IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 7, JULY 2013 1091

A Novel Reversible Data Hiding Scheme Basedon Two-Dimensional Difference-Histogram

ModificationXiaolong Li, Weiming Zhang, Xinlu Gui, and Bin Yang

Abstract—In this paper, based on two-dimensional difference-histogram modification, a novel reversible data hiding (RDH)scheme is proposed by using difference-pair-mapping (DPM).First, by considering each pixel-pair and its context, a sequenceconsisting of pairs of difference values is computed. Then, atwo-dimensional difference-histogram is generated by countingthe frequency of the resulting difference-pairs. Finally, reversibledata embedding is implemented according to a specifically de-signed DPM. Here, the DPM is an injective mapping defined ondifference-pairs. It is a natural extension of expansion embeddingand shifting techniques used in current histogram-based RDHmethods. By the proposed approach, compared with the conven-tional one-dimensional difference-histogram and one-dimensionalprediction-error-histogram-based RDH methods, the image re-dundancy can be better exploited and an improved embeddingperformance is achieved. Moreover, a pixel-pair-selection strategyis also adopted to priorly use the pixel-pairs located in smoothimage regions to embed data. This can further enhance the em-bedding performance. Experimental results demonstrate that theproposed scheme outperforms some state-of-the-art RDH works.

Index Terms—Difference-pair-mapping (DPM), histogrammodification, reversible data hiding (RDH), two-dimensionaldifference-histogram.

I. INTRODUCTION

R EVERSIBLE data hiding (RDH) aims to embed se-cret message into a cover image by slightly modifying

its pixel values, and, unlike conventional data hiding, theembedded message as well as the cover image should becompletely recovered from the marked content [1]–[3]. RDHis a special type of information hiding and its feasibility ismainly due to the lossless compressibility of natural images.The reversibility in RDH is quite desirable and helpful in somepractical applications such as medical image processing [4],

Manuscript received November 23, 2012; revised February 27, 2013 andApril 24, 2013; accepted April 24, 2013. Date of publication May 01, 2013;date of current version May 29, 2013. The work of W. Zhang was supportedby the funding of Science and Technology on Information Assurance Labora-tory under Grant KJ-13-02. The associate editor coordinating the review of thismanuscript and approving it for publication was Prof. C.-C. Jay Kuo.X. Li, X. Gui, and B. Yang are with the Institute of Computer Sci-

ence and Technology, Peking University, Beijing 100871, China (e-mail:lixiaolong@pku.edu.cn; guixinlu@pku.edu.cn; yang_bin@pku.edu.cn).W. Zhang is with the School of Information Science and Technology,

University of Science and Technology of China, Hefei 230026, China (e-mail:zwmshu@gmail.com).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TIFS.2013.2261062

[5], multimedia archive management [6], image trans-coding[7], and video error-concealment coding [8], etc. Generally, theperformance of a RDH scheme is evaluated by the capacity-dis-tortion behavior. For a required embedding capacity (EC), toobtain a good marked image quality, one expects to reduce theembedding distortion as much as possible.Many RDH methods have been proposed so far, e.g.,

the methods based on lossless compression [9]–[11], differ-ence expansion [12]–[15], histogram modification [16]–[19],prediction-error expansion [20]–[25], and integer transform[26]–[30], etc. Among them, the histogram-based ones haveattracted much attention. The histogram-based methods modifythe histogram in such a way that certain bins are shifted tocreate vacant space while some other bins are utilized to carrydata by filling the vacant space. This type of methods can wellcontrol the embedding distortion and provide a sufficient EC.The first histogram-based RDH method is the one proposed

by Ni et al. in [16]. This method uses peak and minimumpoints of the pixel-intensity-histogram to embed data. Itchanges each pixel value at most by 1, and thus a good markedimage quality can be obtained. However, its EC is quite lowand this method does not work well if the cover image hasa flat histogram. To facilitate it, Lee et al. [17] proposed toutilize the difference-histogram instead. This novel method ex-ploits the correlation among neighboring pixels and can embedlarger payload with reduced distortion compared with Ni etal.’s. Moreover, we will see later (see Section III-A) that Leeet al.’s method can be in fact implemented, in an equivalentway, by modifying the two-dimensional pixel-intensity-his-togram according to a pixel-pair-mapping (PPM) which isan injective mapping defined on pixel-pairs. In this light, thesuperiority of Lee et al.’s method over Ni et al.’s is explainedin another viewpoint. Afterwards, Fallahpour [18] introduceda method by modifying the histogram of prediction-error.Like difference-histogram, the prediction-error-histogram isalso Laplacian-like and sharply distributed which guaranteesan excellent embedding performance. Instead of only usingthe correlation of two adjacent pixels in Lee et al.’s method,Fallahpour’s method can exploit the local correlation of alarger neighborhood, and thus can provide relatively betterperformance. Besides the aforementioned methods, manyother works are also based on histogram by incorporatingsome strategies such as double-layered embedding [31]–[33],embedding-position-selection [24], [31], [34], adaptive em-bedding [24], context-modification [22], and optimal-bins-se-lection [25], [35], etc.

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1092 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 7, JULY 2013

We remark that, the histogram-based RDHmethods generallycontain two basic steps:• (Histogram generation) First, each local image region con-sisting of several pixels (e.g., a pixel-pair consisting of twoadjacent pixels) is projected to a one-dimensional space(e.g., difference value of a pixel-pair) to get a scalar se-quence. Then, a one-dimensional histogram (e.g., differ-ence-histogram) is generated by counting the frequency ofthe resulting sequence.

