nested image steganography scheme using qr-barcode...

NQ

WND3STE

JNI4K

1

TmbdTdibm

ocitCasc

oWapd

0

Optical Engineering 48�5�, 057004 �May 2009�

O

ested image steganography scheme usingR-barcode technique

en-Yuan Chenational Chin-Yi University of Technologyepartment of Electronic Engineering5 Lane 215ection 1, Chung-Shan Roadaiping City, Taichung County 411 Taiwain-mail: [email protected]

ing-Wein Wangational Kaohsiung University of Applied Sciences

nstitute of Photonics and Communications15 Chien Kung Roadaohsiung 807 Taiwan

Abstract. In this paper, QR bar code and image processing techniquesare used to construct a nested steganography scheme. There are twotypes of secret data �lossless and lossy� embedded into a cover image.The lossless data is text that is first encoded by the QR barcode; its datadoes not have any distortion when comparing with the extracted dataand original data. The lossy data is a kind of image; the face image issuitable for our case. Because the extracted text is lossless, the errorcorrection rate of QR encoding must be carefully designed. We found a25% error correction rate is suitable for our goal. In image embedding,because it can sustain minor perceptible distortion, we thus adopted thelower nibble byte discard of the face image to reduce the secret data.When the image is extracted, we use a median filter to filter out the noiseand obtain a smoother image quality. After simulation, it is evident thatour scheme is robust to JPEG attacks. Compared to other steganogra-phy schemes, our proposed method has three advantages: �i� the nestedscheme is an enhanced security system never previously developed; �ii�our scheme can conceal lossless and lossy secret data into a coverimage simultaneously; and �iii� the QR barcode used as secret data canwidely extend this method’s application fields. © 2009 Society of Photo-OpticalInstrumentation Engineers. �DOI: 10.1117/1.3126646�

Subject terms: QR code; steganography; error correction; JPEG; imageprocessing.

Paper 080983R received Dec. 18, 2008; revised manuscript received Mar. 5,2009; accepted for publication Mar. 18, 2009; published online May 6, 2009.

Introduction

he barcode1–6 patterns pasted on commodities in super-arkets are European Article Number Barcodes �EAN-13

arcode�, also known as international barcodes. They wereeveloped by 12 industrialized countries in Europe in 1977.he barcode has several advantages. It is not as easily wornown as magnetic cards, it has very low rate of error, and its very cheap and easy to fabricate. Recently, barcodes haveecome used for the identification of commodities and areassively used in various markets.The two-dimensional barcode is currently being devel-

ped to increase the encoding space. The QR code, Maxi-ode, Datamatrix code, and pdf417 are widely implementedn daily life. In fact, the QR code7 has six desirable fea-ures: high capacity encoding of data, small printout size,hinese/Japanese �kanji and kana� capability, dirt and dam-ge resistance, readability from any direction in 360°, and atructure append feature. Specifically, its capacity can en-ode 7089 numeric characters for numeric data.

As the usage of the Internet becomes extremely popular,nline data exchange becomes extremely easy as well.hile people gain great benefits through the Internet, they

lso encounter many serious problems with it. Piracy ofrivate data is one good example. In order to remedy therawback of data communication through the Internet,

091-3286/2009/$25.00 © 2009 SPIE

ptical Engineering 057004-

many data security techniques have been proposed. How-ever, steganography is a suitable method used for protect-ing nontext data.

In general, there are two types of data security schemes,i.e., steganography and watermarking.8,9 Steganographytechniques10–12 conceal secret data into a cover image sothat other people cannot find it. Liu et al.13 used a stegana-laysis technique on the basis of histogram analysis on thewavelet coefficients to detect the very existence of waveletdomain information hiding. In the approach, the secret mes-sage was embedded via quantizating wavelet coefficients.

Fig. 1 The pattern of a QR barcode.

May 2009/Vol. 48�5�1

Fwdptmrsadwdcaaci

c

Fab

Chen and Wang: Nested image steganography scheme using QR-barcode technique

O

urthermore, by means of histogram analysis of subbandavelet coefficients from stego-images, several statisticaliscrepancies are obtained. Finally, the discrepancies areassed through fast Fourier transform to generate a quanti-ative criterion and then used to determine whether a secretessage has been embedded. Wang14 proposed a steganog-

aphy scheme that used modulus operation to incorporateecret data into a cover image in which half of the size andquarter of the size of the chosen host-image were used toemonstrate that the secret image can be totally embeddedhile preserving a high-quality image. Based on an embed-ed zerotree wavelet compression method and bit-planeomplexity segmentation skill, Spaulding et al.15 proposedsteganography scheme. From his experimental results, it

chieved a large embedding capacity of around 255 of theompressed image size with little noticeable degradation inmage quality.

