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2296 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 29, NO. 8, AUGUST 2019

Image Recapture Prevention Using SecureDisplay Schemes on Polarized 3D System

Sang-Keun Ji and Heung-Kyu Lee

Abstract— Image recapture is one of the simplest methods ofcontent leakage, but is very difficult to address with the rapiddevelopment and dissemination of digital devices. This paperproposes novel secure display schemes to prevent image recaptureon a polarized 3D system without additional hardware. Usingcommercial polarized 3D characteristics, the secret image to beprotected from recapturing is encrypted on a pixel-by-pixel basisand decrypted with polarized glasses (PGs). Moreover, the pro-posed secure display schemes allow for the restricted viewing ofsecret images that only authorized people may see or the selectiveviewing of secret images and public images that anyone may see.Quantitative and qualitative analyses of the experimental resultsshow that the proposed schemes can prevent content leakagefrom recaptured still or time axis images via a digital camerabecause the secret image is statistically encrypted, similar torandom noise. As such, the restricted viewing or the selectiveviewing is possible depending on the presence of PGs. Therefore,the proposed schemes not only prevent image recapture usingdigital devices but also implement a secure display using PGs.

Index Terms— Recapture prevention, copyright protection,secure display scheme, restricted viewing, selective viewing.

I. INTRODUCTION

ILLEGAL distribution of digital contents by recapturingthe display has become an important issue as digital cam-

eras become more widespread and internet access increases.Anyone can easily obtain high quality content anywhere bysimply pressing a button on the device, and recaptured contentis quickly distributed via the Internet. This often constitutesillegal distribution without the copyright holder’s consent,and once content is illegally leaked over the Internet, it isalmost impossible to recover or control it, resulting in massiveeconomic and social impacts. Therefore, methods to funda-mentally prevent unauthorized image recapture are requiredto eradicate illegal distribution of digital content.

Various methods have been proposed to protect copyrightcontents from recapture, which can be divided into threecategories: digital watermarking, recapture detection,

Manuscript received April 17, 2018; revised June 26, 2018 andJuly 27, 2018; accepted August 6, 2018. Date of publication August 17,2018; date of current version August 2, 2019. This work was supportedby the National Research Foundation of Korea (NRF) through the KoreanGovernment (MSIT) under Grant NRF-2016R1A2B2009595. This paperwas recommended by Associate Editor X. Cao. (Corresponding author:Heung-Kyu Lee.)

The authors are with the School of Computing, Korea Advanced Instituteof Science and Technology, Daejeon 34141, South Korea (e-mail:[email protected]; [email protected]).

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TCSVT.2018.2866021

and recapture prevention. Digital watermarking is wherea watermark is inserted into the content in advanceindicating copyright information and is extracted to verifycopyright [1]–[7]. Appropriate design ensures the watermarkis detectable even when the image is recaptured by digitalcameras. However, digital watermarking requires insertingthe watermark in advance and it is difficult for the watermarkto be robust to all possible attacks that occur in recapture.

Recapture detection determines whether a suspected imageis a recaptured image to protect copyright after contentis leaked via recapturing. Various techniques have beenproposed based on detecting distortions which are generatedwhen content on a display is recaptured, such as luminanceartifacts [8], combing artifacts [9], scene jitter [10], ghostingartifacts [11], color spectral sensitivity [12], edge orientationdistribution [13], edge blurriness [14], cyclo-stationarity [15],sensor pattern noise [16], luminance flicker [17], pixel-wisecorrelation coefficients [18]. However, these distortions can beremoved by unintentional or malicious attacks, and recapturedetection cannot prevent recapture itself.

On the other hand, recapture prevention eithercompletely or partially prevents recapture itself to preventillegal leakage of the contents. Various techniques have beenproposed to prevent image recapture, such as secure displaytechniques [19]–[22], infrared emission techniques [23]–[26],and software-based techniques [27], [28]. However, thesetechniques are disadvantageous since they require additionalhardware or cannot completely prevent image recapture dueto constraints, such as time-based decoding. As mentionedabove, various methods have been proposed to cope withimage recapture, but the illegal distribution of digital contentdue to unauthorized image recapture remains unresolved.

This paper proposes an image recapture prevention methodusing secure display schemes on a commercial polarized 3Dsystem. Based on the characteristics used to perceive depthfrom 2D images in a polarized 3D system, this method incor-porates two secure display schemes, the restricted viewingscheme (RVS) and the selective viewing scheme (SVS), whichencrypt the secret image to be protected from recapture.Neither scheme can see the secret image hidden within thepixels without PGs, nor can any information about the secretimage be extracted from a recaptured image. The secret image,which only an authorized person can see, may be recognizedthrough decryption using PGs. The RVS restricts secret imagesfrom being viewable only through PGs by encrypting secretimages with meaningless images, like random noise. In RVS,

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https://orcid.org/0000-0003-3601-6058

https://orcid.org/0000-0002-8119-9407

JI AND LEE: IMAGE RECAPTURE PREVENTION USING SECURE DISPLAY SCHEMES ON POLARIZED 3D SYSTEM 2297

meaningless images are seen if there is no PGs, while secretimages are seen if there is PGs. On the other hand, the SVSallows for meaningful images, called public images, to beseen without PGs. In SVS, it is possible to selectively vieweither the secret image or the public image by decidingwhether or not to use PGs. Experimental results showedthat the proposed methods were able to prevent secret imageinformation leakage from recaptured images using a digitalcamera, and no information regarding the secret image isexposed even if the encrypted images are recaptured on thetime axis. Quantitative and qualitative analyses also verifiedthat encrypted secret images have similar characteristics torandom noise and hence cannot be leaked from recapture.Therefore, the proposed schemes not only prevent imagerecapture using digital devices, but also allow the restrictedviewing or the selective viewing through PGs.

The remainder of this paper is organized as follows.Section II discusses the related work in recapture prevention.Section III describes a polarized 3D system the proposedschemes are based upon, and Section IV presents the twoproposed secure display schemes. Section V describesthe experimental results, and Section VI summarizes andconcludes the paper.

