copy move forgery

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Hindawi Publishing CorporationMathematical Problems in EngineeringVolume , Article ID ,pageshttp://dx.doi.org/.//

Research ArticlePassive Forensics for Region Duplication Image ForgeryBased on Harris Feature Points and Local Binary Patterns

Jie Zhao1,2 and Weifeng Zhao1

School o Computer and Inormation Engineering, ianjin Chengjian University, ianjin , China School o Electronic Inormation Engineering, ianjin University, ianjin , China

Correspondence should be addressed to Jie Zhao; [email protected]

Received November ; Revised December ; Accepted December

Academic Editor: Ebrahim Momoniat

Copyright J. Zhao and W. Zhao. Tis is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Nowadays the demand or identiying the authenticity o an image is much increased since advanced image editing sofwarepackages are widely used. Region duplication orgery is one o the most common and immediate tampering attacks which arerequently used. Several methods to expose this orgery have been developed to detect and locate the tampered region, whilemost methods do ail when the duplicated region undergoes rotation or ipping beore being pasted. In this paper, an efficientmethod based on Harris eature points and local binary patterns is proposed. First, the image is ltered with a pixelwise adaptiveWiener method, and then dense Harris eature points are employed in order to obtain a sufficient number o eature points withapproximately uniorm distribution. Feature vectors or a circle patch around each eature point are extracted using local binarypattern operators, andthe similar Harris points are matched based on their representation eature vectors using the BBF algorithm.Finally, RANSAC algorithm is employed to eliminate the possible erroneous matches. Experiment results demonstrate that theproposed method can effectively detect region duplication orgery, even when an imagewas distortedby rotation,ipping, blurring,AWGN, JPEG compression, and their mixedoperations, especially resistant to the orgery with the at areao little visual structures.

1. Introduction

Nowadays, with the development o state-o-the-art digitalimage technologies andthe widespread useo powerul imageediting sofware, even people who are not experts in imageprocessing can ake an image easily without leaving any visualtampering clues. Digital image orgeries, which seriously

debase the credibility o photographic images as deniterecords o events, have become so widespread a problemthat affects social and legal systems, orensic investigations,intelligence services, and security and surveillance systems.In order to recover peoples condence in the authenticityo digital images, image orensics aiming to reveal orgeryoperations in digital images are receiving more and moreattention.

In recent years, many image orgery detection techniqueshave been proposed, which can be broadly classied intotwo categories: active approach and passive approach. Activeimage orensic techniques represented by digital watermark[, ] require prior knowledge about the original image,

thus they are not automatic. In addition, the drawbacko digital watermark is that an imperceptible digital code(a watermark) must be inserted at the time o recording,which would restrict this approach to specially equipped dig-ital cameras. In contrast, passive orensics aims at identiyingthe authenticity o an image without prior knowledge and inthe absence o watermarks, which works by assuming that

even though the tampered images do not reveal any visualartiacts, the underlying statistics o these images would bedistinct rom the original ones. Owing to its incomparableadvantage, passive image orensics has been regarded as thepromising research interest in the eld o image orensics.

Among orgery techniques using typical image pro-cessing tools, region duplication, also being called copy-move, is the most common type o image orgery where aregion o an image is copied and then pasted to anothernonintersecting region in the same image to conceal animportant element or to emphasize a particular object. Dueto the nature o region duplication orgery, there will be atleast two similar regions in the tampered image, which is not


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Mathematical Problems in Engineering

common in natural images and thus can be used to detectthis specic artiact. In [], Weiqi et al. proposed the model oregion duplication orgery on which most existing detectionmethods are based. Since duplicated regions come rom thesame image, they have similar properties like texture, color,and noise. In practical situations, however, several image

intermediate operations and postprocessing operations couldbe involved in practical region duplication orgery. Teintermediate operations could be rotation, ipping, scaling,or illumination modiying. Te postprocessing operationsinclude noise adding, JPEG compression, or blurring. In apractical situation, a aked image may be a combination otwo or more operations, which is a direct challenge to mostexisting techniques.