Noore et al.16 present an approach for embedding un-ompressed image in a standard pdf417 �2-D barcode17,18�

Fig. 2 The structure of QR barcode.

ig. 3 Hiding text data into barcode: �a� the text data I, �b� gener-ted QR barcode I, �c� the text data II, and �d� the generated QRarcode II.


using a blind digital watermarking. The text is encoded inthe 2-D barcode with error correction, while the face imageis watermarked in the encoded 2-D barcode. In this paper, a2-D QR barcode �as in Fig. 1� shows a QR barcode fromwhich image-processing techniques were used to constructa nested steganography scheme. Then the QR barcode pat-tern and a face image serve as the secret data which areembedded into the cover image without degradation ofquality. The remainder of this paper is organized as fol-lows: In Section 2, the introduction to the QR barcode ispresented. The secret data embedding algorithm is de-scribed in Section 3. The secret data extracting algorithm isillustrated in Section 4. Empirical results are presented inSection 5. Section 6 concludes this paper.

2 QR Barcode

2.1 QR Barcode StructureThe QR barcode7 is a 2-D symbology developed by DensoWave in 1994. The code contains information in both the x-and y-axis, whereas traditional barcodes contain data in onedirection only. The QR barcode therefore stores a consider-ably greater volume of information than a normal bar code.The main structure of the QR barcode is shown in Fig. 2.The outer range is the quiet zone. The upper-left, upper-right, and left-bottom square areas are used for positiondetection and pattern separators for positioning. There are

Fig. 4 A bigger QR barcode pattern: �a� the encoding text and �b�the corresponding QR barcode pattern with size 279�279.

Fig. 5 Flow chart of nested image steganography embedding.

May 2009/Vol. 48�5�2

staiccmf

2TJu3QdsFwba2

3ItepTleaBi

Fcrp

F


O

ix smaller squares which are the alignment patterns. Addi-ionally, the main area, which is colored gray, is the kernelrea of data and error correction code. The QR code’s sizes decided by determining a symbol version based on dataapacity, character type �numeric, alphanumeric, Japaneseharacters, etc.�, and error correction level, and by setting aodule size based on the target printer’s or scanner’s per-

ormance level.

.2 QR Barcode Encodinghe QR barcode can be generated by English, Chinese, orapanese text. The text data shown in Figs. 3�a� and 3�c� aresed for input. The generated QR barcode, as Figs. 3�b� and�d� show, are the QR encoding output. Because the size ofR barcode is varied by encoding input data, larger textata will generate a larger size QR code. In Fig. 3�b�, theize of the QR barcode is 159�159 and it is encoding fromig. 3�a�. It is a part of the secret data in our scheme andill be embedded into a cover image. Figure 4 shows aigger QR barcode pattern. Figure 4�a� is the encoding text,nd Fig. 4�b� is the corresponding QR barcode pattern of79�279 size.

Embedding Algorithmn the embedding process, the working flow is divided intohree parts as Fig. 5 shows. The upper path is the text datancoding flow; it first transfers the text into a 2-D barcodeattern �QR code� and then embeds it into the cover image.he middle path is the face image embedding flow. The

ower path is preparing the cover image for secret datambedding. At the beginning, the text data is used generateQR barcode pattern at the barcode encoding �BCE� stage.ecause the QR barcode is a regular format of data blocks,

t can be eliminated for decreasing the secret data. Thus, the

ig. 6 Useful QR barcode pattern: �a� the original generated bar-ode pattern, �b� the resulting image after discarding the outerange, �c� the useless area of the barcode pattern, and �d� the testattern that can be used as the secret data.

ig. 7 2-D reduction of a QR barcode to one dimension bit stream.


