R ESEARCH ARTICLE

doi: 10.2306/scienceasia1513-1874.2013.39.423ScienceAsia 39 (2013): 423–435

Grid-line watermarking: A novel method for creating ahigh-performance text-image watermarkWiyada Yawai∗, Nualsawat Hiransakolwong

Department of Computer Science, Faculty of Science, King Mongkut’s Institute of Technology Ladkrabang,Chalongkrung Road, Ladkrabang, Bangkok 10520 Thailand

∗Corresponding author, e-mail: wyd.yawai@gmail.comReceived 4 Jun 2012

Accepted 18 Jan 2013

ABSTRACT: This study aims to discover an effective method to watermark any text image in any language. The ultimategoal of this work is to generate invisible and more robust watermarks, increase the hiding capacity, and identify any changein the original text. Generally these are the limitations of most text-image watermarking methods. Using a grid of horizontaland vertical lines is one of the most effective methods to overcome these limitations. These grid lines run across text-image character-skeleton lines to finely mark and detect watermarks on these line-character intersection points; there is oneintersection for one specific zero watermark pixel. Each intersection point is defined by one hiding bit of the watermarksuch that the more reference horizontal and vertical lines of grid pattern are plotted, the more points of intersection areobtained. This approach increases the hiding-bit capacity to watermark embedding, which is a disadvantage shared by manytext-image watermarking methods. In addition, if all positions of all intersection points are collected, these points will actas the identity reference points of all original characters to verify their integrities after they are modified.

KEYWORDS: digital watermarking, text image, integrity, cross ratio, collinear points, grid lines, zero watermark

INTRODUCTION

State of problem

Currently, creating and embedding watermarks in atext image faces many limitations, particularly re-duced robustness against a variety of attacks and areduced capacity to hide a large volume of watermarkdata due to the nature of the content, language andtext, which is mostly written in black. This approachprovides a small variation space to hide the largevolume of invisible watermark data, hence makingthe data easy to observe and destroy by simple at-tacks. Many researchers have proposed algorithmsto hide large amounts of watermark data in a textimage. However, these algorithms only apply tocertain languages, only achieve a small hiding-bit datacapacity and are not strong enough to survive attacks.Thus increasing the capacity for watermark hidingand robustness are the main goals of watermarkingapplications for storing the copyrighted data of alldigital media and text documents. Unfortunately,these features are the most difficult aspects to addressfor black and white text images. In fact, if watermarksare made to be absolutely invisible and imperviousto any attack or tampering, these difficulties willbe doubled. For the latter, even though we maybe able to detect our own copyright by extracting

information using intact redundant watermarks, wehave not necessarily obtained our original text image.In contrast, a copyright on a modified text image isachieved instead. This issue is the greatest challengefor text-image watermarking research.

Existing research

According to the various types of host media, digitalwatermarking may be classified into the followingfour categories: image watermarking, video water-marking, audio watermarking and text watermark-ing. The principles of image watermarking, videowatermarking and audio watermarking are similar inthat they make use of the redundant information oftheir host media, but text watermarking techniquesare different from those of non-text watermarking.Moreover, text watermarking algorithms cannot easilysatisfy the requirements of transparency (i.e., invisibil-ity or imperceptibility) and robustness1.

Most watermarking studies have concluded thattext watermarking is the most difficult type ofsteganography, largely due to the relative lack ofredundant information in a text file when comparedwith a picture or a sound file2. One reason that textsteganography is difficult is that text contains littleredundancy compared to other media. Another reasonis that humans are sensitive to abnormal text3.

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Text files present various challenges for copyrightprotection. Any text transformation should preservethe meaning, fluency, grammar, writing style andvalue of the text. Short documents have a lowcapacity for watermark embedding and are relativelydifficult to protect. Text watermarking algorithmsare also dependent on the text size, language, rules,grammar, conventions and writing style. In the past,text watermarking methods based on text images,synonyms, pre-supposition, syntactic-trees, nouns andverbs, word and sentences, acronyms and type errors4.

The watermarking of text falls under two do-mains: (1) text-image watermarking and (2) naturallanguage watermarking. The aim of both of these wa-termarking systems is the embedding of informationby modifying the original data in a discrete mannersuch that the modifications are imperceptible andthe embedded information is impervious to possibleattacks. In image text watermarking, this goal isachieved by exploiting the redundancy in images andthe limitations of the human visual system. However,language has a discrete and syntactical nature thatmakes such techniques more difficult to apply5.

Our survey revealed that most of the existingstudies are focused on watermarking an electronic textor document file, rather than a text image, in onespecified language, as opposed to multiple languages,and stress the watermark embedding technique overwatermark robustness, not the integrity verification.These existing text watermarking studies can be cat-egorized into three methods as follows (see Table 1).

First method: Watermark embedding withtext document physical layout/pattern/structure rear-rangement. Specific examples of this method includethe shifting of lines4, 6, 7 and words, particularly thebinding of word spacing, word shift coding or wordclassification8, 9. This method has some disadvan-tages. For example, the line shifting technique of Lowet al6 would be weakly robust for a document passingthrough document processing.