II. RELATED WORK

Secure display techniques [19]–[22] that control the viewingzone by using visual cryptography (VC) can be appliedto prevent image recapture. VC encrypts the secret imagewith n(≥ 2) meaningless images, and when k(≤ n) ofthem overlap with each other, the secret image is visuallydecrypted. They encrypt the secret image with meaninglessimages using VC and then locate (k − 1) of them on thedisplay as single or multiple decoding masks. Thus, whenthe single remaining meaningless image is scanned on thedisplay, the secret image is visually decoded by the decodingmasks. When the image is recaptured in areas outside of theviewing zone, which can be controlled by adjusting the masks,only meaningless images are recaptured. However, the viewingzone is fixed at the center of the display and they require theinstallation of additional tools, like single or multiple decodingmasks on display.

Infrared (IR) emission techniques can prevent video recap-ture based on the difference in sensory perception of IR raysbetween humans and devices [23]–[26]. IR emission units areplaced behind a display and emit IR rays as the content isdisplayed. Humans can view the video normally because theIR rays are not perceived, but any video that is recapturedusing a camcorder is corrupted by the IR beams and losesits content value. However, such systems incur additionalinstallation cost for the IR emission units.

Unlike previous techniques, software-based techniquescan prevent recapture without requiring additional hardwareinstallation. Chia et al. [27] proposed a technique to protectcontent from being recaptured as screenshots using an image-processing approach. They distorted the gray level image byadding random noise based on the human visual system (HVS)and produced multiple distorted images. The recaptured stillimage loses content value due to distortion when the image

is recaptured, so this technique can prevent image recapture.On the other hand, humans can automatically and mentallyrecover information that is similar to the original contentby viewing multiple distorted images for a specific time.However, the original image may not only be leaked byrecapturing distorted images on the time axis, but the distortedimages may also not be completely meaningless.

As an extension of [27], Hou et al. [28] proposed a tech-nique that makes recaptured images completely meaningless.They exploited the difference in the temporal response of thehuman eye between two areas of text and background in abinary image. In other words, the human eye can distinguishbetween two areas with different frequency flickers on thetime axis. Based on this characteristic, they encrypted a binaryimage with a number of meaningless images using VC. As aresult, the recaptured still image is meaningless, preventingimage recapture. Although this system successfully producedmeaningless images when recaptured by screenshots, the con-tent remained accessible if recaptured on the time axis.

III. POLARIZED 3D SYSTEM

Various 3D display methods have been considered toreproduce depth perception from 3D content comprising 2Dimages [29]. Two display methods, which are time multiplexeddisplays and polarization multiplexed displays, have becomethe main commercial glasses-based 3D systems. Time mul-tiplexed displays alternately display binocular images on thetime axis using shutter glasses synchronized with the display;whereas polarization multiplexed systems display left and rightviews simultaneously with different polarizations, and PGscomprising polarizing filters in different directions ensure theleft view is displayed only to left eye and vice versa. Incontrast to time multiplexing, polarization multiplexing doesnot require additional synchronization, and suitable PGs areless expensive and lighter.

Polarization multiplexing systems can be divided intolight reflecting (projector) or light emitting (TV) displays.Projector based systems display binocular images on onescreen using two projectors with different polarizing filters.Since these displays require two projectors and a specialreflective screen to preserve the light polarization, they areexpensive and require additional installation. On the otherhand, light emitting TV displays use spatial interleaving divid-ing different polarizations in the row or column direction,hence they consist of a single display. Although this spatialinterleaving halves the resolution, since the reduced resolutionon ultra-high-definition displays is less than the resolutionof high-definition display, the deterioration in image qualityis not noticeable. The methods proposed here are based ona polarized 3D system using light emitting TV displays toexploit line interlacing due to spatial interleaving.

Fig. 1 shows a typical polarized 3D system using light emit-ting TV display, consisting of a polarization multiplexed dis-play, PGs, and stereoscopic 3D (S3D) contents. The consumermarket commonly uses circular polarization, which allowviewers to slightly tilt their heads, as opposed to linear polar-ization. Circular polarization transforms unpolarized light intocircularly polarized light in a clockwise or counterclockwise


Fig. 1. Typical polarized 3D system.

Fig. 2. Overview of the proposed schemes. (a) Restricted viewing scheme.(b) Selective viewing scheme.

direction using circular polarizing filters, which consist of alinear polarizer and quarter-wave plate. The circular polarizingfilters are arranged line by line in the row or column directionsin the display. Polarized filters are commonly arranged inthe row direction to preserve 3D content horizontal disparity.Thus, we consider only displays where the filters are spatiallyinterleaved in the row direction, although the principle can beextended to other polarizing arrangements.

In Fig. 1, when left and right images are input to thedisplay, two images are halved in the vertical direction andline interlaced alternately in the row direction. Hence, the leftand right views circularly polarized in different directions arescanned on display. The left lens marked with red in thePGs passes through the odd rows and blocks the even rows ondisplay, so that the left eye sees the left view where the leftimage is located in odd rows and the black pixels are locatedin even rows. On the contrary, the right eye through the rightlens marked with blue sees the right view where the blackpixels are located in odd rows and the right image is locatedin even rows. Therefore, since the left and right eyes can seedifferent images from one display through the PGs, the viewercan perceive the depth.

IV. PROPOSED METHOD

This paper proposes two secure display schemes to preventimage recapture on polarized 3D systems, as shown in Fig. 2.Since the secret image is encrypted in pixel units, it is notpossible to extract any information about the secret image

from the recaptured image in both schemes. In addition, it ispossible to restrictively view the secret image and selectivelyview between secret and public images, depending on thepresence of PGs.

Before describing the proposed schemes, we explain theassumptions made in this paper. Let II be an interlaced image,Io be an image composed of odd rows of II , and Ie bean image composed of even rows of II . In other words,the image obtained by alternately line interlacing Io and Ie

in the row direction is II . Suppose that the secret image,S, is hidden in odd rows of II , that is, Io. Thus, we alsorefer to Io as the encrypted secret image. In a light emittingTV display, suppose that clockwise circular polarizing filtersare arranged in odd rows and counterclockwise filters in evenrows. If users view II through PGs, Io is visible through theclockwise circular polarizing filter, and Ie is visible throughthe counterclockwise filter. In contrast to the typical PGsin Fig. 1, the proposed schemes use PGs comprising of circularpolarizing filters with only one direction as shown in Fig. 2.To see only odd rows of II by the above assumptions, the pro-posed schemes use PGs in which the two lenses are composedof clockwise circular polarizing filters. Then, the secret imagehidden in odd rows of II is displayed on both eyes of theviewers through PGs. We define ID as the decrypted image,which is the image viewed through PGs in II , that is, the imagein which the secret image is encrypted in odd rows and blackpixels are located in even rows. For conceptual correspondenceto ID , we define IE as the encrypted interlaced image, whichis II encrypted with the secret image.