regular area moving �RAM� is designed for moving theredundancy. For enhancing the security, a chaotic mecha-nism �CM� provides hashing of the secret data. Further-more, the dimension reduction �DR� stage is used to map2-D barcode patterns into 1-D data for convenient secretdata embedding. A lower nibble byte discarding �LNBD�stage is used to keep the kernel data of the face image andeliminate the unimportant message. However, the key and apseudorandom number sequence �PRNS� generator areused to generate the pseudorandom sequences. On thecover image, a discrete cosine transform �DCT� is adoptedto convert the image from the spatial domain to frequencydomain for robustness. Simultaneously, a block selection�BS� and coefficient selection �CS� are used to hash theorder, thereby increasing the security. Once all of these pro-cesses have been prepared, the secret bits embedding �SBE�step is used to hide the secret data into the cover image.After all the secret data is embedded, an inverse discretecosine transform �IDCT� returns the cover image to thespatial domain from the frequency domain. Then the secretdata embedding is complete.

3.1 Moving of the QR Barcode’s Regular AreaFor the data hiding scheme, the secret data must be a valu-able message. Because a barcode has a regular pattern, sev-eral areas are useless while it serves as the secret data. Theouter range is a useless white pattern and can be deleted.The regular format of the QR pattern shown in Fig. 6�c� isuseless and can be removed. In our approach, a RAM stageis implemented to eliminate the useless regions and de-crease the secret data. Figure 6 describes which regions areuseful or useless in the QR barcode pattern. Figure 6�a� isthe original generated barcode pattern; Fig. 6�b� shows theresulting image after discarding the white outer-range area.

Fig. 8 LNBD of an 8-bit grayscale image.

Fig. 9 Coefficients of the DCT block.

May 2009/Vol. 48�5�3

Fpu

3Iccpusps

m

i

wos

3Indtc


O

igure 6�c� shows the regular format area of the barcodeattern. Figure 6�d� displays the test pattern that can besed as the secret data; its size is 15,147 bits.

.2 Chaotic Mechanismn data hiding, we want the data to be chaotic becausehaotic data is not easy to attack. Therefore, it needs ahaotic mechanism to hash the output streams �ms�. In thisaper, a fast pseudorandom number-traversing method issed as the chaotic mechanism to permute the outputtreams �ms�. The relation between the bit sequence afterermutation and the bit sequence before permutation is pre-ented in the following formulas:

c�i� = m�i��, 1 � i, i� � F �1�

= permutation�i�� , �2�

here F is the length of the bit sequence. The permutationperation is accomplished by using Eq. �1� and the PN1equence.

.3 Dimension Reduction of QR Barcoden secret data embedding, we use a bit replacement tech-ique to embed the secret data into a cover image. Thus, aimension reduction �DR� of QR barcode is used to converthe secret data from two dimensions into bit streams foronvenient secret bit embedding. Figure 7 shows the sche-

Fig. 10 Ex

Fig. 11 QR barcode extracting and padding pro2-D format in an incomplete state, �b� the regumerging �a� and �b�, and �d� complete extracted


matic of a QR barcode pattern from two dimensions beingreduced to bit streams. However, having a bit stream isessential for secret bit embedding.

3.4 Lower Nibble Byte Discard (LNBD)For an 8-bit grayscale image, the high nibble byte repre-sents 88% of each pixel’s energy. In other words, the lownibble bytes possess only a little energy and can be omittedwithout noticeably effecting picture quality. In order to re-duce the secret data, we preserve the high nibble byte dataand discard the low nibble byte of the face image. After theLNBD, we have a 50% reduction of secret data for the faceimage. From the simulation results, we find the face imageis acceptable under the adopted LNBD strategy. Figure 8shows the schematic of an LNBD.

3.5 Block Selection and Coefficients SelectionDCT is used to transfer the image from the spatial domaininto the frequency domain. The coefficients of the DCTblock with size 8�8 are the values corresponding to theDCT basis. In the coefficients of the DCT block as shownin Fig. 8, the upper-left coefficient is the DC value, variesaccording to the luminance, and is not suitable for bit em-bedding. Where the coefficients in the upper-left corner arethe low-frequency bands, the energy is concentrated and issuitable for secret data concealing. The low-frequency bandcoefficients marked P1, P2, D1, and D2 of the DCT blockin Fig. 9 are suitable for use in our scheme. In our ap-

algorithm.

: �a� the extracted QR barcode rearranged intoa of a QR code, �c� the resulting image afterde with outer white area.

tracting

cedurelar arebarco

May 2009/Vol. 48�5�4

pie

3Tnwsrsmw�TiiemPboPBt

Fte


O

roach, the locations P1 and P2 are used to conceal the facemage. Simultaneously, the locations D1 and D2 are used tombed the QR barcode pattern.