Second method: Embedding text watermark bytext character/letter feature modification. For exam-ple, Du et al11 proposed a text digital watermarkingalgorithm based on a slight change in text colour;watermarks were embedded after changing the low4 bits of the RGB colour components of the characters.However, this study tested only the robustness ofthis algorithm against word deletion and modification.Brassil, et al10 used letter adjusting by reducing orincreasing the length of letters, such as increasing thelengths of the letters b, d, or h. The limitation of thisprocess is that the hidden data will be weakly robustfor a document passing through document processing.

Third method: Watermarking with semanticschemes or word/vowel substitution and text structure.Topkara et al13 developed a technique for embeddingsecret data without changing the meaning of the textafter replacing words in the text with synonyms. Thismethod reduces the quality of a document, and a largesynonym dictionary is necessary. Samphaiboon et al3

proposed a steganographic scheme for electronic Thaiplain text documents that exploits redundancies fora particular vowel in the standard Thai characterset. However, this method can be used with onlyThai text documents, and these watermarks are easilydestroyed after re-editing with a word processingprogram. Meanwhile, structural schemes of text wa-termarking use text structures to embed watermarkswithout modifying any original text, so called thezero watermarking14, 15. For instance, the existence ofdouble letter (aa-zz) in the text is used to watermarkthe copyright data. However, the structural algorithmsare not applicable to all types of text documents andthe algorithm use an alphabetical watermark12.

Research objectives

This study aims to discover a novel method to solvethe previously stated problems and to eliminate theweakest points of the currently used methods. Indoing so, the cross-ratio theory will be applied withintersections of the virtual reference grid lines andcharacter-skeleton lines to build an effective water-mark performance including robustness against pos-sible attacks, hiding-bit data capacity, imperceptivity,multi-language text image applying and text integrityverification. Thus the five objectives of this work arethe following:

(1) To find a new method to effectively embedwatermarks in any text image in any language.

(2) To make watermarks absolutely invisible ordifficult to detect.

(3) To obtain a higher hiding-bit data capacity.(4) To make the watermark more robust to possi-

ble attacks.(5) To identify any change in an original text

image and verify its integrity.

MATHEMATICAL METHODS FOR TEXTWATERMARKING

The two mathematical methods applied in this studyare the cross-ratio theory to build marking robustnessand detect the embedded zero watermark location andthe matching percentage method to verify embeddedzero watermarks and the integrity of the text.

The cross ratio is a basic invariance in projectivegeometry (i.e., all other projective invariances can be

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Table 1 Performance comparison of the existing watermarking method.

Method Technique Applied to Imperceptivity Language Approx. Capacity Robustness

first Text line shifting 4, 6, 7 Digital text Visible Multi- 30 bits/A4 page Weakly robust todocument, language document processingtext image or OCR(300–400 dpi)

Inter-word space shifting Digital text Visible Multi- 720 bits/A4 page Weakly robust to(to left - right) 8, 9 document, language document processing

text image or OCRsecond Point shifting of letter Text image Visible English 600 bits/A4 page Weakly robust to

‘i’ & ‘j’ 5 brightness and noisesignal adding

Adjusting character Digital text Visible Chinese 650 bits/A4 page Weakly robust tosize 1 document, document processing

text image or OCRCharacter peak point Text image Visible Persian 900 bits/A4 page Weakly robust todistinction 2 and brightness and noise

Arabic signal addingReduce/increase length Text image Visible English 4000 bits Weakly robust toof letter for feature /A4 page document processingcoding 10 or OCRChanging the low 4 bits Digital Invisible Chinese 40 000 bits Robust to deletionof RGB colour plain text /A4 page modification attackof characters 11 etc.

third Existence of double Digital Invisible English 50 bits/A4 page Not applicable toletter (aa–zz) in plain text all types of textthe text 12 documentParticular vowel creating Digital Invisible Thai 58 bits/A4 page Very weakly robustsequent changing 3 plain text to document

processingSubstitute words in Digital Invisible English 870 bits/A4 page Very weakly robustthe text by synonyms 13 plain text to document

processing

derived from it). Let A, B, C and D be four collinearpoints (three or more points; A, B, C, are said tobe collinear if they lie on a single straight line16).The cross ratio is the ‘double ratio’ which is givenby A,B;C,D = AC.BD/BC.AD. Based on afundamental theory, any homography preserves thecross ratio. Thus central projection, linear scaling,skewing, rotation, and translation preserve the crossratio17.

The matching percentage MPW is the percentageof the amount of the watermark embedded position,which exactly matches the watermark detected po-sition, one-to-one, based on the total amount of thewatermark embedded positions:

MPW =

∑Dwm(xi, yi)∑Pip(xi, yi)

× 100%.

Dwm is the detected watermark at position (xi, yi)and Pip is the prominent intersection points at position(xi, yi) which were selected to embed the watermark.

A NOVEL TECHNIQUE FOR DIGITALLYWATERMARKING TEXT IMAGES

Three steps for arranging the zero watermarks ona text image exist. The first step is to define thezero watermark embedding positions, for hiding secretdata, and its hiding capacity, with certain intersectionpoints where either horizontal or vertical grid lines, orboth of grid lines run across the text-character skeletonlines. The second step is to match the cross-ratioreference points on specific line intersection pointswith zero watermarking positions to easily track thezero watermarking points after a text image has beenattacked. The last step is to detect the specific zero

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Fig. 1 Eleven grid-line intersection layers of main and 2-pixel-shifted horizontal (H), vertical (V) and double (H &V) lines for embedding a large volume of watermark secretdata in each layer.

watermarked points and verify the integrity of the textwith the matching percentage method.