• (Histogram modification) Finally, embed data into thecover image by modifying the histogram. In most cases,the histogram bins with high frequencies are expandedto carry data while some others are shifted to ensure thereversibility.

In the first step, the complex local image correlation is sim-plified to a one-dimensional statistic. Clearly, by this simplifi-cation, the image redundancy cannot be fully exploited and itonly contributes to the second step since a one-dimensional his-togram is easy to deal with. Based on this consideration, insteadof one-dimensional histogram used in current RDH methodsand to better exploit the image redundancy, we propose in thispaper a novel RDH scheme by using a two-dimensional differ-ence-histogram.For the proposed method, by considering a pixel-pair and its

context, a local image region is projected to a two-dimensionalspace to obtain a sequence consisting of difference-pairs.Then, a two-dimensional difference-histogram is generatedby counting the difference-pairs. Finally, reversible dataembedding is implemented according to a specifically de-signed difference-pair-mapping (DPM). Here, the DPM isan injective mapping defined on difference-pairs, and it is anatural extension of expansion embedding and shifting tech-niques used in current histogram-based methods. By using thetwo-dimensional difference-histogram and this specific DPM,compared with the conventional one-dimensional histogrambased methods, more pixels are used for carrying data whilethe number of shifted pixels is reduced as well, and thus animproved embedding performance is achieved. In addition,inspired by the embedding-position-selection techniques intro-duced in previous works [24], [31], [34], a pixel-pair-selectionstrategy is adopted in our method to priorly use the pixel-pairslocated in smooth image regions to embed data. This mayfurther enhance the embedding performance. Experimentalresults demonstrate that the proposed method outperformssome state-of-the-art works.The rest of the paper is organized as follows. The related

works are briefly introduced in Section II. Section III presentsthe proposed RDH scheme in details. The comparisons with theprior arts are shown in Section IV. Section V concludes thispaper.

II. RELATED WORKS

To well exploit the redundancy of natural images, the dif-ference-histogram-based RDH methods utilize pixel-pairs withsmall differences for expansion embedding and other pairs forshifting. These methods may control the maximum modifica-tion to each pixel value and thus the marked image quality can

be well guaranteed. There are several related works providingvaluable thoughts.In 2006, Lee et al. [17] proposed a difference-histogram-

based RDH method. The method modifies the pixel-pairs withdifferences 1 or to carry data. Specifically, for data embed-ding, the difference image is first computed for a gray-scalecover image as

(1)

where is a pixel-pair consisting of twoconsecutive pixels. Then the marked image can be obtainedas

ififififif

(2)

where is a data bit to be embedded. Notice that thefirst pixel in the pair keeps unchanged in this embeddingprocedure, i.e., one simply takes .Accordingly, from a marked image , the embedded data

bit can be extracted as

ifif

(3)

and the original pixel value can be recovered as

ififif

(4)where is the differencevalue computed from the marked image.In this method, the bins 1 and are utilized for expansion

embedding and other bins (except bin 0) for shifting. It outper-forms some classical RDH methods such as [12] and [16].In 2009, another difference-histogram-based RDH method

was proposed by Tai et al. [15]. This method could markedlyextend the embedding space by utilizing a binary tree structure(BTS). Particularly, unlike Lee et al.’s, the pixel-pairs for differ-ence calculation in this method can be overlapped since in thedata extraction procedure, the first pixel of the pair is already re-covered when processing this pixel-pair. In this overlapped wayand by utilizing BTS, Tai et al.’s method performs well and itcan provide a higher EC than that of Lee et al.’s.Recently, Tai et al.’s method is improved by Hong [34] by

utilizing a dual binary tree (DBT), a better pixel predictor andan error energy estimator. Compared with Tai et al.’s, Hong’sDBT-based method can increase EC at the same level of dis-tortion. Moreover, in addition to DBT, median-edge-detection(MED, see also [36] and [19]) and error energy estimatorare used to obtain a more concentrate histogram and to lo-cate the embeddable pixels. With the error energy estimator,only pixels with error energy less than a threshold will beexpanded or shifted, leading to a marked decrease of distortion.Experimental results demonstrate that Hong’s method can

LI et al.: NOVEL RDH SCHEME BASED ON TWO-DIMENSIONAL DIFFERENCE-HISTOGRAM MODIFICATION 1093

Fig. 1. PPM for illustrating the data embedding procedure for (a) Lee et al.’s method and (b) its improvement.

significantly improve the image quality and EC of Tai et al.’sand some state-of-the-art works such as [37] and [38].It is worth mentioning that some recent works [18]–[21] can

also be viewed as Tai et al.’s improvement. In [18]–[21], thedifference value is replaced by the prediction-error computedusing a larger local image region so that the redundancy can bebetter exploited.Our method is motivated by the aforementioned works and

will be described in details in next section.

