar ldquoReview steganographymdashbit plane complexity segmentation (BPCS) techniquerdquo Interna-tional Journal of Engineering Science and Technology vol 2 no9 pp 4860ndash4868 2010
[15] H Liu and H D Yuan ldquoModified algorithm of bit-plane com-plexity segmentation steganography based on preprocessingrdquoJournal of Computer Applications vol 32 no 1 pp 89ndash91 2012
Advances in Multimedia 7
[16] A Al-Ataby and F Al-Naima ldquoA modified high capacity imagesteganography technique based on wavelet transformrdquo TheInternational Arab Journal of Information Technology vol 7 no4 pp 358ndash364 2010
[17] X Z Zhang and S Wang ldquoStatistical analysis against spatialBPCS steganographyrdquo Journal of Computer-Aided Design ampComputer Graphics vol 17 no 7 pp 1625ndash1629 2005
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
6 Advances in Multimedia
Table 1 Comparison of PSNR value and capacity with differentmethods
ImageLena Baboon Jet
Capacitybit
BPCS (120572 = 04) 951783 984782 942457Proposed method 954140 982769 943296Mandal and Das [10] 145787 144916 145648Juneja and Sandhu [8] 561345 830546 505658LSB 467004 720785 463758
PSNRdb
BPCS (120572 = 04) 4286 4085 4137Proposed method 4571 4276 4439Mandal and Das [10] 4226 3844 4260Juneja and Sandhu [8] 4459 3636 4250LSB 4101 3399 3938
Table 2 Threshold values 120573 120574 and 120579 of each bit-plane
119894 0 1 2 3 4 5 6 7120573119905
0632 0583 0534 0485 0436 0387 0 0120574119905
0394 0351 0308 0265 0222 0179 0 0120579119894
0562 0448 0334 0220 0106 0 0 0
The PSNR of the image obtained by BPCS (Figure 5(b))and improved BPCS (Figure 5(c)) was 4571 dB and 4286 dBFigures 5(d) 5(e) and 5(f) show the part areas of the imagesin Figures 5(a) 5(b) and 5(c) respectively It can be foundthat there is deterioration in the image obtained by BPCS(Figure 5(e)) but not in the image obtained by improvedBPCS (Figure 5(f))The experiment also shows that improvedBPCS can not only embed more data but also keep the imagequality higher In a word improved BPCS is superior to BPCSin steganography
6 Conclusion
It is shown that the final embedded image with the proposedmethod seems to be the same as the original image andlocates noise-like regions in a cover image more exactly Italso shows that the algorithm in this paper can withstandstatistics analysis while the way of BPCS cannot Experimentsalso prove that the proposed approach is better in PSNR valueand capacity than existing techniques
In this paper some parameters and thresholds are gotby experiment How to determine these values automaticallyneeds to be researched in the future
Conflict of Interests
The author declares that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
This research was financially supported by a grant from theNational Natural Science Foundation of China (no 61473329)
and the Special Research Foundation of the Fujian Province(Grant no JK2013062) and was also supported by FuqingBranch of Fujian Normal University of China (Grant nosKY2014028 and KY2014029)
References
[1] S Channalli and A Jadhav ldquoSteganography An art of hidingdatardquo International Journal on Computer Science and Engineer-ing vol 1 no 3 pp 137ndash141 2009
[2] I Singh S Khullar and S C Laroiya ldquoDFT based imageenhancement and steganographyrdquo International Journal ofComputer Science and Communication Engineering vol 2 no1 pp 5ndash7 2013
[3] G Kaur and A Kochhar ldquoA steganography implementationbased on LSB amp DCTrdquo International Journal for Science andEmerging Technologies with Latest Trends vol 4 no 1 pp 35ndash412012
[4] T Narasimmalou and R A Joseph ldquoRobust discrete wavelettransform based steganographyrdquo International Journal of PowerControl Signal and Computation vol 4 no 2 pp 102ndash108 2012
[5] N A Abu P W Adi and O Mohd ldquoRobust digital imagesteganography within coefficient difference on integer haarwavelet transformrdquo International Journal of Video amp ImageProcessing and Network Security vol 14 no 2 pp 1ndash8 2014
[6] H Ramezani F K Nia and M J S Zadeh ldquoA novel securesteganography in contourlet domainrdquo International Journal ofTrends in Economics Management amp Technology vol 2 no 5pp 27ndash29 2013
[7] K I Alsaif and M M Saalih ldquoText embedding based oncontourlet transformation coefficientsrdquo International Journal ofInformation Technology and Business Management vol 12 no 1pp 49ndash56 2013
[8] M Juneja and P S Sandhu ldquoAn improved LSB based steganog-raphy technique for RGB color imagesrdquo International Journalof Computer and Communication Engineering vol 2 no 4 pp513ndash517 2013
[9] C G Soumi J George and J Stephen ldquoGenetic algorithmbased mosaic image steganography for enhanced securityrdquoInternational Journal on Signal and Image Processing vol 5 no1 pp 15ndash26 2014
[10] J KMandal andDDas ldquoColour image steganography based onpixel value differencing in spatial domainrdquo International Journalof Information Sciences and Techniques vol 2 no 4 2012
[11] S Arora and S Anand ldquoA new approach for image steganogra-phy using edge detectionmethodrdquo International Journal of Inno-vative Research in Computer and Communication Engineeringvol 1 no 3 pp 626ndash629 2013
[12] P R Rudramath and M R Madki ldquoImproved BPCS steganog-raphy based novel approach for data embeddingrdquo InternationalJournal of Engineering and Innovative Technology vol 1 no 3pp 156ndash159 2012
[13] E Kawaguchi and R O Eason ldquoPrinciple and applications ofBPCS-steganographyrdquo inMultimedia Systems and Applicationsvol 3528 of Proceedings of SPIE pp 464ndash473 1998
[14] S S Khaire and S L Nalbalwar ldquoReview steganographymdashbit plane complexity segmentation (BPCS) techniquerdquo Interna-tional Journal of Engineering Science and Technology vol 2 no9 pp 4860ndash4868 2010
[15] H Liu and H D Yuan ldquoModified algorithm of bit-plane com-plexity segmentation steganography based on preprocessingrdquoJournal of Computer Applications vol 32 no 1 pp 89ndash91 2012
Advances in Multimedia 7
[16] A Al-Ataby and F Al-Naima ldquoA modified high capacity imagesteganography technique based on wavelet transformrdquo TheInternational Arab Journal of Information Technology vol 7 no4 pp 358ndash364 2010
[17] X Z Zhang and S Wang ldquoStatistical analysis against spatialBPCS steganographyrdquo Journal of Computer-Aided Design ampComputer Graphics vol 17 no 7 pp 1625ndash1629 2005
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
Advances in Multimedia 7
[16] A Al-Ataby and F Al-Naima ldquoA modified high capacity imagesteganography technique based on wavelet transformrdquo TheInternational Arab Journal of Information Technology vol 7 no4 pp 358ndash364 2010
[17] X Z Zhang and S Wang ldquoStatistical analysis against spatialBPCS steganographyrdquo Journal of Computer-Aided Design ampComputer Graphics vol 17 no 7 pp 1625ndash1629 2005
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
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