.6 Secret Bit Embeddinghere are two types of secret data �text and image� whicheed to be embedded into a cover image. In our approach,e use the coefficients of the DCT block D1 and D2 for

ecret text, and P1 and P2 for secret image embedding,espectively. The locations of the D1, D2, P1, and P2 arehown in Fig. 9. In the embedding process, the bit replace-ent technique is used for secret data concealing. In fact,e select bit 4 and bit 5 of selected DCT coefficients

meaning D1, D2, P1, and P2� for secret bit replacement.here are two sizes �512�512 and 1024�512� of cover

mages used for simulation. When the size of the covermage is 512�512, we use two bits �bit 4 and bit 5� fromach selected DCT coefficients �D1 and D2� for the place-ent of secret text �meaning the QR pattern� embedding.1 and P2 are used for the placement of secret image em-edding. When the size of the cover image is 1024�512,nly bit 5 of the selected DCT coefficients �D1, D2, P1, and2� is used for the placement of secret data embedding.ecause 1024�512 is exactly twice the size of 512�512,

herefore only one bit is needed.

Table 1 The extracted error bits of the stego-

JPEGquality

Notcompressed

QRbarcode

Extractederror bits 72

After errorcorrection 0

100% textdecoding OK

Faceimage


ig. 12 MBC error correction of QR barcode pattern: �a� the ex-racted pattern I, �b� the resulting pattern after MBC of �a�, �c� thextracted pattern II, and �d� the resulting pattern after MBC of �c�.


4 Extracting Algorithm

In the extracting process, the inverse of the concealing pro-cess is used to extract the secret image. A flow chart of theextracting process is shown in Fig. 10. The stego-image R�

is transferred to R̂� by DCT. The locations are exactly thesame as were used in the embedding and were selected bymeans of the BS, CS, and the PN3, PN4 sequence with itsprivate key. Once the locations are determined, a bit cap-ture is used to extract the secret data. When the secret bitsare all extracted, they are immediately concatenated intotwo bit sequences �m1�� and �m2�� by a rearranging opera-tion. Then �m1�� is contracted by an inverse chaotic mecha-nism �ICM�. Furthermore, the regular area padding of theextracted QR code is used to reconstruct the complete QRcode. Then a QR decoder is used to extract the original text.Additionally, �m2�� is contracted by ICM. Following execu-tion, low nibble byte padding the recovered face image isobtained. Because the extracted face image includes noise,a median filter is used to filter out the noise and output anacceptable face image. The details of the secret data ex-tracting is described below.

F16 with PSNR=37.69 under JPEG attacks.

8 9 10 11 12

45 980 655 618 606

6 9 0 0 9

K OK OK OK OK

23 759 501 495 494

Fig. 13 The stego-image F16 under JPEG attacks: �a1�–�a6� arethe extracted QR barcode pattern; �b1�–�b6� are after-error correc-tion of �a1�–�a6�; �c1�–�c6� are the extracted face image; and �d1�–�d6� are the after median filter of �c1�–�c6�.

image

16

3

O

10

May 2009/Vol. 48�5�5

4InoaiimI

m

i

wpt

4BiTspmwFp


O

.1 ICMn the secret data embedding process, a fast pseudorandomumber-traversing method is used as the CM to permute theutput streams. The relationship between the bit sequencefter permutation and the bit sequence before permutations described by Eqs. �1� and �2�. In the secret data extract-ng, the inverse fast pseudorandom number-traversing

ethod is used as the ICM to repermute the output streams.t is given by Eqs. �3� and �4�,

�i�� = mc�i�, 1 � i, i� � F �3�

� = permutation�i� , �4�

here F is the length of the bit sequence. The re-ermutation operation uses Eq. �4� and is associated withhe PN1 sequence.