In this paper, we propose a passive detection schemeor region duplication image orgery based on Harris cornerpoints and local binary patterns. Experiment results showthat the proposed method can effectively detect regionduplication orgery, even when an image was distorted byrotation, ipping, blurring, AWGN, JPEG compression, andtheir mixed operations,especially resistant to theorgery withthe at area o little visual structures.

Te rest o the paper is organized as ollows. InSection ,the related works on region duplication orgery detection areintroduced.Section briey reviews Harris corner points andlocal binary patterns. InSection , the proposed algorithmis described in detail. Te experimental results are givenand the corresponding analysis is discussed inSection . Teconclusion is drawn inSection .

2. Related Works

In the last decade, many passive techniques or regionduplication orgery have been proposed, which could begrouped into two categories: block-based methods []and keypoint-based methods []. Fridrich et al. [] rstanalyzed the exhaustive search and then proposed a blockmatching detection scheme based on quantized DiscreteCosine ransorm (DC) coefficients. In order to make thisalgorithm more robust and efficient, Huang et al. [] andCao et al. [] proposed an improved DC-based detectionmethod, respectively, which reduced the dimension o eature

vector. Popescu and Farid [] proposed a similar methodwhich represented image blocks using Principal ComponentAnalysis (PCA) instead o DC. Weiqi et al. [] extracted

color eatures as well as special intensity ratio to representa block characteristics vector. A different approach waspresented by Xiaobing and Shengmin [] in which the ea-tures were represented by the Singular Value Decomposition(SVD). Guohui et al. [] proposed to decompose the imageintoour subbands usingDiscrete Wavelet ransorm (DW)and then apply SVD on the blocks. However, when imagesare manipulated through geometry transorms like rotation,ipping, or scaling, all these above-mentioned methods ceaseto be effective. o address this problem, Bayram et al. []applied Fourier-Mellin ransorm (FM) to each block andFM values were nally projected to one dimension to ormthe eature vector. However, FM-based method can only

detect duplicated regions with slight rotation according totheir experimental results. Bravo-Solorio and Nandi []proposed a scheme based on log-polar coordinates to detectorgery regions, even when the duplicated regions haveundergone ipping, rotation, and scaling. Nevertheless, sincethe method depends on the pixel values, it is sensitive to the

change o the pixel values. Almost all the methods above-mentioned are block-based which attempt to nd an effectiveand robust representation o each block, moreover, theyare expected to be insensitive to common postprocessingoperations and intermediate operations.

In contrast to block-based methods, keypoint-basedmethods rely on the identication and selection o high-entropy image regions. In [], some approaches thatextracted keypoints by Scale-Invariant Feature ransorm(SIF) were proposed to detect the orgery due to theirrobustness to several geometrical transorms such as rotationand scaling. However, SIF-based scheme still has a limi-tation on detection perormance since it is only possible toextract the keypoints rom peculiar points o the image andnot robust to some postprocessing operations like blurringand ipping based on our experimental results. Xu et al. []and Shivakumar and Baboo [] proposed another keypoint-based method which used Speeded Up Robust Features(SURF) to approximately show the duplicated regions in theorged images. Te main drawback o most keypoint-basedmethods is that copied regions are ofen only sparsely coveredby matched keypoints. Tus they do not provide the exactextent and location o the detected duplicated region but onlydisplay the matched keypoints. Furthermore, i the copiedregion exhibits little structure, it may happen that the regionis completely missed [].

Most existing methods are typically evaluated againstsimple orgeries where human viewers have no trouble toidentiy the duplicated regions or low resolution imageswhich are a ar cry rom realistic tampered images withhigh resolution. Teir detection perormance on challengingrealistic orgery images is ar rom certain.