III. PROPOSED RDH SCHEME

Basically, the proposed RDH scheme is based on modifica-tion of two-dimensional difference-histogram by constructing aDPMwhich is an injective mapping defined on difference-pairs.This Section is organized as follows. First, to better ex-

plain our idea, we introduce Lee et al.’s method from a PPMviewpoint in Section III-A. We show that this difference-his-togram-based method can be implemented by modifying thetwo-dimensional pixel-intensity-histogram according to a PPMwhich is an injective mapping defined on pixel-pairs. Thenin Section III-B, we extend the idea of PPM to DPM andpresent our DPM-based scheme. In this way, by extending dif-ference-histogram (or, in an equivalent way, two-dimensionalpixel-intensity-histogram) to two-dimensional difference-his-togram, the proposed scheme extends current histogram-basedRDH methods. The image redundancy can be better exploitedby our approach and the advantage of DPM is demonstrated inSection III-C. Finally, the detailed embedding and extractionprocedures of our scheme are summarized in Section III-D.

A. PPM for RDH

We point out that, in an equivalent way, Lee et al.’s em-bedding procedure can be demonstrated by a PPM shown inFig. 1(a), in which a subset of is divided into two disjointed

parts as black points and blue points, each black point is mappedto a blue one (indicated by a green arrow) and each blue pointis mapped to another blue point. Here, each point represents thevalue of a pixel-pair, and the black points are used for expan-sion embedding while the blue ones for shifting. According tothis PPM, for a cover pixel-pair , its marked value can bedetermined in the following way:1) if (i.e., is a red point), the marked pixel-pair is taken as itself.

2) if or (i.e., is a black point)a) if the to-be-embedded data bit , the markedpixel-pair is taken as itself.

b) if the to-be-embedded data bit , the markedpixel-pair is taken as its associate blue point.

3) if or (i.e., is a blue point),the marked pixel-pair is taken as its associate blue point.

The corresponding data extraction and image restorationprocess can also be demonstrated according to the PPM sinceit is an injection, i.e., each point has at most one inverse. Thetrivial description is omitted.From the PPM viewpoint, Lee et al.’s difference-histogram-

based method is actually implemented by modifying the two-dimensional pixel-intensity-histogram.Lee et al.’s method only modifies the second pixel of the pair.

Thus two modification directions, up and down, are allowed indata embedding. This is to say, in PPM, a point can be ei-ther mapped to its upper neighbor or lower neighbor

. Actually, one can also modify the first pixel withoutintroducing additional distortion resulting in modification direc-tions left and right. In this way, the associate mapped point of

has four choices: , , or(see Fig. 2(a)). Based on these four modification direc-

tions, Lee et al.’s method can be improved by designing a newPPM shown in Fig. 1(b). According to this figure, one can see

1094 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 7, JULY 2013

Fig. 2. (a) By modifying either or by 1, has four modification direc-tions. (b) The corresponding difference-pair also has four modificationdirections, where , , and is a prediction of .

that more pixel-pairs (black points) are utilized for expansionembedding, and the number of shifted pixel-pairs (blue ones) isreduced as well. Here in Fig. 1(b), the parameters and canbe adaptively selected by maximizing EC.By this new PPM, more data bits can be embedded without

degrading the marked image quality. Taking the 512 512sized gray-scale Lena image for an example, compared withLee et al.’s original method, EC is increased from 24,000 bitsto 25,000 bits and PSNR is slightly improved from 52.12 dB to52.14 dB. The advantage of the new PPM lies in the exploita-tion of more modification directions.

B. DPM for RDH

For a pixel-pair , we propose to compute two differ-ence values and to form a two-di-mensional difference-histogram of , where is a pre-diction of which will be clarified later. Inspired by the afore-mentioned new PPM, we will modify either or by 1. Inthis situation, since has four modification directions, thedifference-pair also has four modification directions:

, , or(see Fig. 2(b)). For example, by modifying to , the mod-ification direction to is “up” and the corresponding mod-ification direction to is “upper-left”, since changesto and changes to . Based on these four mod-ification directions, we will introduce a new RDH scheme bydesigning a DPM.The ideas of related works play an important role in our

scheme. We extend the idea of two-dimensional pixel-inten-sity-histogram (or, in an equivalent sense, one-dimensionaldifference-histogram) of Lee et al. to two-dimensional dif-ference-histogram. Besides, for each , we compute thepredication of based on the context of for an accurateestimation. Here, the gradient-adjusted-prediction (GAP) willbe used in our scheme. Moreover, to further improve themarked image quality, we adopt a strategy to select smoothpixel-pairs for data embedding. The main idea of this strategy issimilar to those of pixel selection of our previous work [24] anderror energy estimation of Hong [34]. By pixel-pair-selection,a noisy-level is computed for each pixel-pair, and only thepixel-pairs with relatively small noisy-levels will be embedded.We now briefly introduce our embedding procedure. First,

divide the cover image into nonoverlapped pixel-pairs. For each

Fig. 3. Context of , where the location of pixel is . The blue pixelsare used to compute the GAP predictor for , and all ten pixelsare used to compute the noisy-level.

pixel-pair , compute the prediction of to get using GAPpredictor:

ifif

ififif

ifif

(5)

where are neighboring pixels of (seeFig. 3), and

represent the vertical and horizontalgradients, and . Notice thatshould be rounded to its nearest integer if it is not an integer.Then, compute the noisy-level of denoted asusing its ten neighboring pixels (see also Fig. 3)as