A. Restricted Viewing Scheme

The restricted viewing scheme (RVS) encrypts the secretimage so that no information about the secret image canbe obtained from the recaptured image. Since the secretimage can only be viewed by decrypting the interlaced imageusing PGs, unauthorized users without PGs cannot view thesecret image. Thus, RVS is a secure display scheme that allowsrestricted viewing of secret images depending on the presenceof PGs. The restricted viewing conditions, block design, andimplementation are described in detail below.

1) Restricted Viewing Conditions: The proposed m-RVSencrypts the secret image in units of m pixels. A block ofsize m, where m = p × q , p and q are positive integers,and m is a positive integer ≥ 2, represents one color, eitherblack or white. We define a block BI of size (2 p × q) in theinterlaced image II , and blocks of odd and even rows in BI

as Bo and Be, respectively. Thus, Bo and Be are blocks of size(p ×q) in Io and Ie, respectively. Let X be a random variablerepresenting the pixel value between 0 (black) and 1 (white),where X I , Xo, and Xe are the average pixel values in blocksBI , Bo, and Be, respectively. Let Xi

oand Xie be the average

pixel values of the i -th row in blocks Bo and Be, respectively,and Xi

I in block BI be the average pixel values of the i -thodd and even rows, as shown in Fig. 3. Then

E[XiI ] = 1

2

{E[Xi

o] + E[Xie]

}, (1)

where E denotes expectation.


Fig. 3. Image pixel block configuration.

Restricted viewing conditions for m-RVS, can be defined asfollows.

1. For any BI in II , X I depends on BS in S and thecorresponding BI , and satisfies

E[X I |X S = w] = E[X I |X S = b] = E[X I ]. (2)

2. For any BI in II , XkI of the k-th vector Bk

I satisfies

E[XkI ] = E[Xk+1

I ] = E[X I ]. (3)

3. For any Bo in Io, Xo depending on BS in S satisfies thecontrast

c = E[Xo|X S = w] − E[Xo|X S = b] > cth. (4)

X S is the average pixel value in block BS of S, and w and bdenote a white pixel and a black pixel, respectively.

The first and second conditions ensure the secret imageis invisible in the interlaced image, and the third condi-tion ensures appropriate contrast for secret image visibilityin the decrypted images. Condition 1 means that E[X I ]when X S is white simultaneous with E[X I ] when X S isblack, i.e., E[X I ] is constant regardless of the secret image.Condition 2 means that the expected average pixel valuesbetween different rows in an interlaced image should beequal, ensuring there is no visible difference between the rowsin II . Condition 3 means that the difference between expectedvalues representing the white and black pixels of the secretimage should be larger than the HVS based threshold cth

to perceive S within ID . If all three conditions are satisfied,it is possible to not only hide the secret image within theinterlaced image but also to allow the secret image to be visiblethrough PGs.

2) Block Design: We propose a block design to satisfythe conditions, where m-RVS consists of two collections ofm-dimensional Boolean vectors Cw and Cb that representswhite and black pixels respectively. Cw and Cb arecomprised of basis vectors V r

m , and V rm is defined as a set of

m-dimensional vectors v that combine r unit vectors so thatthe hamming distance of v is r . Hence

V rm =

{v

∣∣∣∣∣ v =m∑i

bi eiT

}, where

m∑i

bi = r, bi ∈ {0, 1}

(5)

where ei is a unit vector with 1 in the i -th position and0 elsewhere, and eT denotes the transpose of the vector e.

Thus, X of any vector in V rm is r . Since the ideal case for m-

RVS conditions is that E[Xo|BS = w] and E[Xo|BS = b] areclose to 1 and 0, respectively, and E[X I ] = 0.5, Cw and Cb

are constructed to have symmetry with each other to ensurethe sum of E[X] between Cw and Cb is 1, i.e.,

Cw = {V �m/2�m , ..., V m

m } (6)

and

Cb = {V 0m, ..., V �m/2�

m }. (7)

Consider the 4-RVS case for example. Cw and Cb that arecomprised of V r

4 ,

Cw = {V 24 , V 3

4 , V 44 }

=

⎧⎪⎪⎨⎪⎪⎩

⎡⎢⎢⎣

1100

⎤⎥⎥⎦,

⎡⎢⎢⎣

1010

⎤⎥⎥⎦,

⎡⎢⎢⎣

1001

⎤⎥⎥⎦,

⎡⎢⎢⎣

0110

⎤⎥⎥⎦,

⎡⎢⎢⎣

0101

⎤⎥⎥⎦,

⎡⎢⎢⎣

0011

⎤⎥⎥⎦,

⎡⎢⎢⎣

1110

⎤⎥⎥⎦,

⎡⎢⎢⎣

1101

⎤⎥⎥⎦,

⎡⎢⎢⎣

1011

⎤⎥⎥⎦ ,

⎡⎢⎢⎣

0111

⎤⎥⎥⎦,

⎡⎢⎢⎣

1111

⎤⎥⎥⎦

⎫⎪⎪⎬⎪⎪⎭

(8)

and

Cb = {V 04 , V 1

4 , V 22 }

=

⎧⎪⎪⎨⎪⎪⎩

⎡⎢⎢⎣

0000

⎤⎥⎥⎦,

⎡⎢⎢⎣

1000

⎤⎥⎥⎦,

⎡⎢⎢⎣

0100

⎤⎥⎥⎦,

⎡⎢⎢⎣

0010

⎤⎥⎥⎦,

⎡⎢⎢⎣

0001

⎤⎥⎥⎦,

⎡⎢⎢⎣

1100

⎤⎥⎥⎦,

⎡⎢⎢⎣

1010

⎤⎥⎥⎦,

⎡⎢⎢⎣

1001

⎤⎥⎥⎦,

⎡⎢⎢⎣

0110

⎤⎥⎥⎦,

⎡⎢⎢⎣

0101

⎤⎥⎥⎦,

⎡⎢⎢⎣

0011

⎤⎥⎥⎦

⎫⎪⎪⎬⎪⎪⎭

. (9)