3. Theoretical Background

.. Harris Corner Detector. Harris corner detector [] is awidely used interest point detector, which has been appliedsuccessully in several image processing [,] and robotic

vision [, ] applications, since Harris eature points arestable under majority o the attacks such as rotation, noiseadding, and illumination change. Harris corner detector isbased on an underlying assumption that eature points areassociated with maxima o the local autocorrelation unction.

For a given image (,), its autocorrelation matrix atpoint(, )can be calculated as ollows:

, = ,V

(, V)

2 2

, ()

where and are the respective derivatives o pixelintensity in the

and

directions at point

(, ).

(,V

)is


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173 86 67 0

0

0

0 0 0

0

0

1

2

128

8

1

1

1 1

7997226

189 81 117

Treshold Multiply

Gray image

3 3 neighborhoodTresholded

neighborhoodWeighting LBP= 1 + 2 + 8 + 128

= 139

27 26 25

24

232221

20

F : Calculation o the original LBP operator.

gc

gn

F : Circularly symmetric neighborhoods or differentand, and( = 8, = 1), ( = 16, = 2), ( = 12, = 1.5).

the weighting unction usually o circular Gaussian orm asollows:

(, V)= 122 exp 2

+ V2

22 . ()Harris proposed a measure response to detect corners o animage:

=det () tr2 () , ()where det()is the determinant, tr()is the trace, andis ascalar value empirically chosen rom the range[0.04, 0.06].Corner pointswhich are greater than a specied thresholdareidentied as local maxima o the Harris measure response asollows:

, = , | , > , , , , ,, > , ()where(, )}is the set o all corner points,(, )is theHarris measure response calculated at point (,), (, )is an -neighbor set centered around the point (, ), andis a specied threshold.

In the process o Harris eature points extraction, thethresholddetermines the number o Harris eature points.Te larger the value is, the less the number o eature pointsis. On the contrary, the smaller the value is, the greater thenumber o eature points is, and the more intensive they

are distributed. In order to make the proposed algorithmeffective even when the duplicated region is in the at areawith little visual structure or o small size, we propose to

employ the dense Harris eature points, namely, the threshold equal to zero, so that a large number o Harris eaturepoints are obtained with approximately uniorm distribution,which is more benecial to enhance the robustness o thealgorithm.

.. Local Binary Pattern. Local Binary Pattern (LBP), pro-posed by Ojala et al. [], is a powerul means o texturedescription, which has gained increasing attention in manyimage analysis applications in virtue o its low computationalcomplexity, invariance to monotonic grayscale changes andtexture description ability. Te LBP operator can be seen as a

unied approach to statistical and structural texture analysis,since it describes each pixel by the relative gray levels oits neighboring pixels. Figure illustrates the calculation othe original LBP or one pixel with a3 3 neighboringblock. Tese eight neighbors are labeled by thresholding withthe central pixel value, weighted with powers o two, andthen summed to obtain a new value assigned to the centralpixel.

Using circular neighborhoods and linearly interpolation,LBP can be extended to allow the choice o any radius and number o pixels in the neighborhood to orma(, ) neighborhood, illustrated in Figure . Denote thecentral pixel at position

(

,

). Having

equally spaced


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Input imageConvert to

gray image

Adaptive

Wiener

ltering

Harris corner

detector

Extract LBP

features

Feature

matching

Remove false

matches

Detection

result map

F : Te ow diagram o the proposed detection method.

neighborhood pixels on a circle o radius, LBP is calculatedby:

LBP, , =1=0

2, ()= 1, 0,0,


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Step (Harris eaturepoints detection and eature extraction).Harris eature points in the ltered image are detected.As described in Section ., dense Harris eature pointsare employed in order to obtain a sufficient number oeature points with approximately uniorm distribution. Aferobtaining location coordinates o each eature point, LBP

is applied to each pixel in a circle patch with the radius o around each eature point. In the proposed algorithm,

by means o using rotation invariant uniorm LBP (LBP riu2, )and various combinations o/ values, we can realizeoperators or any quantization o angular space and or anyspatial resolution, which combine the inormation providedby multiple LBP operators.