(6)

where represents the context of containing the tenpixels and stands for the gradient operator. Here, for dis-crete image, the noisy-level is computed by summing bothvertical and horizontal differences of every two consecutivepixels in , and it is less than or equal to .Clearly, a pixel-pair located in smooth regions may have asmall noisy-level. Finally, for each pixel-pair with noisy-levelless than a threshold , compute the difference-pairand implement data embedding according to the DPM shownin Fig. 4.Here, as the same as the case of PPM, each black point in this

DPM is mapped to a blue one and it will be used for carryingone data bit, while each blue point is mapped to another blueone and it is simply shifted to ensue the reversibility. Also, likethe PPMs in Fig. 1, this DPM is an injection.It should be mentioned that there are many ways to design

DPM to derive different RDH schemes with different perfor-mance. Any DPM can give a RDH scheme if it is an injection.As for the DPM shown in Fig. 4, our idea is to use as much aspossible the points with high frequency to carry data. So we takesome points with either or small as black to carrydata, and meanwhile we shift the other points according to thefour allowable modification directions. The superiority of this

LI et al.: NOVEL RDH SCHEME BASED ON TWO-DIMENSIONAL DIFFERENCE-HISTOGRAM MODIFICATION 1095

TABLE IMARKED VALUE OF COVER PIXEL-PAIR ACCORDING TO THE DPM SHOWN IN FIG. 4 AND DIFFERENT CASES OF

DIFFERENCE-PAIR , WHERE IS A TO-BE-EMBEDDED DATA BIT

Fig. 4. DPM for illustrating the proposed data embedding procedure.

specific DPM will be experimentally demonstrated in the nextsubsection.We give some explanations for the final step of our

method to clarify how DPM works. For example, whenand , the corresponding differ-

ence-pair is which is a black point in Fig. 4. Inthis case, the pixel-pair will be expanded to carry one data bit:1) if the to-be-embedded data bit , the marked pixel-pairis taken as itself.

2) if the to-be-embedded data bit , the marked dif-ference-pair is then taken as its associate pointwhere the modification direction is “upper-left”, and thusthe marked pixel-pair is taken as the upper neighbor of

, i.e., .For another example, and , thedifference-pair is which is blue. In this sit-uation, the pixel-pair will be shifted. Since the modificationdirection to this difference-pair is “lower-right”, the markedpixel-pair is then taken as the lower neighbor of , i.e.,

TABLE IIEXTRACTED DATA BIT AND RECOVERED VALUE FROM A MARKED PIXEL-PAIR

ACCORDING TO THE DPM SHOWN IN FIG. 4 AND DIFFERENTCASES OF DIFFERENCE-PAIR

. For general cases, as a detailed de-scription of the DPM shown in Fig. 4, the marked value is listedin Table I.Notice that the pixel-pair scanning order in our data extrac-

tion procedure is inverse to that of embedding. By this means,when processing a pixel-pair in data extraction, its context hasalready been recovered. Thus the same prediction and noisy-level used by encoder can be obtained by decoder. This issueand the injectivity of DPM guarantee the reversibility of ourscheme. The corresponding data extraction and pixel-pair re-covery process are listed in Table II. In the table,and are difference values computed from themarked pixel-pair , where is the same predictionused in data embedding by encoder.

C. Evaluation for DPM

As we have known (see Section II-C of [24] for details),for the histogram-based RDH, if the maximum modification topixel values is 1 in data embedding, the expected value of themodification (in -norm) to cover image is ,where and are numbers of expanded and shiftedpixels, respectively. Thus the ratio of expanded pixels

(7)

is a measurement of the embedding performance. The largerthe ratio is, the less modification to cover image and better per-formance is. We then use (7) to demonstrate the superiority of

1096 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 7, JULY 2013

TABLE IIICOMPARISONS FOR THE RATION OF EXPANDED PIXELS BETWEEN THE PROPOSED SCHEME AND THE METHODS OF LEE et al. [17] (INCLUDING ITS IMPROVEMENT

PRESENTED IN SECTION III-A), HONG et al. [19], AND FALLAHPOUR [18], FOR SIX STANDARD 512 512 SIZED GRAY-SCALE IMAGES

the proposed DPM-based scheme. Clearly, for our method, ac-cording to Fig. 4, the ratio of expanded pixels can be formulatedas

(8)where means the frequency of .Table III shows the comparisons of the ration of expanded

pixels for: our method with different pixel-pair-selection thresh-olds, Lee et al.’s method [17] and its improved one presented inSection III-A, Hong et al.’s MED-based method [19], and Fal-lahpour’s GAP-based method [18]. Except Barbara, all the im-ages used in our experiments are downloaded from the USC-SIPI database1. For our method, when the pixel-pair-selectionthreshold is taken as its maximum, i.e., the sum of maximumnoisy-level of all pixel-pairs and 1

(9)

it means that the pixel-pair-selection strategy is disabled andall pixel-pairs are used for data embedding. According to thisTable, one can see that our method is better than those prior artssince it always has a larger ratio. The superiority of the two-di-mensional difference-histogram and this specifically designedDPM is thus verified. On the other hand, for our method, the ra-tion becomes larger with smaller . This demonstrates the ad-vantage of pixel-pair-selection strategy. Finally, as a comple-ment to Table III, Fig. 5 shows performance comparisons withthe aforementioned works. Our scheme performs well and it sig-nificantly outperforms the prior arts.The detailed data embedding and extraction procedures in-

cluding the treatment of overflow/underflow will be given inthe next subsection. More experimental results of comparisonswith state-of-the-art works will be reported in Section IV.