Line CaCb runs from an origin point (Ca) to adestination point (Cb), where a = 1, 2, 3, 4 andb = 1, 2, 3, 4 and Cr = ((CA/CD)/(BA/BD)) =(CA/CD)(BD/BA), R = ((AC/CD)/Cr) andBA = (AD × R)/(1 + R). Q is the distance ofBA : AD, which is equal to BA : DA.

Embedding Scheme: Create watermarkembedding positions and hiding-bit capacity withmultiple layers of grid-line intersections

The points for embedding zero watermarks and in-creasing the watermark hiding-bit capacity can beachieved by effectively created using multiple layers

of grid-line intersection of the main and shifted hor-izontal (H), vertical (V) and double (H & V) linesthat cross one another under a specific pattern, asin Fig. 1. Each layer is created by following theprocedure below.

Step 1: Construct the first, second and third layers ofmain, lower-shifted and upper-shifted horizontalgrid-line intersections by identifying and drawingthe first reference horizontal lines for the topborders of the first text line on each text imagepage. Draw the second main, lower-shifted andupper-shifted horizontal lines next to the firstmain, lower-shifted and upper-shifted horizontallines, the third and the rest until the last text line,at 3-pixel intervals, which is a suitable gap for ageometric variation buffer, as found in a previousexperiment, on the first, second and third layers,respectively.

Step 2: Create the fourth, fifth and sixth layers ofmain, left-shifted and right-shifted vertical grid-line intersections by identifying the right and leftborders of the text body on a text image, anddraw the first main, left-shifted and right-shiftedvertical lines along the left border of the fourth,fifth and sixth layers, respectively. Draw thesecond main, left-shifted and right-shifted verticallines, the third and the rest until reaching the endof the right border, using a 3-pixel interval.

Step 3: Create the seventh, eighth, ninth, tenth andeleventh layers by drawing both the main, upper-left, upper-right, lower-left and lower-right shiftedhorizontal (H) and vertical (V) lines, respectively,e.g., the main double (H & V) lines, using a 3-pixel interval, on the same text-image page tocreate the seventh main double-line intersectionlayer, where both the main horizontal and verti-cal lines run across either text-character skeletonlines or blank areas and create intersection pointsfor embedding watermark data bits.

Step 4: Select each prominent grid-line intersectionposition in each main or shifted line that runsacross each text-character skeleton line and blankarea, as shown in Fig. 2, for the embeddingwatermark data. If the tone of the grid-lineintersection is less than the 245 greyscale levelor is a gray/black tone, its bit value is specifiedas 1. If its greyscale value is higher than the 245greyscale level or is a white tone, the bit valueis specified as 0. These selected prominent whiteand black positions are specified as the markingpoints of the zero digital watermark bits for hidingsecret data, and these points represent the main

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Fig. 2 The technique for transforming intersection points(xi, yi), running across text-character skeleton lines andblank areas with (a) horizontal (H), (b) vertical (V), (c) dou-ble (H & V), (a-1) lower-shifted horizontal, (b-1) left-shiftedvertical and (c-1) upper-left shifted double H & V lines, intobinary or bit, 0, 1, data to create the watermark-hiding bitcapacity for each layer.

hiding-bit capacities of the first, second, third,fourth, fifth, sixth, seventh, eighth, ninth, tenthand eleventh grid-line layers.

Step 5: If more hiding-bit capacity is required in eachlayer, the horizontal, vertical and double (H &V) lines can be shifted to new positions, andnew shifted horizontal, vertical and double (H &V) lines can be drawn to create new intersectionpoints that run across the text-character skeletonlines and blank areas. These shifts create moreintersection points at new positions to providemore hiding-bit capacity. Moreover, these newintersection points, surrounding their main orig-inal intersection points, can be used as crosscheckpoints to identify the exact point of origin andverify any changes to the integrity of the textimage.

These 11 grid-line intersection layers are designedto generate relatively large watermark hiding-bit ca-pacities for each text-image page. In our experiments,

Fig. 3 The six steps for embedding and detecting watermarksecret data, i.e., ‘COPYRIGHT’, which is transformed intobinary data (0, 1) before being embedded into the selectedreference line intersection points. The watermark is thenagain detected by checking whether the embedded intersec-tion positions exist.

we tested 35 pages of Thai, English, Chinese andArabic language scanned text images by using ourMATLAB program to draw automatically grid linesand counting all grid-line intersection points in alllayers, where one intersection point encodes one bit ofwatermark hiding data. This way, it can automaticallywatermark a lot of scanned text papers.

Transform the watermarked secret data intobinary and embedding positions

To transform the watermarked secret data into binaryand embedding positions can be done as describedbelow.

Step 1: Define the secret data to be embedded as thewatermark on each layer.

Step 2: Transform the watermarked secret data, i.e.,the word COPYRIGHT, into binary, i.e., the bits0 and 1, as illustrated in Figs. 3 and 4.