In our experiments, three variants o rotation invariantuniorm LBP, including LBPriu28,1 , LBP

riu212,2, and LBP

riu216,2, are

applied to the circle patch around each eature point toextract the eatures. For a given circle patch with the radiuso around theth eature point, three histograms orotation invariant uniorm LBP, denoted by1,(LBPriu28,1),2,(LBP

riu212,2), and3,(LBP

riu216,2), are used as eature vectors,which are computed using LBPriu28,1 , LBPriu212,2, and LBPriu216,2,

respectively. It should be noticed that each eature vector isnormalized to unit length. Extracted eature vectors are putin separate eature matrices. Assuming that the total numbero Harris eature points is, thus we can obtain three eaturematrices o size(+2), tobe specic,FM1, FM2,andFM3,with dimensions o10, 14, and 18, respectively.Step (eature matching). In the eature matching step, thesimilar Harris points are matched based on their representa-tion eature vectors using the best-bin-rst (BBF) algorithm[] to determine the duplicated regions correctly. For a

Harris eature point at location x with eature vector f,we match it with point at locationx, whose correspondingeature vectorfis the nearest neighbor to fmeasured with2 (Euclidean) distance. It is well known that due to thesmoothness o natural image,the best match o a eature pointusually lies within its close spatial adjacency. Tus, in order toavoid searching nearest neighbors o a eature point rom thesame region, we perorm the search outside a 10 10 pixelscircle window centered at the eature point. Only pair-wisepoints with distinct similarities are kept in the matching step.Specically, we require that, or any other eature vector f

other thanfand f, the2distance between fand fhas to besmaller than that betweenfand f by at least a threshold

:

f f2f f2 < , ()where (0,1) is a preset threshold controlling thedistinctiveness o eature matching.

For each eature matrix o FM1, FM2, and FM3, werecord the indexes o every pair-wise matching points sat-isying (). Formally, let( index1, index2) be an indexpair o the two eature points which are represented bytwo rows o each eature matrix. Due to the order o anindex pair making no difference, the index pair o match-ing points is normalized, i necessary, by interchange o

positions so that index1 index2. For each index pair( index1, index2), we increment a matching requencycounterby one as ollows:

index1, index2 = index1, index2 + 1 . ()Te matching requency counter is initialized to zero beorethe algorithm starts. At the end o the matching process,the counterindicates the requencies with which differentindex pairs o matching points, which are determined bythree eature matrices FM1, FM2, and FM3, respectively,occur. o determine the candidate matching points, themajority rule is utilized. Specically, all the pair-wise match-ing points are ound, whose occurrence exceeds twice. Tematching strategy o eature points is applied or all Harriseature points in the corresponding matrices FM1, FM2,and FM3, and nal matching results are stored in a similarpoints matrix SPM, which records the corresponding spacial

coordinates o matching points.Step (removing alse matches and outputting detectionresult map). Due to a portion o mismatched eature points,we employ a widely used robust estimation method known asthe Random Sample Consensus (RANSAC) algorithm []to remove alse matches in the similar points matrix SPM.Te nal detection result map is output with color linesconnecting all the matching points to identiy the duplicatedregion and orgery region.

5. Experimental Results and Analysis

In our experiments, the tampered images were created byAdobe Photoshop CS based on the ollowing two datasets.Te rst one contains uncompressed PNG true colorimages with the size o pixels released by KodakCorporation or unrestricted research usage[]. In addition,we collected high resolution color images o size pixels rom Google image search [], which ormed thesecond dataset. Trough a large number o repeated experi-ments, threshold isxed to ., and the size o Wiener lterwindow is set to[5 5]. All the experiments were carried outon the platorm with Intel Pentium . GHz and MALABRb. By using our method, or each image with the twodifferent sizes rom the two datasets mentioned above, it takes

about s and s to locate the tampered regions, respectively,which are o high efficiency. Nevertheless, i we use C++ orJava programming languages to implement the algorithm,our algorithm will achieve higher efficiency.