.2 Regular Area Padding of the QR Codeecause the regular area of QR code was not embedded

nto the cover image, the extracted data lacks that data.herefore, a regular area padding �RAP� of the QR codetage is needed to fit the format and recover its standardattern for QR decoding. During the embedding step, weoved away the necessary data of the QR format and outerhite area. Therefore, in extracting, we must pad that data.igure 11 shows the QR barcode extracting and paddingrocedure. Figure 11�a� denotes the extracted QR barcode

Table 2 The extracted error bits of the stego-im

JPEGquality

Notcompressed

QRbarcode




Faceimage


Table 3 The extracted error bits of the stego-im

JPEGquality

Notcompressed

QRbarcode




Faceimage



data restored into 2-D format in an incomplete state. Figure11�b� displays the regular area of a QR code which isneeded for padding. Figure 11�c� is the resulting image af-ter merging �a� and �b�. Finally, Fig. 11�d� shows a com-plete extracted barcode with outer white area. Its formatcan be accepted for QR decoding.

4.3 Error Bit Correction of the QR Barcode Patternby MBC

Since the QR barcode pattern consists of blocks with size3�3, we can correct some error bits by the majority bitcheck �MBC� technique. Figure 12 shows the error correc-tion schematic of the QR barcode by MBC. Figure 12�a� isthe extracted pattern I; there are five bits which are 0�black� and four bits 1 �white�. Figure 12�b� illustrates theresulting pattern after MBC is applied to Fig. 12�a�; weobtain nine bits with value 0. On the contrary, Fig. 12�c� isthe extracted pattern II; there are five bits belonging towhite and four bits belonging to black. Applying the MBCto Fig. 12�c�, we obtain nine bits belonging to white, as Fig.12�d� shows.

4.4 Low Nibble Byte Padding of the Face Image

In order to reduce the secret data, we discard the lowernibble byte data of the face image during the embeddingprocess. On the contrary, we need to pad the low nibble

ppers with PSNR=37.67 under JPEG attacks.

8 9 10 11 12

69 1345 728 931 877

8 0 0 9 0

K OK OK OK OK

47 865 725 833 841

aboon with PSNR=38.20 under JPEG attacks.

9 10 11 12

6 1574 1076 1276 1240

9 9 0 0

OK OK OK OK

5 1010 727 815 811

age Pe

18

1

O

11

age B

8

210

45

OK

119

May 2009/Vol. 48�5�6

bub

4TtpdlifE

F

wv

Fitc


O

yte data in the extracted face image. In our approach, wese the zero padding technique to restore the low nibbleyte data of the face image.

.5 Median Filterhe median filter, a nonlinear spatial filter, is a powerful

ool for removing outlying-type noise. It is not suitable forreserving the edges of an image. The filter mask simplyefines what pixels must be included in the median calcu-ation. The computation of the median filter starts by order-ng those n pixels defined by the filter mask, in the orderrom minimum to maximum value of the pixels, as given inq. �5�,

0 � F1 � F2 ¯ � Fn−2 � Fn−1, �5�

here F0 denotes the minimum and Fn−1 is the maximumalue of all the pixels in the filter calculation. The output of

Table 4 The extracted error bits of the stego-imattacks.

JPEGquality

Notcompressed

QRbarcode




Faceimage


ig. 14 The test images: �a� cover image X-ray of hand, �b� stego-mage of �a� with PSNR=36.34, �c� cover image X-ray of chest, �d�he stego-image of �c� with PSNR=36.39, �e� cover image X-ray ofranium, and �f� the stego-image of �e� with PSNR=36.21.


the median filter is the median of these values and is givenby

Fmed = �Fn/2 + Fn/2−1

2for even n

Fn/2 for odd n� . �6�

Typically, an odd number of filter elements is chosen toavoid the additional step in averaging the middle two pixelsof the order set when the number of elements is even. Inthis scheme, we select a mask with size 3�3 used to filterout the noise.

5 Experimental Results

Imperceptibility is an important factor in steganography. Inthis paper, we employ peak signal to noise ratio �PSNR� tomeasure the degree of transparency. The PSNR of Y isgiven by the following formula:

-ray of chest� with PSNR=36.39 under JPEG

8 9 10 11 12

67 1551 321 492 527

25 153 0 0 0

OK OK OK OK

55 643 241 393 388

Fig. 15 The stego-image X-ray of chest under JPEG attacks: �a1�–�a6� are the extracted QR barcode pattern; �b1�–�b6� are after errorcorrection of �a1�–�a6�; �c1�–�c6� are the extracted face image; and�d1�–�d6� are the after median filter of �c1�–�c6�.

age �X

31

20

X

2

May 2009/Vol. 48�5�7

P

wpio

��spwa1bcRgi

Fara


O

SNR = 10 log102552

1

N � N�i=0

N−1

�j=0

N−1

�X�i, j� − Y�i, j�2

, �7�

here X�i , j� is the grayscale value of the host image X atoint �i , j�, and Y�i , j� is the grayscale value of the stego-mage Y of size n=N�N at point �i , j�. We use the numberf error bits to indicate the extracted fidelity.