D. Data Embedding and Extraction Procedures

The proposed data embedding procedure contains severalbasic steps. First, divide the cover image into nonoverlappingpixel-pairs. Then, embed the secret message into a part of coverimage (noted as ). Next, record the least significant bits (LSB)of some pixels of (noted as ) to get a binary sequence, andembed this sequence into the rest part of , i.e., . Finally,

1http://sipi.usc.edu/database/database.php?volume=misc

Fig. 5. Performance comparisons between the proposed method, Lee et al.’smethod [17] and its improved one presented in Section III-A, Hong et al.’sMED-based method [19], and Fallahpour’s GAP-based method [18], for imageLena.

by using LSB replacement, embed the auxiliary informationand the compressed location map into . The detailed dataembedding procedure is described as bellow step-by-step.Step-1: Except the last two columns and last two rows, from

left to right and top to bottom, divide the cover image intononoverlapped pixel-pairs. Then we define a location map

noted to solve the overflow/underflow problem. Notice thatthe maximum modification to each pixel is 1 in our method, sowe only need to deal with the pixels with extreme values 0 or255. Specifically, for the -th pixel-pair , if or

, we define . Otherwise, we take .Consequently, is a binary sequence of length . Then loss-lessly compress the location map using arithmetic coding. De-note the length of the compressed location map as . For eachpixel-pair with , compute the prediction of andthe noisy-level according to (5) and(6).Step-2: The secret message will be embedded in this step ac-

cording to the location map and a pixel-pair-selection threshold, i.e., successively, for each , consider the -thpixel-pair:1) if , the overflow/underflow would occur and wedo nothing with it.

2) if and its noisy-level is no less than , the pixel-pair is considered as a rough one and we also do nothingwith it.

3) otherwise, it will be shifted or expanded to carry one databit according to Table I or the DPM shown in Fig. 4.

This step will stop if all message bits have been embedded, andwe denote as the index of the last data-carrying pixel-pair.

LI et al.: NOVEL RDH SCHEME BASED ON TWO-DIMENSIONAL DIFFERENCE-HISTOGRAM MODIFICATION 1097

Fig. 6. Six test images. From left to right: Lena, Baboon, Barbara, Airplane (F-16), Peppers, and Fishing boat.

Step-3: The auxiliary information and the compressed loca-tion map will be embedded in this step for blind decoding. Thisstep contains several parts:1) First, record LSB of the first imagepixels to obtain a binary sequence , where is theceiling function.

2) Then, embed into the remaining pixel-pairs (i.e., thepixel-pairs with index to ) using the samemethod inStep-2. After embedded, we denoteas the index of the last data-carrying pixel-pair.

3) Finally, replace LSB of the first pixelsby the following auxiliary information and the compressedlocation map defined in Step-1 to generate the markedimage.a) pixel-pair-selection threshold: (12 bits),b) index of the last embedded pixel-pair in Step-2:( bits),

c) length of the compressed location map: (bits),

d) index of the last embedded pixel-pair: (bits).

We remark that, in the above embedding procedure, to priorlyuse smooth pixel-pairs, the threshold is taken as the smallestpositive integer such that the message can be embedded. Thisparameter can be determined iteratively.The corresponding data extraction and image restoration

process are summarized as follows.Step-1: Read LSB of the first pixels of

marked image to determine the values of , , and .Then, read the next LSB to determine the compressed locationmap. Finally, generate the location map by decompressingthe compressed location map.Step-2: In this step, we extract the LSB sequence defined

in 1) of Step-3 of data embedding. First, as the same as data em-bedding, except the last two columns and last two rows, dividethe marked image into nonoverlapped pixel-pairs. Then, in areverse order, from the -th to -th pixel-pair, extractthe sequence and restore the cover image as follows. Supposethe index of the to-be-processed pixel-pair is :1) First, compute the prediction and noisy-level accordingto (5) and(6). Notice that the pixel-pair processing orderhere is inverse to that of embedding, so every pixel-pairwith an index larger than has already been restored whenprocessing the -th pixel-pair. In this light, the decodercan obtain the same prediction and noisy-level as used byencoder.

2) Then, if and the noisy-level is less than , thedata extraction and pixel-pair restoration can be realized

according to Table II; otherwise, there is no data embeddedand the pixel-pair is just recovered as itself.

It should bementioned that for the -th pixel-pair with ,it is unchanged in embedding procedure, and thus the pixel-paircan be recovered as itself.Step-3: Replace LSB of the first image

pixels by the sequence extracted in the step above. Then usethe same method as Step-2 to extract the embedded messagefrom the first pixel-pairs, and meanwhile to realize restora-tion for these pixel-pairs. Finally, the embedded message is ex-tracted and the original image is recovered.