.. Perormance Evaluation. For practical applications, themost important aspect o a detection method is the abilityto distinguish tampered and original images. Tus we adoptthe evaluation indexes which are dened in [] to evaluatethe perormance o our algorithm at image level. We keepa record o some important measures including the numbero correctly detected orged images, the number oimages that have been erroneously detected as orged

, and


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(a) Hiding specic objects

(b) Adding specic objects/multiple region duplication orgery

(c) Hiding specic objects/homogenous background orgery

F : Shown are the detection results o nonregular region duplication orgeries without any postprocessing operations.

the alsely missedorged images. Fromthese wecan obtaintwo evaluation indexes precision, , and recall,, as ollows:

= + , = + . ()

Precision denotes the probability that a detected orgery istruly a orgery, while recall shows the probability that a orgedimage is detected.

.. Effectiveness est. In the ollowing experiment, we select

some original imagesrom the two datasetsabove-mentionedto test the effectiveness o our algorithm. It is noted thatall the duplicated regions are nonregular and meaningulobjects, which are commonly true in realistic tamperedimages with high resolution. All the doctored images inthis experiment are without any postprocessing operationand the corresponding detection results are illustrated inFigure . Te rst column shows the original images, thesecond one gives the tampered images, and the third oneshows the detection results. Owing to space constraints, justa part o the experimental results is given here.Figure (a)illustrates the case o hiding specic objects and Figure (b)shows the case o adding specic objects, which indicates

that ouralgorithm canexpose regions o duplication orgerieseffectively. Images shown in Figure (b) also demonstratethat our algorithm works well even when the tamperedimages have multiple duplicated regions. Te doctored imageinFigure (c)shows the specic scenario that there are largesimilar or at regions in the image, such as large areas owater, sky or grass. Due to the homogenous background inthe suspicious images, it is, challenge to discern the orgery.o the best o our knowledge, a number o existing methodscease to be effective under the circumstances; however, thedetection results o our algorithm are satisactory. It is noted

that the proposed method outputs detection result maps withcolor lines connecting all the matching points to identiy theduplicated region and orgery region. Although the orgeryregion cannot be localized precisely to pixel level, we caneasily identiy the tampered region by color lines, which issufficient or practical detection requirements.

.. Robustness est. Since orgers usually do their utmostto create an imperceptible tampered image, various kindso intermediate operations and postprocessing operationsare carried out such as rotation, ipping, additive Gaussiannoise, Gaussian blurring, JPEG compression, or their mixedoperations.In thissection, we conduct a series o experiments


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(a) Rotation degrees

(b) Rotation degrees

(c) Rotation degrees

(d) Horizontal ipping

(e) Vertical ipping

F : Shown are the detection results o nonregular region duplication orgeries with intermediate operations rotation and ipping.

to test the robustness o the proposed method. Figure indicates that our algorithm can identiy duplicated regionsin the cases o different angles o rotation and horizontal and

vertical ipping with a satisactory degree. Images shown inFigure illustrate that the proposed algorithm can effectively

locate the duplicated regions under common postprocessingoperation including Gaussian blurring, AWGN, and JPEGcompression, even when the quality o distorted image ispretty poor, such as Gaussian blurring( = 7, = 5),AWGN

(SNR

= 10dB

), and JPEG compression

( = 20).


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AWGN (SNR= 10 dB) JPEG (Q = 20)

+ AWGN (SNR= 20 dB)+ JPEG (Q = 50)

Vertical flipping+

Gaussian blurring (w = 7, = 5)

Rotation:180 + Gaussian blurring (w = 5, = 3)

F : Shown are the detection results o nonregular region duplication orgeries under different postprocessing operations.

It is particularly worth mentioning that our method is robust,even when tampered images are distorted by mixed opera-tions o rotation/ipping transormations and postprocessingoperations.