Step 3: Select the major intersection points or theirlocations, (xi, yi), corresponding to specifics bits(1 or 0) of the transformed secret data to assignthe watermark embedding positions.

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Fig. 4 The technique for embedding secret watermark databits (0, 1) into the selected grid-line intersection positions.

Embed watermarks under cross-ratio directing

The text watermarking process shown in Figs. 3 (Step3) and 4 is based on two subprocesses. The firstone is to match the cross-ratio reference points withzero watermark marking positions to easily track thezero watermark marking points after a text image hasbeen attacked. The second one is to detect the zerowatermarks and verify the integrity of the text withthe matching percentage method.

To apply the cross ratio to digital image water-marking, three reference points are required. In thissection, a method for deriving such reference pointsis described. Let us start by considering the markingpart. The method is described algorithmically, step bystep, as follows.

Step 1: Predefine the set of cross-ratio values to be

(a)[1ex](b) (c) (d)

Fig. 5 (a) Notation of double reference grid-line intersectionof vertical and horizontal lines on a text image. (b), (c) and(d) Notation of the horizontal, vertical and horizontal &vertical grid lines, in the upper parts, respectively, intersecttext character skeleton lines and blank areas used to embedor mark zero watermark pattern bits in the text image.

used in subsequent steps.Step 2: Find the image centre using the line intersec-

tion formula18 (two diagonal lines of the image)described by the following equations: xc =xt/xb, yc = yt/yb;

xt =

∣∣∣∣a x1 − x4

b x3 − x2

∣∣∣∣ , xb =

∣∣∣∣x1 − x4 y1 − y4x3 − x2 y3 − y2

∣∣∣∣ ,yt =

∣∣∣∣a y1 − y4b y3 − y2

∣∣∣∣ , yb =

∣∣∣∣x1 − x4 y1 − y4x3 − x2 y3 − y2

∣∣∣∣ ,where

a =

∣∣∣∣x1 y1x4 y4

∣∣∣∣ , b =

∣∣∣∣x3 y3x2 y2

∣∣∣∣ .In addition, (xi, yi) is the coordinate of the

point Ci, i = 1, . . ., 4 (Fig. 5a). From the aboveequations, xc is the x-axis value of the point Dc

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of a two-line intersection; C1C4 intersects C2C3,and yc is the y-axis value of the same point. ||denotes a determinant operator (Fig. 5a).

Step 3: Find each of the primary-level watermarkmarking points (TCr1 and TCr2) on theright diagonal line (Fig. 5a), using: xTCri =x2 +Q(x3 − x2), yTCri = y2 +Q(y3 − y2),where (xTCri , yTCri), i = 1, 2, A = C2, B =TCri , C = Dc and D = C3. These points canbe identified after using two corner points of theleft diagonal line (C2 and C3), in combinationwith the image centre point Dc and the predefinedcross-ratio values (Cr).

Step 4: For each pair of coordinates((x1, y1)(x2, y2)), find an intersection, (xi, yi),of the grid line of each level drawn across(x3, y3)(x4, y4) by applying the followingequations: xi = xt/xb, yi = yt/yb;

xt =

∣∣∣∣a x1 − x2

b x3 − x4

∣∣∣∣ , xb =

∣∣∣∣x1 − x2 y1 − y2x3 − x4 y3 − y4

∣∣∣∣ ,yt =

∣∣∣∣a y1 − y2b y3 − y4

∣∣∣∣ , yb =

∣∣∣∣x1 − x2 y1 − y2x3 − x4 y3 − y4

∣∣∣∣ ,where

a =

∣∣∣∣x1 y1x2 y2

∣∣∣∣ , b =

∣∣∣∣x3 y3x4 y4

∣∣∣∣ .For the line C4TCr2, find a left-handhorizontal grid-line intersection, (xHCrL,yHCrL), of this line drawn across the lineC1C3, where (x1, y1) = (xTCr2 , yTCr2),(x2, y2) = (xC4

, yC4), (x3, y3) = (xC3

, yC3),

and (x4, y4) = (xC1, yC1

). For the line C1TCr1,find a right-hand horizontal grid-line intersection,(xHCrR, yHCrR), of this line drawn acrossthe C4C2, where (x1, y1) = (xC1 , yC1),(x2, y2) = (xTCr1 , yTCr1), (x3, y3) =(xC4

, yC4), (x4, y4) = (xC2

, yC2).

Step 5: Find each of the secondary level water-mark marking points (HLk) on the left-handhorizontal grid line, as described by xHLk

=x1 +Q(x3 − x1), yHLk

= y1 +Q(y3 − y1),where A = C1, B = (xHLk

, yHLk), C = HCrL

and D = C3. These points can be identifiedafter using two corner points of the line C1C3, incombination with the image left-hand horizontalcentral point (HCrL) and the predefined cross-ratio values (Cr).

Step 6: Find each of the secondary level watermarkmarking points (HRk) on the right-hand hor-izontal grid line, as described by xHRk

=x2 +Q(x4 − x2), yHRk

= y2 +Q(y4 − y2),

where A = C2, B = (xHRk, yHRk

), C =HCrR and D = C4. These points can beidentified after using two corner points of theline C2C4 in combination with the image right-hand horizontal central point (HCrR) and thepredefined cross-ratio values (Cr).