IV. EXPERIMENTAL RESULTS

Six 512 512 sized gray-scale images including Lena,Baboon, Barbara, Airplane (F-16), Peppers, and Fishing boat(see Fig. 6) are used in our experiment. Fig. 7 shows theperformance comparisons between the proposed method andfollowing state-of-the-art works: Lee et al. [17] (including itsimproved one presented in Section III-A), Fallahpour [18],Hong et al. [19], Sachnev et al. [31], Li et al. [24], Hong [34],and Hong [33]. For our method, we vary EC from 5,000 bits toits maximum with a step of 1,000 bits. Here, 5,000 bits of ECmeans an embedding rate (ER) of 0.019 bits per pixel (bpp).According to this figure, compared with Lee et al.’s method

and its improvement, one can see that our superiority is sig-nificant. It experimentally demonstrates that the DPM-basedscheme can provide a much better performance than PPM.Moreover, compared with the methods of Fallahpour and

Hong et al., our superiority is also significant. The two methodsare based on the prediction-error-histogram with differentpredictors. Although these methods may exploit the spatialredundancy for a larger pixel context and perform better thanLee et al.’s in most cases, our scheme can improve them by in-creasing PSNR by 1–6 dB. Our advantage lies in the utilizationof two-dimensional difference-histogram and pixel-pair-selec-tion strategy.Sachnev et al.’s method is based on the prediction-error-his-

togram incorporating with an embedding-position-selectionstrategy similar to ours. A sorting technique is used in thismethod to record prediction-errors based on the magnitudeof local variance, and a pixel will be priorly embedded if ithas a small local variance. This method performs well andit is superior to some typical RDH schemes such as [6], [14]and [20]. Referring to Fig. 7, one can see that in most cases,our scheme is better than this well performed method. But,for some images, when EC approaches its maximum (e.g., forBaboon when EC is larger than 9,000 bits, for Airplane whenEC is larger than 45,000 bits), Sachnev et al.’s method can

1098 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 7, JULY 2013

Fig. 7. Performance comparisons between our scheme and following seven methods: Lee et al. [17] (including its improvement presented in Section III-A),Fallahpour [18], Hong et al. [19], Sachnev et al. [31], Li et al. [24], Hong [34], and Hong [33].

achieve a larger PSNR. The reason is that in such cases, sinceEC is high, the smooth pixel-pairs are insufficient and ourmethod should necessarily use noisy pixel-pairs to embed datawhich is unfavorable to the performance. On the other hand,since Sachnev et al.’s method modifies each pixel to embed

data (recall that in our method, we modify only one pixel ina pixel-pair), its sorting technique still works well while ourpixel-pair-selection strategy is no longer effective when our ECapproaches its maximum. In this light, Sachnev et al.’s methodmay perform better than ours in a few cases. However, referring

LI et al.: NOVEL RDH SCHEME BASED ON TWO-DIMENSIONAL DIFFERENCE-HISTOGRAM MODIFICATION 1099

TABLE IVCOMPARISONS OF PSNR (IN dB) BETWEEN OUR METHOD AND FOUR METHODS

OF SACHNEV et al. [31], LI et al. [24], HONG [34], AND HONG [33],FOR AN EC OF 10,000 BITS (ER 0.038 BPP)

TABLE VCOMPARISONS OF PSNR (IN dB) BETWEEN OUR METHOD AND FOUR METHODS

OF SACHNEV et al. [31], LI et al. [24], HONG [34], AND HONG [33],FOR AN EC OF 20,000 BITS (ER 0.076 BPP)

to Tables IV and V, our method generally achieves a largerPSNR than Sachnev et al.’s. Our average gains are 1.44 dBfor an EC of 10,000 bits, and 1.2 dB for an EC of 20,000 bits.Notice that the result for Baboon is not presented in Table Vsince our method cannot embed such a payload into this image.Li et al. and Hong’s methods [24], [33], [34] are also based

on the prediction-error-histogram incorporating with an em-bedding-position-selection strategy. According to Fig. 7, asexpected, these methods perform similarly to Sachnev et al.’sand they are better than ours only for some images when ourEC approaches its maximum. In most cases, our method canachieve a larger PSNR. Referring to Tables IV and V, one cansee that our method improves Li et al. and Hong’s by increasingPSNR by at least 1 dB in average.We remark that, comparing our method with [24], [31], [33],

[34], our main advantage is the utilization of two-dimensionaldifference-histogram.We now discuss the size of the compressed location map.

Notice that in our method, the maximum modification to eachimage pixel is at most 1 in value, so the overflow/underflowproblem may only occur for the pixel-pair where

or . For the six test images Lena, Ba-boon, Barbara, Airplane (F-16), Peppers, and Fishing boat, ex-cept the last two columns and last two rows which are not usedin data embedding, the amount of pixel-pairs satisfying

or is 0, 0, 0, 0, 2, and 9, respectively.So, in the test cases, there are only a few “1” in the locationmap (actually, there is no overflow/underflow in most cases).Thus the size of the compressed location map is rather small.Finally, it should be mentioned that some tested methods here

can provide a much higher maximum EC (about 1.0 bpp) thanthe proposed one (e.g., only 0.14 bpp, for the image Lena). Themaximum EC in bits (ER in bpp) for our method is 36,690(0.14) for Lena, 12,830 (0.049) for Baboon, 30,790 (0.117) forBarbara, 50,399 (0.192) for Airplane (F-16), 25,309 (0.097) forPeppers, 24,137 (0.092) for Fishing boat, respectively. This is

a drawback of our method. However, this level of EC is suffi-cient for many practical applications, e.g., Coatrieux et al. [4]pointed out that an EC of 3,500 bits (about 0.014 bpp for a512 512 sized image) is enough for the application of RDHin medical image sharing. Improving EC is beyond the scope ofthis paper, and we will investigate this issue in our future work.