Furthermore, in order to evaluate quantitatively therobustness o our algorithm to different image distortions, weselected randomly original images rom the two datasetsto generate orged images by copying a square region at arandom location andpasting it onto a nonoverlapping region.Te sizes o square region were pixels, pixels,and pixels, respectively, each kind o which includedtranslating and two different intermediate operations to gen-erate tampered images. Te intermediate operations wereipping (horizontal and vertical) and rotation (//degrees), respectively. Tese tampered images were thendistorted by commonly used postprocessing operations withdifferent parameters, such as Gaussian blurring, AWGN,

and JPEG compression. In order to obtain more credibleevaluation indexes, authentic images rom CASIA V.[] were chosen randomly together with original imagesand all the distorted images to compose a robustness testingimage set. Te experimental results were given in Figure . Ingeneral, the detection results shown inFigure indicate thatthe larger the area o duplicated region is, the betterthe detec-tion perormance would be, no matter which post-operationthe image is distorted by. As can be seen rom Figure (a),the proposed method has a high detection perormance whenthe images are distorted by Gaussian blurring, even whenthe image has poor quality ( = 7, = 4) and small orgeryregion (

60 60pixels), where the precision rate is larger

than % and the recall rate is larger than % in all thecases with different parameters o Gaussian blurring lter.We can draw a conclusion romFigure (b)that our methodperorms well also in the case o processing AWGN distorted

images. Te precision rate is over % till SNR drops to dB,even though there is a slight decay in the recall rate whenSNR drops. Results o tampered images distorted by JPEGcompression with different quality are shown inFigure (c),which indicate that our method perorms well in the case oJPEG compression.

.. Comparison o Detection Perormance. In the last exper-iment, we compared our method with the method in [], atypical keypoint-based scheme, based on the SIF keypointsdetection and eature matching which is robust to rota-tion, scaling, and some postprocessing operations includingAWGN and JPEG compression. In [], the duplicated region

is required to contain more than SIF keypoints, however,which is unrealistic in many practical detections since theduplicated region may not guarantee so many SIF keypointsespecially when the copied region exhibits little structure.Further, according to our experiments, the SIF method[] is sensitive to blurring artiacts and i the copied regionexhibits little structure or small orgery area, it may happenthat the region is completely missed [].

In contrast to the popular keypoint-based scheme basedon SIF keypoints detection in [,], the proposed methodhas good robustness against ipping artiacts and Gaussianblurring. As mentioned beore, the method in [,] is onlypossible to extract the keypoints rom peculiar points o


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Precision

(3, 1) (5, 2) (5, 3) (7, 4)

100

80

60

40

20

0

Gaussian blurring (w, )

Recall

100

80

60

40

20

0

(3, 1) (5, 2) (5, 3) (7, 4)

Gaussian blurring (w, )

(a)

Precision Recall

15 20 25 30

100

80

60

40

20

0

AWGN (dB)

100

80

60

40

20

0

15 20 25 30

AWGN (dB)

(b)

Precision Recall100

80

60

40

20

0

60 60

80 80

100 100

60 70 80 90

JPEG compression (quality)

100

80

60

40

20

0

60 60

80 80

100 100

60 70 80 90

JPEG compression (quality)

(c)

F : Shown are comparisons o average detection perormance o the proposed method. Precision and recall in (a) Gaussian blurring,(b) AWGN, and (c) JPEG compression.


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(a) SIF points- (b) Dense Harris points-

F : Comparison o the number and distribution o different eature points.