Step 7: Repeat Steps 4–6 to find other secondary-level watermark marking points (Ehi,k) at theprominent horizontal grid-line intersection posi-tions where each horizontal grid line runs acrossa text-character line or blank area and its toneis lower or higher than the 245 greyscale levelwhen more effective embedding and detection isrequired. These prominent positions are specifiedas the embedded bits, one or zero, of invisiblezero watermarks (Ehi,k) when their tones arelower than 245 or higher than 245, respectively,as indicated in Fig. 5b.

Step 8: Find each of the secondary level water-mark marking points (V Ui) on the upper-partvertical grid line, as described by xV Ui

=x1 +Q(x2 − x1), yV Ui

= y1 +Q(y2 − y1),where A = C1, B = (xV Ui , yV Ui), C = V CrUand D = C2. These points can be identifiedafter using two corner points of the line C1C2

in combination with the image upper-part verticalcentral point (V CrU ) and the predefined cross-ratio values (Cr).

Step 9: Find each of the secondary level water-mark marking points (V Di) on the lower-partvertical grid line, as described by xV Di

=x3 +Q(x4 − x3), yV Di

= y3 +Q(y4 − y3),where A = C3, B = (xV Di

, yV Di), C = V CrD

and D = C4. These points can be identifiedafter using two corner points of the line C3C4

in combination with the image lower-part verticalcentral point (V CrD) and the predefined cross-ratio values (Cr).

Step 10: For each vertical line, detect other promi-nent intersection positions of each vertical linethat runs across each text-character skeleton lineor blank area, where its tone is lower or higherthan the 245 greyscale level when more effec-tive embedding and detection is needed. Thesedetected prominent positions are specified as theembedded bits, one or zero, of invisible zerowatermarks (Evi,k) when their tones are loweror higher than 245, respectively, as indicated inFig. 5c.

Step 11: For each pair (xV Ui, yV Ui

) and(xV Di , yV Di), find an intersection,(xEvhi,k

, yEvhi,k), of the grid line of each level

drawn across (xHLi, yHLi

) and (xHRi, yHRi

)

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430 ScienceAsia 39 (2013)

after applying the followings equations:xEvhi,k

= xt/xb, yEvhi,k= yt/yb;

xt =

∣∣∣∣a x1 − x2

b x3 − x4

∣∣∣∣ , xb =

∣∣∣∣x1 − x2 y1 − y2x3 − x4 y3 − y4

∣∣∣∣ ,yt =

∣∣∣∣a y1 − y2b y3 − y4

∣∣∣∣ , yb =

∣∣∣∣x1 − x2 y1 − y2x3 − x4 y3 − y4

∣∣∣∣ ,where

a =

∣∣∣∣x1 y1x2 y2

∣∣∣∣ , b =

∣∣∣∣x3 y3x4 y4

∣∣∣∣ .For each pair (xV Ui

, yV Ui) and (xV Di

, yV Di),

find an intersection, (xEvhi,k, yEvhi,k

), of thegrid line of each level drawn across (xHLk

, yHLk)

and (xHRk, yHRk

), (x1, y1) = (xV Ui, yV Ui

),(x2, y2) = (xV Di

, yV Di), (x3, y3) =

(xHLk, yHLk

), (x4, y4) = (xHRk, yHRk

).Step 12: Determine the positions of each prominent

intersection of the double (H & V) lines that runacross each text-character line or blank area withtones lower or higher than the 245 greyscale levelwhen more effective embedding and detection isneeded. These prominent positions are specifiedas the embedded bits, one or zero, of invisible zerowatermarks (Evhi,k) when their tones are loweror higher than 245, respectively, as indicated inFig. 5d.

DETECTION SCHEME

Detection of watermark bits at the embeddedintersection points

To detect a watermark in a text image, first a water-marked paper has to be scanned and then its scannedtext-image output would be passed through our MAT-LAB program to detect its four image corner points.This detection can be achieved using any existingcorner detection algorithm. After detecting the fourcorner points, watermark marking points must beidentified. Each selected intersection point used toembed a watermark data bit can be calculated using amethod similar to that in the marking stage, as in Step4 of Fig. 3. By extracting and transforming the pixelbit values corresponding to these watermark markingpoints, a watermark can be detected using any existingwatermark detector. We adopt the basic matching per-centage method described in ”The matching percent-age” to compare these detected watermark positionswith their originally marked positions, one-by-one,until all specified positions are covered. We predefinethe set of cross-ratio values to be used in subsequentsteps.

Translation of watermark bits into the embeddedsecret data

After obtaining the extracted bit values of each layer,the next step is translating them into characters orembedded watermark secret data, as shown in Fig. 3(Step 5), and comparing them with the original em-bedded secret data. These comparisons are madeblock-by-block or layer-by-layer by checking thematching percentage.