Blocks Bo and Be that constitute BI are encrypted usingCw and Cb . We select one vector at random in Cw and Cb tohave uniform distribution, and construct Bo and Be dependingon X S by transforming the m-dimensional vector into a p ×qmatrix. Since all vectors with the same r are included in V r

m ,it does not matter how the vector is transformed into a matrix.In this paper, we construct a block by linear scanning in therow direction. Then, in the implementation, 0 representing ablack pixel is converted to 0, and 1 representing a white pixelis converted to 255. Block Bo representing BS is set to thesame color as that X S of BS . On the other hand, Be, whichdoes not represent the secret image but is included in BI ,is configured to be opposite to Bo to encrypt the secret image,i.e., if X S <0.5, Bo is constructed using Cb and Be using Cw ,and vice versa. Thus,

E[Xo|X S = w] =m∑

k=�m/2�

{k

m×

(mk

)∑m

l=�m/2�(m

l

)}

(10)

and

E[Xo|X S = b] =�m/2�∑k=0

{k

m×

(mk

)∑�m/2�

l=0

(ml

)}

, (11)

where(n

k

)is the number of k-combinations from n elements.


The proposed block design satisfies the restricted viewingconditions as follows. Since

E[X I |X S = w] = 1

2{E[Xo|X S = w] + E[Xe|X S = w]}

= 1

2{E[X |X S = w] + E[X |X S = b]} (12)

and

E[X I |X S = b] = 1

2{E[Xo|X S = b] + E[Xe|X S = b]}

= 1

2{E[X |X S = b] + E[X |X S = w]}, (13)

E[X I |X S = w] and E[X I |X S = b] have the same value,which satisfies the first condition. Since E[Xo] + E[Xe] = 1due to the block configuration and symmetry of the combi-nation, E[X I ] = 0.5 (the ideal value) regardless of the secretimage. Since all vectors in V r

m that have the same r are usedto construct a block, a vector that is vertically symmetric inthe matrix is also included in V r

m . Thus,

E[Xk |Cw] = E[Xk+1|Cw] (14)

and

E[Xk |Cb] = E[Xk+1|Cb], (15)

and hence

E[XkI |X S = w] = 1

2

{E[Xk

o|X S = w] + E[Xke |X S = w]

}

= 1

2

{E[Xk |Cw] + E[Xk |Cb]

}

= 1

2

{E[Xk+1|Cw] + E[Xk+1|Cb]

}

= 1

2

{E[Xk+1

o |X S = w]+E[Xk+1e |X S =w]

}

= E[Xk+1I |X S = w] (16)

and the case where X S is black is the same as (16). Thus,the second condition is satisfied. Substituting (10) and (11)into (4), the contrast is

c =m∑

k=�m/2�

{2k − m

m×

(mk

)∑m

l=�m/2�(m

l

)}

. (17)

Experimental results in Section V-B show that c is largeenough to distinguish white and black pixels based on HVS.Therefore, the proposed block design satisfies the restrictedviewing conditions.

3) Implementation: Fig. 4 shows the proposed m-RVSencryption process. We halve S vertical resolution and gen-erate Io, which represents the secret image, using m-RVSencryption. We then generate the complement image S′ of Sand encrypt it with Ie also using m-RVS encryption. Finally,IE is generated by line interlacing Io and Ie. Fig. 5 showssample 4-RVS generated images. The top half and bottomhalf of Fig. 5 (b) are Io and Ie, respectively, in whichS and S′ are encrypted using m-RVS encryption. Fig. 5 (c)is the encrypted interlaced image which encrypt (a) by lineinterlacing Ie and Io. Finally, authorized users can view (d)by decrypting (c) through PGs. Thus, the proposed m-RVSnot only prevents image recapture, but also allows restrictiveviewing of the secret image depending on the presence of PGs.

Fig. 4. m-RVS encryption process.

Fig. 5. Sample 4-RVS images. (a) Secret image. (b) Encrypted secret image(top half) and encrypted complement image (bottom half). (c) Encryptedinterlaced image. (d) Decrypted image through polarized glasses.

B. Selective Viewing Scheme

The selective viewing scheme (SVS) is similar to RVS inthat the secret image is encrypted and no information can beobtained from the recaptured image. The difference betweentwo schemes is that SVS allows anyone to see the meaningfulimage, called the public image, without PGs. Thus, SVS isa secure display scheme that allows selective viewing of thesecret image and the public image depending on the presenceof PGs. The selective viewing conditions, block design, andimplementation are described in detail below.

1) Selective Viewing Conditions: The proposed m-SVSencrypts the secret image in units of m pixels and the publicimage in units of 2m pixels. In m-SVS, Io represents the secretimage, and Ie not only conceals the secret image, S, but alsorepresents the public image, P , by interlacing with Io. Thefollowing selective viewing conditions must be satisfied.

1. For any BI in II , X I depending on BP in P and BS inS corresponding BI satisfies

E[X I |X P = w, X S = w] = E[X I |X P = w, X S = b](18)

and

E[X I |X P = b, X S = w] = E[X I |X P = b, X S = b].(19)

2. For any BI in II , X I depending on BP in P and BS inS corresponding BI satisfies

Pr(X I |X P = w, X S = w) = Pr(X I |X P = w, X S = b)

(20)


and

Pr(X I |X P =b, X S =w)= Pr(X I |X P =b, X S =b).

(21)

3. For any BI in II , X I depending on BP in P correspond-ing BI satisfies the contrast

cp = E[X I |X P = w] − E[X I |X P = b] > cth . (22)

4. For any Bo in Io, Xo depending on BS in S correspond-ing Bo satisfies the contrast

cs = E[Xo|X S = w] − E[Xo|X S = b] > cth. (23)

Pr denotes probability.The first and second conditions ensure the secret image is

invisible in the interlaced image. The third condition ensuresappropriate contrast for public image visibility in the interlacedimage, and the fourth condition ensures appropriate contrastfor secret image visibility in the decrypted images. Condition 1means that E[X I ] is constant depending on X P , withoutregard to X S , i.e., the interlaced image is affected only bythe public image, not the secret image. Condition 2 means thatthe distributions of X I in the interlaced image should be equaldepending on X P , i.e., the secret image cannot be statisticallyextracted from the recaptured image. Condition 3 means thatthe difference between E[X I ] representing white and blackin the public image should be greater than the HVS basedthreshold cth to perceive P from IE without PGs. Condition 4means that the difference between E[Xo] representing whiteand black in the secret image should be greater than thethreshold cth to perceive S from IE through PGs. In otherwords, two contrasts should be sufficiently larger than thethreshold based on HVS to recognize a public image and asecret image. If all the conditions are satisfied, the secret imageis concealed within the interlaced image, the public image isvisible with the naked eye, and the secret image is only visiblethrough PGs simultaneously.