The cover images Lena �512�512�, Peppers �512512�, F16 �512�512�, Sailboat �512�512�, Baboon

512�512�, X-ray of hand �1024�512�, X-rays of chest1024�512�, and X-ray of cranium �512�512� are used inimulation for demonstrating the performance of the pro-osed scheme. The texts as shown in Figs. 3�a� and 3�c�ere first encoded by the QR barcode with size 159�159,

nd face images as shown in Figs. 13�d� and 15�d�, and Fig.6�d� with size 64�64 were used as the secret images. Thelock size used in the simulation was 8�8. The QR bar-ode pattern size was shrunk to 135�135 first, and then aAM stage was implemented to eliminate the useless re-ions and shrink the secret data. Finally, it was convertednto a bit stream �15147 bits� by dimensional reduction.

Table 5 The extracted error bits of the stego-imwith PSNR=36.23 under JPEG attacks.

JPEGquality

Notcompressed

QRbarcode




Faceimage


ig. 16 The stego-image sailboat under JPEG attacks: �a1�–�a6�re the extracted QR barcode pattern; �b1�–�b6� are after error cor-ection of �a1�–�a6�; �c1�–�c6� are the extracted NCUT logo image;nd �d1�–�d6� are the after median filter of �c1�–�c6�.


The number of the secret bits is equal to 15,147+64�64�4=31,531. The total number of blocks is equal to �512�512�÷ �8�8�=4096. Therefore, the number of coeffi-cients of each selected block is four, and each coefficientconceals 2 bits for embedding secret bits.

In order to demonstrate that our scheme can sustain in-terference on the Internet, we use JPEG compression toattack the stego-image. For example, the stego-image aftersecret data was concealed into the cover images Lena andSailboat are PSNR=37.76 and PSNR=37.97, respectively.Table 1 is the extracted error bits of the stego-image F16with PSNR=37.69 under JPEG attacks. In JPEG attacks,we use several quality degrees �no compression, 8, 9, 10,11, 12� for simulation. The simulation results are listed intwo parts �QR barcode and face image� in Table 1. In theQR barcode we listed three kinds of results: extracted errorbits, the error bits after error correction by MBC, andwhether it can be fully decoded. According to Table 1, wesee that the secret text can obtain perfect recovery underJPEG attacks. Although the extracted face image has noise,we can clearly identify it, as Fig. 13 shows. Likewise,Tables 2 and 3 are the test results of the still images Pep-

ailboat for the embedding image is NCUT logo

9 10 11 12

1 1559 1083 1229 1206

9 18 9 9

OK OK OK OK

1 1087 1123 1703 1739

Table 6 Comparison between the state-of-the-art steganalysismethod and the proposed method.

ItemState-of-the-artsteganalysis method

Proposedmethod

Embeddingcapacity

Large Large

Can sustainJPEG attacks

Yes Yes

Security High High

Nested structure No Yes

Include losslessand lossy secretdata

No Yes

Applicationrange

Median Wide �because ofthe 2-D barcode�

age s

8

194

36

OK

105

May 2009/Vol. 48�5�8

pma=o1ia

kosoi


O

ers and Baboon, respectively. Figure 14 shows the test onedical images. Figures 14�a� and 14�b� are the cover im-

ge X-ray of a hand and its stego-image with PSNR36.34. Figures 14�c� and 14�d� are the cover image X-rayf a chest and its stego-image with PSNR=36.39. Figures4�e� and 14�f� are the cover image X-ray of a cranium andts stego-image with PSNR=36.21. Figure 15 and Table 4re the test results of the medical images chest X-ray.