Verification of text image integrity and changedetection

If the extracted data do not match the original embed-ded secret data, the unmatched bits lead us to theirpositions, (xi, yi), where we can check for modifica-tions, i.e., new text additions, reordering or deletions,as in Fig. 3 (Step 6). This helps the copyright ownersto trace any changes in their original text images. Thistechnique can also prevent copyright owners fromaccepting ownership of counterfeit text images thatembedding their verified secret watermark data. Thissituation may occur when the owners have applied aphysical watermarking technique like inter-word orline shifting, which some hackers may observe andknow how to manipulate by not altering the watermarkembedding positions. The correctly watermarkeddata are still detected and extracted, although theyhave been modified. This false acceptance wouldrarely occur using this new technique because all textcharacters are finely marked, stored and detected, so asmall bit change can eventually be identified.

EXPERIMENTS

Text images tested in multiple languages

We tested a set of 35 text images of greyscale multi-language text, including Thai, English, Chinese andArabic, with a size of 1240× 1754 pixels and aresolution of 150 dpi.

Testing the number of grid-line intersection layersfor imperceptivity and hiding-bit capacity

To obtain a high hiding-bit capacity for watermarking,each page of these greyscale-text images, we firstvirtually created the three main grid-line intersectionlayers, containing the main horizontal (H), vertical(V) and double (H & V) grid-line layers, as in Fig. 1.Each layer was then derived into eight 2-pixel-shiftedgrid-line intersection layers, including two shiftedhorizontal grid-line intersection layers, two shiftedvertical grid-line intersection layers and four shifteddouble (H & V) grid-line intersection layers. There

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are 11 total grid-line intersection layers for testing andcontributing the watermark hiding-bit capacity.

Watermark secret data embedding test

In this test, the prominent grid-line intersection pointscreated on each grid-line intersection layer describedabove, i.e., the points where the main and shiftedsingle horizontal, single vertical or double (H & V)lines run across the text-character skeleton lines andblank areas, were selected to embed the watermarksecret data bits. One selected prominent intersectionpoint represented one embedded data bit with cross-ratio controlling. Here, the word ‘COPYRIGHT’was encoded in the secret data bits to be embeddedin each grid-line intersection layer, as in Figs. 3 and4. Here, each instance of ‘COPYRIGHT’ requires72 bits: 010 000 110 100 111 101 010 000 010 110010 101 001 001 001 001 010 001 110 100 100 001 010100.

Testing for robustness against manipulationattacks on embedded watermark detection andintegrity verification

The grid-line cross-ratio watermarking robustnessagainst some possible attacks such as compression,sharpness, noise signal adding, shearing and rotatingand three text-group manipulations, including ten textgroups with addition, reordering and deletion manip-ulations, were used to test the embedded watermarkdetection and integrity verification scheme. The per-centage of matching was used as the criterion for thistesting.

RESULTS

The first result was from the embedding and detectiontesting of the controlled watermarked text imageswithout manipulation attack. Comparing the plot-ted watermark positioning pattern and extracting theembedded secret data, this method obtained a 100%matching percentage and a zero error percentage. Foreach of the following tests, we briefly describe theresult according to our objectives.

Language-independent testing

The results of this test clearly demonstrated that thismultiple grid-line intersection layer technique can beapplied to all Thai, English, Chinese and Arabic textimages. This technique requires only the grid-lineintersection points where the virtual reference linesrun across text-character skeleton lines and blankareas to embed watermark data bits without physi-cally modifying the original text images or using anyspecial technique for any specific language. Each

tested language affects only the number of intersectionpoints created due to the different character skeletonline structures.

Imperceptivity testing

Virtual lines are drawn according to these grid linesonly in the program process, and they are not drawn oneach text image page. Thus it would not immediatelybe possible to observe the lines and watermarks afterthese text images were completely watermark embed-ded, even with many secret data bits. Conversely,the pages look exactly like their original text imagesbefore watermarking.

Hiding-bit capacity testing

The testing revealed that the 7th, 8th, 9th, 10th and11th layers of the main and shifted double (H &V) line intersections generated a maximum hiding-bit capacity volume of 240 608 bits/layer/A4 page oftext image (see Table 2), whereas the first, second,third, 4th, 5th and 6th layers of the main and shiftedsingle horizontal and vertical line intersections gener-ated a maximum hiding-bit capacity volume of only77 334 bits/layer/A4 page of text image.

With respect to each language, this experimentdemonstrated that Arabic text generates high hiding-bit capacities under both single horizontal and verticalgrid-line intersection layers (see Table 2). The testedlanguages did not affect their hiding-bit capacitiesunder the double (H & V) grid-line intersection layer,as all intersection points, whether running across text-character skeleton lines or blank areas, were counted.

The above results describe the hiding-bit capac-ities generated by eleven main and shifted grid-lineintersection layers, as in Table 2. To count the totalhiding-bit capacity per text image page using thistechnique, we would count all the hiding-bit capacitiesin all layers. Arabic script generates the maximumhiding-bit capacity per page: up to 1 545 738 bits/A4page of text image can be hidden.

Robustness, copyright ownership and integrityverification testing

After the manipulation tests, with compression, sharp-ness, noise signal adding, shearing and rotating andten text groups added, reordered and deleted, eachlayer could be used to detect and extract the embeddedwatermark and verify its integrity or modification,measured as an error percentage, as shown their higherperformance results in Figs. 6, 7, 8, 9 and 10 andsummarized in the robustness testing results below,comparing with the performance of the existing text-image watermarking methods summarized in Table 1.

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Table 2 The hiding-bit capacity volume of each language and layer.