2) Block Design: We propose a block design to satisfythe selective viewing conditions. m-SVS constructs Io byencrypting the secret image using m-RVS, then constructs Ie

by encrypting the public image based on Io. In contrastto m-RVS, m-SVS need only considers m is even, sinceit includes V m/2

m , representing a neutral color. We usecollections Cw and Cb, consisting of the m-dimensionalBoolean vectors used in m-RVS, to encrypt S into Io. Thenwe use collections Cr

w and Crb , comprised of m-dimensional

Boolean vectors depending on Io, to encrypt P into Ie,

Crw =

{V r+α

m , if r =m

2.

V m−rm , otherwise.

(24)

and

Crb =

{V r−α

m , if r =m

2.

V m−rm , otherwise.

(25)

where the strength α ∈ Z+ (1 ≤ α ≤ m

2 ) controls publicimage visibility. For example, we use Cw when X S of BS iswhite, which contains sets from V m/2

m to V mm . If V r

m is selected

as r = m/2 in Cw, we choose V m−rm irrespective of X P of P

to ensure S invisibility in IE . Otherwise, if V m/2m is selected

in Cw, S invisibility is satisfied in IE . Therefore, to ensurethe public image is visible regardless of X S of S, V m/2+α

m isselected when X P is white, and V m/2−α

m when X P is black.The proposed block design satisfies the selective viewing

conditions as follows. Since

E[X I |X P = w, X S = w]= E[X I |X P =w, Xo >0.5] + E[X I |X P =w, Xo =0.5]= E[X I |X P =w, Xo <0.5] + E[X I |X P =w, Xo =0.5]= E[X I |X P =w, X S =b] (26)

and

E[X I |X P = b, X S = w]= E[X I |X P =b, Xo >0.5] + E[X I |X P =b, Xo =0.5]= E[X I |X P =b, Xo <0.5] + E[X I |X P =b, Xo =0.5]= E[X I |X P =b, X S =b], (27)

where

E[X I |X P =w, Xo >0.5] = E[X I |X P =w, Xo <0.5] (28)

and

E[X I |X P =b, Xo >0.5] = E[X I |X P =b, Xo <0.5]. (29)

Thus, the proposed block design satisfies the first conditionthat E[X I ] is not affected by the secret image. Since Io isconstructed by selecting vectors with uniform distribution inCw and Cb, then if X P represents white, the probability of X I

depending on X S is

Pr(

X I = 1

2

∣∣∣ X P =w, X S =w)

=∑m

k= m2 +1

(mk

)∑m

k= m2

(mk

)

=∑ m

2 −1k=0

(mk

)∑ m

2k=0

(mk

) = Pr(

X I = 1

2

∣∣∣ X P =w, X S =b)

(30)

and

Pr(

X I = 1

2+ α

m

∣∣∣ X P =w, X S =w)

=( m

m/2

)∑m

k= m2

(mk

)

=( m

m/2

)∑ m

2k=0

(mk

) = Pr(

X I = 1

2+ α

m

∣∣∣ X P =w, X S =b), (31)

which is the same when X P represents black. Therefore,the distribution of X I depending on X P is constant regardlessof X S . E[X I ] depending on X P can be expressed as

E[X I |X P =w]=m∑

k= m2

{1

2×

(mk

)∑m

l= m2

(ml

)}

+ α

2m×

( mm/2

)∑m

l= m2

(ml

)

(32)


Fig. 6. m-SVS encryption process.

Fig. 7. Sample 4-SVS images. (a) Public image. (b) Encrypted secret image(top half) and encrypted public image (bottom half). (c) Encrypted interlacedimage. (d) Decrypted image through polarized glasses.

and

E[X I |X P =b]=m2∑

k=0

⎧⎨⎩

1

2×

(mk

)∑ m

2l=0

(ml

)

⎫⎬⎭ − α

2m×

( mm/2

)∑ m

2l=0

(ml

) .

(33)

The contrast cs , which is the difference between E[Xo]depending on X S , is equal to the contrast of m-RVS. Bysubstituting (32) and (33) into (22), the contrast cp , whichis the difference between E[X I ] depending on X P , is definedas

cp = α

m×

( mm/2

)∑m

k= m2

(mk

) . (34)

Experimental results in Section V-B show that cp is sufficientlylarge to recognize public images on IE based on HVS.Therefore, the proposed block design satisfies the selectiveviewing conditions.

3) Implementation: Fig. 6 shows the proposed m-SVSencryption process. We halve S vertical resolution and encryptit into Io using m-RVS encryption. Then, encrypt P into Ie,called the encrypted public image, depending on Io usingm-SVS encryption. Finally, generate IE by line interlacingIo and Ie in the vertical direction. Fig. 7 shows sample4-SVS generated images. The secret and public images usedare Fig. 5 (a) and Fig. 7 (a), respectively. In Fig. 7 (b),the top half is the encrypted secret image Io, and the bottomhalf is the encrypted public image Ie. Fig. 7 (c) is theencrypted interlaced image by line interlacing Ie and Io.As a result, anyone can view the public image without PGs

Fig. 8. Example test images. (a) Test image 1. (b) Test image 2.

as shown in (c), and authorized users can view the secretimage by decrypting (c) through PGs as shown in (d). Thus,the proposed m-SVS can prevent image recapture in the sameway as m-RVS, and allows selectively viewing of the publicand secret images depending on PGs.