V. CONCLUSION

In this paper, we presented a novel RDH scheme by usinga two-dimensional difference-histogram according to a specifi-cally designed DPM. In addition, a pixel-pair-selection strategyis also proposed to further enhance the embedding performance.This work is the first attempt to employ higher dimensional

histogram to design RDH. Compared with the previously in-troduced one-dimensional histogram based methods, our ap-proach can exploit the image redundancy better and achievean improved performance. However, since only one pixel ofa pixel-pair is allowed to be modified by 1 in value, our ECis low. This issue should be investigated in the future. More-over, utilizing more suitable two-dimensional histogram and de-signing more meaningful DPM (e.g., in an image dependentway) to achieve the best embedding performance is also a valu-able problem.

REFERENCES[1] Y. Q. Shi, “Reversible data hiding ,” in Proc. IWDW, 2004, vol. 3304,

pp. 1–12, ser. Springer LNCS.[2] Y. Q. Shi, Z. Ni, D. Zou, C. Liang, and G. Xuan, “Lossless data hiding:

fundamentals, algorithms and applications,” in Proc. IEEE ISCAS,2004, vol. 2, pp. 33–36.

[3] R. Caldelli, F. Filippini, and R. Becarelli, “Reversible watermarkingtechniques: An overview and a classification,” EURASIP J. Inf. Secu-rity, vol. 2010, 2010, Article 134546.

[4] G. Coatrieux, C. L. Guillou, J. M. Cauvin, and C. Roux, “Reversiblewatermarking for knowledge digest embedding and reliability controlin medical images,” IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 2,pp. 158–165, Mar. 2009.

[5] M. Fontani, A. D. Rosa, R. Caldelli, F. Filippini, A. Piva, and M. Con-salvo, “Reversible watermarking for image integrity verification in hi-erarchical pacs,” in Proc. 12th ACM Workshop on Multimedia and Se-curity, 2010, pp. 161–168.

[6] S. Lee, C. D. Yoo, and T. Kalker, “Reversible image watermarkingbased on integer-to-integer wavelet transform,” IEEE Trans. Inf.Forensics Security, vol. 2, no. 3, pp. 321–330, Sep. 2007.

[7] R. Li, O. C. Au, C. K. M. Yuk, S. Yip, and T. Chan, “Enhanced imagetrans-coding using reversible data hiding,” in Proc. IEEE ISCAS, 2007,pp. 1273–1276.

[8] K.-L. Chung, Y.-H. Huang, P.-C. Chang, and H.-Y. Liao, “Reversibledata hiding-based approach for intra-frame error concealment inH.264/AVC,” IEEE Trans. Circuits Syst. Video Technol., vol. 20, no.11, pp. 1643–1647, Nov. 2010.

[9] J. Fridrich, M. Goljan, and R. Du, “Lossless data embedding—newparadigm in digital watermarking,” EURASIP J. Appl. Signal Process.,vol. 2002, no. 2, pp. 185–196, Feb. 2002.

[10] M. U. Celik, G. Sharma, A. M. Tekalp, and E. Saber, “Lossless gen-eralized-LSB data embedding,” IEEE Trans. Image Process., vol. 14,no. 2, pp. 253–266, Feb. 2005.

[11] M. U. Celik, G. Sharma, and A. M. Tekalp, “Lossless watermarkingfor image authentication: A new framework and an implementation,”IEEE Trans. Image Process., vol. 15, no. 4, pp. 1042–1049, Apr. 2006.

[12] J. Tian, “Reversible data embedding using a difference expansion,”IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 8, pp. 890–896,Aug. 2003.

[13] A. M. Alattar, “Reversible watermark using the difference expansionof a generalized integer transform,” IEEE Trans. Image Process., vol.13, no. 8, pp. 1147–1156, Aug. 2004.

[14] L. Kamstra and H. J. A. M. Heijmans, “Reversible data embeddinginto images using wavelet techniques and sorting,” IEEE Trans. ImageProcess., vol. 14, no. 12, pp. 2082–2090, Dec. 2005.

1100 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 7, JULY 2013

[15] W. L. Tai, C. M. Yeh, and C. C. Chang, “Reversible data hiding basedon histogram modification of pixel differences,” IEEE Trans. CircuitsSyst. Video Technol., vol. 19, no. 6, pp. 906–910, Jun. 2009.

[16] Z. Ni, Y. Q. Shi, N. Ansari, and W. Su, “Reversible data hiding,” IEEETrans. Circuits Syst. Video Technol., vol. 16, no. 3, pp. 354–362, Mar.2006.

[17] S. K. Lee, Y. H. Suh, and Y. S. Ho, “Reversible image authenticationbased on watermarking,” in Proc. IEEE ICME, 2006, pp. 1321–1324.

[18] M. Fallahpour, “Reversible image data hiding based on gradientadjusted prediction,” IEICE Electron. Express, vol. 5, no. 20, pp.870–876, Oct. 2008.

[19] W. Hong, T. S. Chen, and C. W. Shiu, “Reversible data hiding forhigh quality images using modification of prediction errors,” J. Syst.Software, vol. 82, no. 11, pp. 1833–1842, Nov. 2009.

[20] D.M. Thodi and J. J. Rodriguez, “Expansion embedding techniques forreversible watermarking,” IEEE Trans. Image Process., vol. 16, no. 3,pp. 721–730, Mar. 2007.