Layer No. Type of grid-line intersection Hiding-bit capacity (bits)Thai English Chinese Arabic

1 Main Horizontal (H) 64 278 65 125 61 614 75 3292 Lower-shifted H. 67 244 64 327 61 541 75 8023 Upper-shifted H. 61 829 64 457 61 893 77 3344 Main Vertical (V) 32 234 32 449 30 829 38 0535 Left-shifted V. 32 188 32 204 30 978 38 0486 Right-shifted V. 32 253 32 302 30 717 38 1327 Main Double (H & V) 240 608 240 608 240 608 240 6088 Upper-left Shifted Double (H & V) 240 608 240 608 240 608 240 6089 Upper-right Shifted Double (H & V) 240 608 240 608 240 608 240 60810 Lower-left Shifted Double (H & V) 240 608 240 608 240 608 240 60811 Lower-right Shifted Double (H & V) 240 608 240 608 240 608 240 608

Total 1 431 237 1 493 904 1 480 612 1 545 738

5 10 15 20 25 30 350

0.5

1

1.5

2

2.5

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3.5

4

4.5

5

Rotation angle (degrees)

Err

or (

%)

Average errorMinimum errorMaximum error

Fig. 6 Error level 4% of single horizontal reference grid-line zero watermark detection after rotation angles rangingfrom 5–35°.

Figs. 6, 7 and the robustness testing resultsbelow show the remarkable performance of the cross-ratio theory in strengthening the robustness of text-image watermarking against some possible geometricdistortion attacks, including shearing and rotating andmanipulating attacks, such as noise signal adding,compression and sharpness. These attacks are theresult of applying the single horizontal reference grid-line watermarking method while controlling the wa-termark data embedding and detecting using the crossratio of four collinear points on each the horizontalreference grid lines that run across each text characterskeleton line. The results show that these attacksdid not significantly affect watermark detection. Therotating attack, for example, affected the embeddedzero watermark by detecting only 0.15–4% of errors.This error indicates that the matching percentage of

1 2.5 5 10 12.5 150

0.02

0.04

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0.1

0.12

Noise addition (%)

Err

or

(%)

Average error

Minimum error

Maximum error

Fig. 7 Low error (%) level of single horizontal referencegrid-line zero watermark detection after adding salt andpepper noise signals.

watermark detected is generally > 96%.The robustness testing results are as follows.

Sharpness: 0–100%; low error (0.5–2%) - JPEGcompression: 0–100%; low error (0–2%) - blur: 3x3- 15x15 mask size; low error (0.083%) - contrasting:10–50%; no error - noise signal adding:pepper; lowerror (0–0.11%) - shearing:x right shifted 0–5%; lowerror (0–2%) - rotation: 5–35°; error level: 0.15–4%- scaling: 10–130%; error level: 0–6% - text addingdetection: by double (H & V) lines; error Level:0.008–0.067% - text deleting detection: by horizontal(H) lines; error Level: 0.25–1% - text reorderingdetection:by horizontal (H) lines; error Level: 0.18–0.82%.

Figs. 8, 9, 10 and the robustness testing resultsabove present the ability of horizontal and vertical gridline intersection with cross-ratio theory to strengthen

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1 2 3 4 5 6 7 8 9 100

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Number of added text groups

Err

or (

%)

Average horizontal line errorAverage vertical line errorAverage horizontal line intersect vertical line error

Fig. 8 The percent of errors, under the double referencegrid-line intersection watermarking method and text addingmanipulations attack, were more effectively detected thanwhen using single reference grid-line watermarking.

2 4 6 8 100

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Number of reordered text groups

Err

or

(%)

Average horizontal line errorAverage vertical line errorAverage horizontal line intersect vertical line error

Fig. 9 The single reference grid-line intersection water-marking of the horizontal grid line was mostly suitable fordetecting percent of errors after being attacked with textreordering.

the original text verification performance against threemanipulations: text adding, reordering and deleting.The results indicate that the horizontal and verticalgrid line intersection method with cross-ratio theorycan still sensitively detect even a few changes, such asadding, reordering and deleting one to ten text groups,for which the observed maximum errors are 0.067%,0.82% and 1% respectively.

These results show that the percent error of detec-tion depends on the exact positions selected for zero

1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1

1.2

1.41.5

Number of deleted text groups

Err

or

(%)

Average horizontal line errorAverage vertical line errorAverage horizontal line intersect vertical line error

Fig. 10 The single reference grid-line intersection water-marking of the horizontal grid line was mostly suitable fordetecting the percent of errors after being attacked with textdeletion.

watermark embedding or data collecting. For exam-ple, the intersected positions of the double reference,vertical and horizontal grid lines, which run acrossor intersect one another on the blank area of a textimage, could be used as blank reference points to trapany added text from the text adding attack. This iswhy the double reference grid lines are more effectivethan the single reference grid line, (Fig. 8). However,for text reordering and deleting, the horizontal singlereference grid-line intersection watermarking methodperforms better than the other techniques (Figs. 9 and10).