V. EXPERIMENTAL RESULTS

We performed quantitative and qualitative analyses to verifythe performances of the proposed scheme. Quantitative analy-ses included histogram, correlation, information entropy, anddifferential attack analyses, which are widely used to evaluateimage encryption. In differential attack analysis, we definedtime-based differential attacks to verify information leakagefrom recapture on the time axis. Moreover, we performedcontrast analysis for fidelity and texture analysis related toimage recognition. We verified the dependence on the secretimage by performing quantitative analyses not only on theentire area of the encrypted image but on the area of theencrypted image corresponding to the area of white pixels andthe area of black pixels, respectively, in the secret image. Thus,we defined Sw and Sb as the area of white pixels and blackpixels in the secret image, respectively, and I (Sw) and I (Sb)as the area of an image I that corresponds to Sw and Sb,respectively. Qualitative analyses included fidelity assessmentsof secret and public images according to PGs, and secretimages from recaptured images. Each analysis was performed100 times for random image, R, secret image, S, encryptedinterlaced image, IE , encrypted secret image Io in IE , anddecrypted image ID . We set the block size m to 4 in bothSVS and RVS by setting p and q to 2, which is the minimumsize excluding 1, in order to confirm the relationship betweenpixel values in row and column directions while havinghigh contrast. Performance was compared for average values.To verify the proposed schemes’ effectiveness, we comparedthe generated images by combining a random image and asecret image with noise. The experiments used 10 text imagesthat were comprised of randomly selected upper and lowercase letters and numbers, as shown in Fig. 8. The text font wasArial and the font sizes were 50, 100, 200, and 300 px (pixels).

A. Contrast Analysis

We analyzed the contrast c, which is related to the visibilityof the secret image in RVS, and the contrast cp , which isrelated to the visibility of the public image in SVS. Thecontrast cs , which is related to the visibility of the secret imagein SVS, is the same as the contrast c in RVS.

Fig. 9 shows that c tends to decrease as m increases. Also,c is lower when m is even rather than odd because V m/2

m ,


Fig. 9. Contrast with block size m on m-RVS.

Fig. 10. Contrast cp with block size m and strength α on m-SVS.

Fig. 11. Example encrypted and decrypted images. Left side of each imageis as seen through polarized glasses. (a) Encrypted and decrypted image using4-RVS. (b) Encrypted and decrypted image using 4-SVS.

which represents the neutral color, is only included in bothCw and Cb when m is even. Although this is a disadvantage,it is offset by the advantage that the ambiguity is increased,which prevents information leakage about the secret imagebeing extracted from the recaptured image. Fig. 10 shows thatthe contrast cp increases with increasing α but decreases withincreasing m. Therefore, the proposed schemes can representvarious intensities by varying the contrasts, such as the blocksize m and strength α.

B. Secret and Public Image Fidelity

The proposed schemes are based on a commercial polarized3D display using circular polarization. Therefore, fidelitydegradation caused by viewer head tilt and viewing angle isextremely low [30], [31]. For example, Fig. 11 (a) shows animage obtained by encrypting Fig. 8 (a) using 4-RVS, and theleft half shows the decrypted image through PGs. Fig. 11 (b)shows an image obtained by encrypting Fig. 8 (a) as the secretimage and Fig. 8 (b) as the public image using 4-SVS, and theleft half shows the decrypted image through PGs. To evaluatethe fidelity of secret and public images, we calculated the

Fig. 12. Secret and public image fidelity for m-RVS and m-SVS schemes.

correction rate, rc,

rc = the length of the correct characters

the total length of the characters(35)

We used a 49-inch polarized 3D display (LG 49UF8570model) with 4K UHD resolution, and PGs comprised ofcircular polarized filters only in the clockwise direction. Sincethe PPI (pixels per inches) is 89.91 in the 49-inch displaywith 4K resolution, the font sizes of 50, 100, 200, and 300 pxcorrespond to 1.41, 2.82, 5.65, and 8.47 cm, respectively,on the display. According to ITU-R [32], the optimal viewingdistances for UHD resolution and FHD resolution are 1.5 and3.1 times the display height, respectively. They correspondto 0.92 m and 1.89 m on the 49-inch display used in theexperiment. Thus, we set the distances from the display to 1 m,1.5 m, and 2 m. Ten adults with normal or corrected vision(≈ 1.0 diopter) assessed image fidelity by viewing encryptedand decrypted images for 1 min.

Fig. 12 shows the impact of font size on correction rate. Thecorrection rate of the secret image in the encrypted interlacedimage was measured to be 0 for both m-RVS and m-SVS,regardless of the distance from the display. The correctionrate decreases as the font size decreases and the distance fromthe display increases, but is close to 1 for font size ≥ 100 pt.Thus, it has been verified that the proposed secure displayschemes can allow restricted viewing of the secret image andselective viewing of the secret and public images dependingon PGs.

C. Histogram Analysis

A histogram that represents the distribution of the pixelvalues for the given image is generally not uniformly distrib-uted for natural images, since they represent specific informa-tion. However, the histogram should be uniformly distributed,similar to random noise, for encrypted images to preventinformation leakage regarding secret images.

Fig. 13 shows histograms for white and black pixels for thetest images. IE and R have the same histogram regardlessof the secret image, while Io and ID histograms are notuniformly distributed since they represent the secret image.Therefore, the proposed schemes make it impossible to obtainany information about the secret image by statistical attack.


Fig. 13. Histogram of test images.

TABLE I

CORRELATION OF TEST IMAGES IN THE HORIZONTAL

AND VERTICAL DIRECTIONS

D. Correlation Analysis

Since natural images represent specific information differ-ently from random noise images, they have closely relatedadjacent pixels due to spatial redundancy. These relationshipsshould be eliminated by image encryption to prevent informa-tion estimation. We used correlation in horizontal and verticaldirections for all pixels to measure relationships betweenadjacent pixels in an image,

r(x, y) = E[{x − E(x)} {y − E(x)}]√D(x)

√D(y)

(36)

where

E(x) = 1

N

N∑i=1

xi , D(x) = 1

N

N∑i=1

{xi − E(x)}2 , (37)

x and y are adjacent pixels in a specific direction;E(x) and D(x) are the expected values and variance of x ,respectively; N is the length of x ; and r(x, y) is the correlationbetween the pixels.

Table I shows that adjacent pixels are highly correlated inboth directions in S, ID , and Io. However, these relationshipsare eliminated, becoming similar to R when encrypted usingthe proposed schemes. Therefore, the proposed schemes effec-tively encrypt secret images to exhibit random characteristics,eliminating spatial redundancy.