[21] Y. Hu, H. K. Lee, and J. Li, “DE-based reversible data hiding withimproved overflow location map,” IEEE Trans. Circuits Syst. VideoTechnol., vol. 19, no. 2, pp. 250–260, Feb. 2009.

[22] D. Coltuc, “Improved embedding for prediction-based reversible wa-termarking,” IEEE Trans. Inf. Forensics Security, vol. 6, no. 3, pp.873–882, Sep. 2011.

[23] X. Gao, L. An, Y. Yuan, D. Tao, and X. Li, “Lossless data embeddingusing generalized statistical quantity histogram,” IEEE Trans. CircuitsSyst. Video Technol., vol. 21, no. 8, pp. 1061–1070, Aug. 2011.

[24] X. Li, B. Yang, and T. Zeng, “Efficient reversible watermarking basedon adaptive prediction-error expansion and pixel selection,” IEEETrans. Image Process., vol. 20, no. 12, pp. 3524–3533, Dec. 2011.

[25] H.-T.Wu and J. Huang, “Reversible image watermarking on predictionerrors by efficient histogram modification,” Signal Process., vol. 92,no. 12, pp. 3000–3009, Dec. 2012.

[26] D. Coltuc and J. M. Chassery, “Very fast watermarking by reversiblecontrast mapping,” IEEE Signal Process. Lett., vol. 14, no. 4, pp.255–258, Apr. 2007.

[27] S. Weng, Y. Zhao, J. S. Pan, and R. Ni, “Reversible watermarkingbased on invariability and adjustment on pixel pairs,” IEEE SignalProcess. Lett., vol. 15, no. , pp. 721–724, 2008.

[28] X. Wang, X. Li, B. Yang, and Z. Guo, “Efficient generalized integertransform for reversible watermarking,” IEEE Signal Process. Lett.,vol. 17, no. 6, pp. 567–570, Jun. 2010.

[29] F. Peng, X. Li, and B. Yang, “Adaptive reversible data hiding schemebased on integer transform,” Signal Process., vol. 92, no. 1, pp. 54–62,Jan. 2012.

[30] D. Coltuc, “Low distortion transform for reversible watermarking,”IEEE Trans. Image Process., vol. 21, no. 1, pp. 412–417, Jan. 2012.

[31] V. Sachnev, H. J. Kim, J. Nam, S. Suresh, and Y. Q. Shi, “Reversiblewatermarking algorithm using sorting and prediction,” IEEE Trans.Circuits Syst. Video Technol., vol. 19, no. 7, pp. 989–999, Jul. 2009.

[32] L. Luo, Z. Chen, M. Chen, X. Zeng, and Z. Xiong, “Reversible imagewatermarking using interpolation technique,” IEEE Trans. Inf. Foren-sics Security, vol. 5, no. 1, pp. 187–193, Mar. 2010.

[33] W. Hong, “An efficient prediction-and-shifting embedding techniquefor high quality reversible data hiding,” EURASIP J. Adv. SignalProcess., vol. 2010, 2010.

[34] W. Hong, “Adaptive reversible data hiding method based on error en-ergy control and histogram shifting,” Opt. Commun., vol. 285, no. 2,pp. 101–108, 2012.

[35] C. Wang, X. Li, and B. Yang, “High capacity reversible image water-marking based on integer transform,” in Proc. IEEE ICIP, 2010, pp.217–220.

[36] M. J. Weinberger, G. Seroussi, and G. Sapiro, “The LOCO-I losslessimage compression algorithm: Principles and standardization intoJPEG-ls,” IEEE Trans. Image Process., vol. 9, no. 8, pp. 1309–1324,Aug. 2000.

[37] P. Tsai, Y. C. Hu, and H. L. Yeh, “Reversible image hiding schemeusing predictive coding and histogram shifting,” Signal Process., vol.89, no. 6, pp. 1129–1143, Jun. 2009.

[38] W. Hong and T. S. Chen, “A local variance-controlled reversible datahiding method using prediction and histogram-shifting,” J. Syst. Soft-ware, vol. 83, no. 12, pp. 2653–2663, Dec. 2010.

Xiaolong Li received the B.S. degree from PekingUniversity, Beijing, China, the M.S. degree fromEcole Polytechnique, Palaiseau, France, andthe Ph.D. degree in mathematics from ENS deCachan, Cachan, France, in 1999, 2002, and 2006,respectively.Before joining Peking University as a researcher,

he worked as a postdoctoral fellow at Peking Uni-versity from 2007 to 2009. His research interests areimage processing and information hiding.

Weiming Zhang received the M.S. and Ph.D.degrees in 2002 and 2005, respectively, from theZhengzhou Information Science and TechnologyInstitute, Zhengzhou, China.Currently, he is an associate professor with the

School of Information Science and Technology,University of Science and Technology of China.His research interests include multimedia security,information hiding, and privacy protection.

Xinlu Gui received the B.S. degree from PekingUniversity, Beijing, China, in 2012. She is currentlyworking toward the Master degree in computerscience at Peking University, Beijing, China. Herresearch interests are image processing and informa-tion hiding.

Bin Yang received the B.S. andM.S. degrees in com-puter science from PekingUniversity, Beijing, China,in 1991 and 1994, respectively.Currently, he is a Professor with the Institute of

Computer Science and Technology, Peking Univer-sity. His research interests are image processing andinformation hiding.