DISCUSSION

The results of all of the experiments mentioned aboveprove that the novel method, which applies the cross-ratio theory and grid-line intersection of the virtualhorizontal and vertical grid lines that run across text-character skeleton lines and blank areas, could lead toa major breakthrough in overcoming all of the majorlimitations of text image watermarking (see Table 1and the robustness testing results above). Thus all fiveobjectives of this study have been achieved, and thefollowing discussions have been drawn.

(1) This method can be applied to watermark anytext image in any language with the grid-line intersec-tion points of either horizontal or vertical referencegrid-lines or both grid lines virtually intersected thetext-character skeleton lines or blank areas, which areused as the embedding points of watermark data. Thisnovel technique does not depend on the type of text

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images used or language, even though these experi-ments tested for only four languages: Thai, English,Chinese and Arabic. Nevertheless, the method canbe applied to other languages because this novel tech-nique does not require any text alignment or syntacticor semantic structural modifications.

(2) This method can be applied to make water-marks absolutely invisible or unobservable becauseno actual colour-tone pixel of a watermark is em-bedded and no physical text structure is modified,as mentioned above. The intersection points usedas reference embedding points of watermark data arevirtual horizontal and vertical grid lines that run acrossall text-character skeleton lines and blank areas butcannot be seen. These grid-line intersection pointscan be used as the reference positions of data bitsto combine them as the embedding patterns of secretwatermark data, zero watermarks, and can also beused as reference points to later detect and interpretthe combined secret data bits.

(3) This method is able to generate a high hiding-bit data capacity of up to 1 545 738 bits/A4 pageof Arabic text image, which safely and effectivelycreates bit combination redundancy to embed a largeamount of secret watermark data on one A4 page ofthe text image. This enormous hiding-bit capacityis obtained from the number of intersection points ofvirtual reference grid lines and text-character skeletonlines, on each layer, where one intersection point isdefined as one bit of secret watermark data. Hence,an increase in the number of grid-line intersectionpoints indicates an increase in the amount of hidingbits obtained. However, our results show that themost suitable interval between each line is 3 pixelsbecause the method requires some clear space forbuffering and tolerating variations under the attackof some geometric distortions. Nevertheless, wecan still dramatically increase the hiding-bit capacityby adding more watermarking grid-line intersectionplains such as 12 layers of diagonal grid-lines.

(4) This method can be applied to make water-marks more robust to or more likely to survive manypossible attacks, especially geometric distortions suchas scaling, shearing and rotating. Specifying thecross ratio of four collinear points for each referencehorizontal and vertical grid line and each diagonalline is first set to control and direct the embeddingposition of each secret watermark data bit (Fig. 4),which makes it easy to refer to the embedded position,even though they may be attacked. Figs. 6, 7 and therobustness testing results above confirm that the crossratio could generate a watermark that is reasonablyrobust to both geometric distortions, as mentioned

earlier, and graphical manipulations, such as sharp-ness, compression, blur, contrast and the addition ofnoise signals. This robustness is due to the cross-ratio marking pattern being able to precisely lock allwatermark embedding positions to easily and exactlydetect and retrieve them after they have been attacked.

(5) This method can be applied to simultaneouslyidentify any change in the original text image and ver-ify the image’s integrity during watermark detection.This performance is directly related to the increase inthe hiding-bit capacity. This relationship indicates thatthe more hiding bits or intersection points we have,the more checkpoints for available for verifying theimage’s integrity. Using this technique, the horizontaland vertical grid line intersection points on the text-character skeleton lines can be used as check pointsfor text deleting and reordering, while their grid-lineintersection points on the blank area can be used as thecheckpoints for text adding. From these experiments,horizontal and vertical grid-line intersections wereshown to be very effective in verifying the additionof new text (Fig. 8). This result is because theintersected positions of these double reference gridlines, vertical and horizontal grid lines, run across orintersect one another in the blank area of a text image,which is used as a reference point and can trap anyadditional text introduced by a text adding attack andprecisely pinpoint the exact positions of text additions.Meanwhile, for text reordering and deleting, a singlehorizontal reference grid line would perform betterthan a single vertical reference grid line and doublereference grid lines (Figs. 9 and 10).

Thus the cross-ratio theory and grid-line intersec-tion are able to effectively generate high text-imagewatermarking performance.

CONCLUSIONS

Although this study involved many experiments, manyways remain to apply and benefit from these grid-line intersections and cross-ratio theory, especiallyfor obtaining greater hiding-bit capacity by applyingfurther pattern grid-line intersections including diag-onal, sine-curve and unique barcode lines. The realidentity of the original text image is also proven bydirecting the watermark embedding points using therelative vector along the grid-line intersection pointsto more efficiently verify original text modificationsand enhance the robustness of this scheme. Twodouble reference grid-line intersection points may beselected, and lines may be virtually drawn to connectthem, storing their relative vector data to form aunique web or mesh network of virtual watermarkingpoints, locking them using the cross-ratio format for

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each text document image. This unique mesh networkacts as a watermarking pattern fingerprint that caneffectively be applied for both integrity verificationand watermark retrieval after an attack. Our futurework will focus on these advanced techniques.

Acknowledgements: This study was performed under thecomputer science doctoral study program of the Departmentof Computer Science, Faculty of Science, King Mongkut’sInstitute of Technology Ladkrabang, Bangkok, Thailand,where all lecturers and officers supported us in completingthis study.

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