E. Information Entropy Analysis

Shannon proposed information to measure random variableuncertainties, and is widely used for quantitative analysis ofcryptosystems [33]. The entropy of an information source X

TABLE II

GLOBAL AND LOCAL INFORMATION ENTROPIES OF TEST IMAGES

can be expressed as

H (X) = −N∑

i=1

pi log pi , (38)

where pi = Pr(X = xi ); and N is the total number ofsymbols, xi . Entropy upper bound for N symbols = log N ,when xi is uniformly distributed. Generally, larger entropyimplies greater information uncertainty. We calculated thewhole image entropy, i.e., global entropy, and local entropyaverage and standard deviation for each block in the image.

Table II shows that global and local entropies of IE areclose to 1, which is the upper bound, i.e., R. However,Io, ID , and S entropies are significantly lower. Therefore,the proposed schemes prevent secret image informationleakage from recaptured images.

F. Time-Based Differential Attack Analysis

Differential attack detects the relationship between plain andcipher images by comparing changes in the cipher image gen-erated by making slight changes to the plain image. In additionto the differential attack, information about the plain image canbe leaked by comparing changes between the cipher imageson the time axis. Thus, we define time-based differential attackthat detects the relationship between cipher images encryptedwith the same plain image on a time axis, in contrast todifferential attack. To measure performance against time-baseddifferential attack, we calculated the number of pixels changerate (NPCR) [34] which is commonly used to measure changesensitivity for differential attack,

NPCR(C1, C2) =∑m×n

i, j D(i, j)

m × n× 100% (39)

where

D(i, j) ={

1, if C1(i, j) = C2(i, j).

0, otherwise.

C1 and C2 are two cipher images, and m and n are theimage height and width, respectively. The ideal NPCR valuefor cryptoanalysis in a binary image composed of white andblack pixels is 0.5 since it means the probability that apixel value was changed is random. If the NPCR approaches0 or 1, it means that the probability that a pixel value wasunchanged or changed is biased, and that certain informationmay appear along the time axis.

In Table III, we calculated the mean and standard deviationof the NPCR for each image for the entire area and for 4 × 4,


TABLE III

THE NUMBER OF PIXELS CHANGE RATE (NPCR) OF TEST IMAGES

TABLE IV

GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) TEXTURE PROPERTIES OF TEST IMAGES IN HORIZONTAL AND VERTICAL DIRECTIONS

8 × 8, 16 × 16, and 32 × 32 block units. We confirmedthat the NPCR values of IE and Io that were encryptedusing RVS and SVS follow a similar distribution to theGaussian distribution. In addition, the experimental resultsshow that the standard deviation decreased as the block sizeincreased and the NPCR values converge to values closeto 0.5. In contrast, since the NPCR value of [28] was biasedto a specific value in each region depending on the testimages, the NPCR values did not converge regardless of theblock size without following the Gaussian distribution. Thisis because [28] decrypted the encrypted image through time-based decoding, so the NPCR value depended on the secretimage’s pixel value. Thus, their method could leak informationabout secret images from the images recaptured on the timeaxis. However, since the decryptions in both RVS and SVS didnot use time-based decoding, we found that the NPCR valuesof the proposed schemes followed the same distribution asrandom noise without depending on the pixel values of thesecret image. Therefore, the proposed methods can preventinformation leakage from recapture on the time axis sincesensitivity to the time axis is low.

G. Texture Analysis

Image texture is an inherent property that is important forimage perception based on HVS [35], [36]. If the secret imagetexture is present in the encrypted image, secret image infor-mation can be leaked. Therefore, we analyzed encrypted imagetexture using the gray level co-occurrence matrix (GLCM),which is commonly used in texture analysis. GLCM representsa histogram of adjacent pixel pairs in a specific direction,and various textural descriptors using GLCM have beenproposed [37]. The three main descriptors: angular secondmoment (ASM), correlation, and entropy are measured.

Table IV shows that textural descriptors of IE in both direc-tions are similar to those for R, in contrast to Io and ID . Thus,the proposed methods not only visually conceal the secretimage in the encrypted image, but also prevent informationleakage through textural analysis from recaptured images byeliminating textural characteristics.

H. Comparison With Image Encryption and Noise Addition

To verify the effectiveness of the proposed methods, we con-structed an interlaced image, II , where the odd row image, Io,was a secret image and the even row image, Ie, was a randomnoise image. Then we investigated the effects of addingnoise to Io with varying noise density. Since Gaussian noisecannot completely hide the secret image, we used the salt-and-pepper noise (S&P), which consists of black and white pixels,to completely hide the secret image in the experiment. Wedefined Isp as an interlaced image with salt-and-pepper noise.For comparison of secret image fidelity, we performed fidelityanalysis at 1 m from the display under the same conditions asin Section. V-B.

Fig. 14 shows that minimum noise density to reach the levelof R and IE was 1.0 for the histogram, 1.0 and 0.85 for thecorrelation, 1.0 for the entropy, and 0.65 for the time-baseddifferential attack. Fig. 15 shows that minimum noise densitiesto reach texture analysis level of the R were 1.0 for all features;and 0.85, 1.0, and 1.0 in horizontal direction, 0.35, 0.90, and0.90 in the vertical direction (correlation, ASM, and entropy,respectively) to match IE .

Fig. 16 shows fidelity examples and Fig. 17 shows thatthe resultant correction rate was lowered for the encryptedimage at noise density = 0.9, but the secret image trace isvisible. The trace only becomes completely invisible whenthe noise density ≥ 0.95. However, as noise density increases


Fig. 14. Comparison between R, IE , and Isp with salt-and-pepper noise. eG and eL are the global entropy and the local entropy, respectively. (a) Histogramof Isp with salt-and-pepper noise. (b) Correlation of Isp with salt-and-pepper noise. (c) Entropy of Isp with salt-and-pepper noise. (d) Time-based differentialattack of Isp with salt-and-pepper noise.

Fig. 15. Textural descriptor comparison between R, IE , and Isp with salt-and-pepper noise. (a) Correlation of Isp with salt-and-pepper noise.(b) Angular second moment of Isp with salt-and-pepper noise. (c) Entropy of Isp with salt-and-pepper noise.

above 0.8 the decrypted image correction rate drops sharply,and it becomes impossible to recognize the secret image above0.9. Although the secret image can be hidden by adding noise,it becomes impossible to decrypt the secret image using PGs.Thus, the proposed methods can encrypt and decrypt the secretimage effectively.