In order to demonstrate that the secret image can be anyind of image, we selected the National Chin-Yi Universityf Technology �NCUT� logo used for simulation. Table 5hows the simulation results. We see that the secret text canbtain perfect recovery under JPEG attacks. In image test-ng, the simulation results demonstrate that the effective-

Fig. 17 Curves comparing the number of erroradded attacks for the Lena, Sailboat, X-ray of c

Fig. 18 Curves comparing the number of erroattacks for the Lena, Sailboat, X-ray of chest, a


ness is the same as when the secret image is a face. Figure16 shows the detail patterns extracted from the cover imageunder JPEG attacks. From Fig. 16, we can clearly identifythe logo, although the extracted image has a little noise.According to the above experimental results, it is evidentthat our method is an effective steganography scheme.

Figure 17 shows the curves comparing the number oferror bits of the extracted QR barcode pattern under noise-added attacks for the Lena, Sailboat, X-ray of chest, andX-ray of cranium images. Although the number of error bitsis a little large, the secret text also 100% extracted underJPEG quality is in the range 9–12. Likewise, Fig. 18 showscurves comparing the number of error bits of the extractedface image under noise-added attacks for the Lena, Sail-

the extracted QR barcode pattern under noise-nd X-ray of cranium images.

f the extracted face image under noise-addeday of cranium images.

bits ofhest, a

r bits ond X-r

May 2009/Vol. 48�5�9

btcqbphtetiNsoas2

6ItotdacQcrcccrdipoiccs

R


O

oat, X-ray of chest, and X-ray of cranium images. Fromhe curves in Fig. 18, the extracted face images are alllearly identified. As Figs. 13 and 15 show, under JPEGuality is in the range 5–12. Table 6 is a comparison tableetween the state-of-the-art steganalysis method and theroposed method. For embedding capacity the two systemsave large concealing ability. It is likely that the two sys-ems can sustain JPEG attacks and have high security; how-ver, there are some different features which exist in thewo systems. First, our scheme is a nested structure whichs different from the state-of-the-art steganalysis method.ext, our scheme can embed lossless and lossy secret data

imultaneously, but the state-of-the-art steganalysis meth-ds embed only one of the two types at a time. Finally, thepplication range of the proposed scheme is wider thantate-of-the-art steganalysis, because our scheme includes a-D barcode that can be used in any field.

Conclusionsn this paper, we use QR barcode and image-processingechniques to propose a nested steganography scheme. Inur approach, two types of data �text and image� serve ashe secret data which are embedded into a cover image. Toemonstrate the robustness, a JPEG compression is used tottack the stego-image. However, the extracted text dataannot have any distortion compared to the original. Thus,R encoding and decoding must be carefully designed. In

onsidering extraction error, we use a 25% error correctionate of QR barcode; it has been shown to be suitable for ourases after experimental testing. Because the QR barcodeonsists of a small 3�3 block, we use a majority bit checkriterion to correct the error bits and obtain an effectiveesult. In image embedding, because it can sustain someistortion, we discard the lower nibble byte of the facemage to reduce the secret data. After image extraction, wead the lower nibble byte and use the median filter to filterut the noise. Results show that we obtain a smoother facemage. From the experimental results, it is evident thatombining the image-processing techniques and QR bar-ode can achieve an excellent nested steganographycheme, even when encountering JPEG attacks.

eferences

1. M. Y. Cheng and J. C. Chen, “Integrating barcode and GIS for moni-toring construction progress,” Autom. Constr. 11�1�, 23–33 �2002�.

2. J. A. Stewart and F. A. Short, “Time accuracy of a barcode system forrecording resuscitation events: Laboratory trials,” Resuscitation42�3�, 235–240 �1999�.

3. I. Y. Soon, C. K. Yeo, and Y. H. Sng, “Portable adapter for barcodescanners,” Microproc. Microsyst. 23�4�, 217–223 �1999�.

4. A. Collins, A. Zomorodian, G. Carlsson, and L. J. Guibas, “A bar-code shape descriptor for curve point cloud data,” Comput. Graph.28�6�, 881–894 �2004�.

5. D. E. Gilsinn, G. S. Cheok, and D. P. O’Leary, “Reconstructing im-ages of barcodes for construction site object recognition,” Autom.Constr. 13�1�, 21–35 �2004�.

ptical Engineering 057004-1

6. A. Noore, N. Tungala, and M. M. Houck, “Embedding biometricidentifiers in 2D barcodes for improved security,” Comput. Secur.23�8�, 679–686 �2004�.

7. See http://www.denso-wave.com/qrcode/qrfeature-e.html�.8. W. Y. Chen and C. C. Liu, “Multiple-watermarking scheme of the

European article number barcode using adaptive phase shift keyingtechnique,” Opt. Eng. 46�6�, 067002-1–12 �2007�.