F : Shown is the detection result o region duplication orgery with little visual structures.

the image and not robust to some postprocessing operationslike blurring and ipping based on our experimental results.Moreover, i the copied region exhibits little visual structureor small area, it may happen that the region is completelymissed. However, our method perorms well in this kindo scenario. Te main reason is that our method employs

the dense Harris eature points which is superior to SIFeature pointsin [, ]. Figure gives an example o differenteature detection methods in the same image that shows thenumber and distribution situation o eature points. As seeninFigure , there are eature points detected by SIFalgorithm, while dense Harris eature points are detectedby our method. What is more, the distribution o eaturepoints is widely divergent. As shown in Figure (a), SIFalgorithm cannot nd reliable eature points in regions withlittle visual structures, and it is also hard to detect in smallerregions. However, dense Harris eature points employed inour method are nearly well distributed in the image shown inFigure (b). Consequently, our method can effectively detect

the orgery regions with little visual structures, such as largeareaso sky, grass, or water. One example is shown in Figure ,where an obvious duplicated region is not detected by theSIF method [, ] since SIF algorithm cannotnd reliableeature points in the orgery region. Te detection result usingour method is shown in the third column oFigure , whichdemonstrates that the proposed method can effectively detectthe orgery region with little visual structures.

In the last experiment, we compared our method withtwo typical approaches: FM-based [] and SIF-based [],which belong to block-based and keypoint-based detectionmethods, respectively. Here we still randomly selected original images rom the two datasets to generate orged

images by copying a square region at a random locationand pasting it onto a nonoverlapping region. Te sizeso square region were pixels, pixels, and pixels, respectively and included three differentlyrelative locations to generate tampered images. In therst scenario, we evaluated the three algorithms in the

case o rotation duplication orgery, where the duplicateregion was copied and then rotated with a random angle 0, 2, 5, 10, 15, 90,180,270} beore pasting. Inthe second scenario, horizontal and vertical ipping wereconsidered. In the third scenario, these tampered imagesweredistorted by commonly used postprocessing operations witha random parameter just as Section . showed, includingGaussian blurring, AWGN, and JPEG compression. All thedistorted images in the above-mentioned three cases togetherwith their original version and authentic images romCASIA V. composed three test image sets, respectively. Tecorresponding experimental results are shown inFigure .As can be seen romFigure , compared to the SIF-based

[] and FM-based [], the proposed method has a highdetection perormance when the images are distorted byGaussian blurring, AWGN, and JPEG compression. Tereare two main reasons or this. On one hand, we rst lterthe input image with a pixelwise adaptive Wiener methodbased on statistics estimated rom a local neighborhood oeach pixel, which has signicant improvements on detectionperormance, especially when the input image is sufferingrom severe AWGN and JPEG compression. On the otherhand, among those doctored images that are not detectedby the SIF method [], most o them are due to lackingreliable SIF keypoints in the duplicated region with little

visual structures. According toFigure , we can also see that


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Precision

Rotation Flipping Blurring AWGN JPEG

Proposed

SIFFM

100

80

60

40

20

0

(a)

Recall

Proposed

SIFFM

Rotation Flipping Blurring AWGN JPEG

100

80

60

40

20

0

(b)

F : Shown is a comparison o detection perormances o FM, SIF, and proposed methods.

the proposed method has a comparative advantage or thedetection o ipping orgery, while the SIF method [] isslightly superior to the proposed method in terms o rotationdetection. Te main reason would be that the detectionperormance o proposed method is slightly inerior to that othe SIF method [] in the rotation angles without a multiple

o90

degrees (90

,180

,270

,and360

).

6. Conclusion

In this paper a passive orensic method based on Harriseature points and local binary patterns or detecting regionduplication image orgery is proposed. We demonstrate theeffectiveness o our detection method with a series o experi-ments on lots o realistic orgery images with high resolution.Experimental results show that the proposed method caneffectively detect region duplication orgery, even when animage was distorted by rotation, ipping, blurring, AWGN,JPEG compression, and their mixed operations, especiallyresistant to the orgery with the at area o little visualstructures. Although having achieved promising detectionperormance, the proposed method ails to detect regionduplication orgery with scaling on account o the act thatHarris corners and LBP are sensitive to image scaling notbeing computed on multiscale image layers, which is animportant work in our uture study.

Conflict of Interests

Te authors declare that there is no conict o interestsregarding the publication o this paper.

Acknowledgment

Tis work was supported by Higher School Science & ech-nology Fund Planning Project o ianjin City (no. ),China.

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