I. Secret Image Fidelity Against Recapture AttackWe verified recapture prevention by conducting experiments

to extract secret images from recaptured images. In theproposed methods, the capability to prevent the leakage ofsecret images from recaptured images depends on the sizeand resolution of the display. Thus, we adopted the PPI,


Fig. 16. Test image examples with salt-and-pepper noise. (a) Isp with noise density 0.85. (b) Isp with noise density 0.90. (c) Isp with noise density 0.95.(d) ID of (a) through polarized glasses. (e) ID of (b) through polarized glasses. (f) ID of (c) through polarized glasses.

Fig. 17. Secret image fidelity in Isp .

which means the pixel density of the display for comparisonin various display environments. The PPI is calculated as= √

h2 + w2/d , where h and w are respectively the heightand width of the display and d is the diagonal length of thedisplay in inches. Fig. 18 shows the PPI with the resolutionand size of the display. We used 27-inch (81.59 PPI, LG27MT57D model) and 32-inch (68.84 PPI, LG 32MB25VQmodel) displays with FHD resolution and a 49-inch (89.91 PPI,LG 49UF8570 model) display with 4K resolution for variousPPIs and used a Panasonic DMC-LX100 model digital camerawith 12.8 million pixels and CMOS sensor for image recap-ture. In as ideal environment as possible to avoid geometricdistortion, encrypted images were recaptured at 0.5 m and 1 mdistances from the display with the parallel horizontal axis.

Fig. 19 shows the extracted secret images by extractingthe odd rows from the recaptured images. Fig. 8 (a) and (b)

Fig. 18. Pixels per inch (PPI) with resolution and inch.

respectively show the secret and public images that were used.No subjects could recognize the texts of the secret image fromeither the recaptured images or the extracted secret images,and not even traces of the secret images could be found.Experimental results show that it is not possible to extractsecret images from images recaptured at 0.5 m from thecenter of the 68.84 PPI display. Since distortions may occurduring image capture such as light scattering, handshaking,and geometric deformation, interference between pixels makesit impossible to extract secret images. The proposed schemesare robust to recapture up to 68.84 PPI, so we set the thresholdof PPI to 68.84 as shown in Fig. 18. Therefore, the proposedmethods could prevent image recapture since no informationabout the secret image could be extracted from the recapturedimage.


Fig. 19. Extracted secret images from recaptured encrypted images. The secret and public images used are (a) and (b) in Fig. 8, respectively. (a) Recaptured IEusing 4-RVS at 1m on 89.91 PPI. (b) Recaptured IE using 4-RVS at 0.5m on 89.91 PPI. (c) Recaptured IE using 4-SVS at 1m on 89.91 PPI. (d) Recaptured IEusing 4-SVS at 0.5m on 89.91 PPI. (e) Extracted secret image from (a). (f) Extracted secret image from (b). (g) Extracted secret image from (a). (h) Extractedsecret image from (b). (i) Recaptured IE using 4-RVS at 1m on 81.59 PPI. (j) Recaptured IE using 4-RVS at 0.5m on 81.59 PPI. (k) Recaptured IE using4-SVS at 1m on 81.59 PPI. (l) Recaptured IE using 4-SVS at 0.5m on 81.59 PPI. (m) Extracted secret image from (i). (n) Extracted secret image from (j).(o) Extracted secret image from (k). (p) Extracted secret image from (l). (q) Recaptured IE using 4-RVS at 1m on 68.84 PPI. (r) Recaptured IE using 4-RVSat 0.5m on 68.84 PPI. (s) Recaptured IE using 4-SVS at 1m on 68.84 PPI. (t) Recaptured IE using 4-SVS at 0.5m on 68.84 PPI. (u) Extracted secret imagefrom (q). (v) Extracted secret image from (r). (w) Extracted secret image from (s). (x) Extracted secret image from (t).

VI. CONCLUSION

We proposed novel secure display schemes to preventimage recapture by concealing images on a pixel basis usingcommercial polarized 3D system characteristics. Moreover,the proposed secure display schemes can restrict and selectcontent views using PGs. The m-RVS allows restricted view-ing of the secret image through PGs, and the m-SVS allowsselective viewing of the secret and public images dependingon PGs. The experiment results show that the proposed meth-ods encrypt secret images both statistically and cognitively,and that it is not possible to extract any information aboutthe secret image from recaptured still or time axis images.Therefore, the proposed methods can prevent content leakagefrom the recaptured images on polarized 3D systems withoutadditional hardware. It is expected that both schemes canbe utilized to prevent content leakage in situations such as

public presentations. RVS can be utilized in a situation suchas a private presentation, where only a limited audiencemay view the content, and SVS can be used in situationswhere two types of content may be viewed simultaneouslyon a single display, such as a multilingual captioning service.The proposed methods can represent various intensities bycontrolling parameters such as block size and strength in ablock, but color representation is limited since the encryptedimage consists only of black and white pixels. Future researchwill investigate secure display schemes for various colorrepresentations.

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Sang-Keun Ji received the B.S. degree from theDepartment of Computer and Software Engineer-ing, Kumoh National Institute of Technology, SouthKorea, in 2013, and the M.S. degree from theDepartment of Computer Science, Korea AdvancedInstitute of Science and Technology, South Korea,in 2015, where he is currently pursuing the Ph.D.degree with the Multimedia Computing Labora-tory, School of Computing. His current researchinterests include information hiding and multimediaprocessing.

Heung-Kyu Lee received the B.S. degree in elec-tronics engineering from Seoul National University,Seoul, South Korea, in 1978, and the M.S. and Ph.D.degrees in computer science from Korea AdvancedInstitute of Science and Technology (KAIST),Daejeon, South Korea, in 1981 and 1984, respec-tively. Since 1986, he has been a Professor with theSchool of Computing, KAIST. He has authored/co-authored over 200 international journal and con-ference papers. His major interests are informationhiding and multimedia forensics. He has been a

Reviewer of many international journals, including Journal of ElectronicImaging, Real-Time Imaging, and IEEE TRANSACTIONS ON CIRCUITS AND

SYSTEMS FOR VIDEO TECHNOLOGY.

2296 ieee transactions on circuits and systems for …hklee.kaist.ac.kr/publications/ieee trans. on...

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