9. C. C. Liu and W. Y. Chen, “Multiple-watermarking scheme for stillimages using discrete cosine transform and modified code divisionmultiple access techniques,” Opt. Eng. 45�7�, 1–10 �2006�.

10. R. J. Anderson and F. A. P. Petitcolas, “On the limits of steganogra-phy,” IEEE J. Sel. Areas Commun. 16�4�, 474–481 �1998�.

11. J. Fridrich, M. Goljan, and R. Du, “Detecting LSB steganography incolor and gray-scale images,” IEEE Trans. Multimedia 8�4�, 22–28�2001�.

12. B. T. Mcbride, G. L. Peterson, and S. C. Gustafson, “A new blindmethod for detecting novel steganography,” Digital Investigation2�1�, 50–70 �2005�.

13. S. Liu, H. Yao, and W. Gao, “Steganalysis of data hiding techniquesin wavelet domain,” in Proc. of the Int. Conf. on Information Tech-nology: Coding and Computing, p. 1–4, Comput. Coll., Harbin Inst.of Technol., China �2004�.

14. S. J. Wang, “Steganography of capacity required using modulo op-erator for embedding secret image,” AMC 164, 99–116 �2005�.

15. J. Spaulding, H. Noda, M. N. Shirazi, and E. Kawaguchi, “BPCSsteganography using EZW lossy compressed images,” Pattern Rec-ogn. Lett. 23�13�, 1579–1587 �2002�.

16. A. Noore, N. Tungala, and M. M. Houck, “Embedding biometricidentifiers in 2D barcodes for improved security,” Comput. Secur. 23,679–686 �2004�.

17. J. Z. Gao, L. Prakash, and R. Jagatesan, “Understanding 2D-BarCodetechnology and applications in M-Commerce, Design and implemen-tation of A 2D barcode processing solution,” in Proceedings of the31st Annual International Computer Software and Applications Con-ference, Vol. 2, pp. 49–56, IEEE Computer Society, Washington, DC�2007�.

18. T. Y. Liu, T. H. Tan, and Y. L. Chu, “2D barcode and augmentedreality supported English learning system,” Computer and Informa-tion Science, IEEE/ACIS Int. Conf., Vol. 6, pp. 5–10 �2007�.

Wen-Yuan Chen received his BS and MS inElectronic Engineering from National Tai-wan University of Science and Technologyin 1982 and 1984, respectively, and thePh.D. degree in Electrical Engineering fromNational Cheng Kung University in Tainan,Taiwan, in 2003. Since 2007 he has been aprofessor in the Department of ElectronicEngineering at National Chin-Yi Universityof Technology. His research interests in-clude digital signal processing, image com-

pression, pattern recognition, and watermarking.

Jing-Wein Wang received his BS and MSin Electronic Engineering from National Tai-wan University of Science and Technologyin 1986 and 1988, respectively, and the PhDdegree in Electrical Engineering from Na-tional Cheng Kung University, Taiwan, in1998. From 1992 to 2000, he was a princi-pal project leader at Equipment DesignCenter of Philips, Taiwan. In 2000 he joinedthe faculty of National Kaohsiung Universityof Applied Sciences, where he is currently

an associate professor at the Institute of Photonics and Communi-cations. His current research interests are combinatorial optimiza-tion, pattern recognition, and wavelets and their applications.

May 2009/Vol. 48�5�0

http://dx.doi.org/10.1016/S0926-5805(01)00043-7

http://dx.doi.org/10.1016/S0300-9572(99)00104-5

http://dx.doi.org/10.1016/S0141-9331(99)00027-7

http://dx.doi.org/10.1016/j.autcon.2003.08.003

http://dx.doi.org/10.1016/j.autcon.2003.08.003

http://dx.doi.org/10.1016/j.cose.2004.09.007

http://dx.doi.org/10.1117/1.2747544

http://dx.doi.org/10.1109/49.668971

http://dx.doi.org/10.1016/j.diin.2005.01.003

http://dx.doi.org/10.1016/S0167-8655(02)00122-8

http://dx.doi.org/10.1016/S0167-8655(02)00122-8

http://dx.doi.org/10.1016/j.cose.2004.09.007

nested image steganography scheme using qr-barcode...

Documents