Invisible logo watermarking using visualization ofspread spectrum signal on high-definition video

Tae-Woo Oh, Seung-Jin Ryu, and Heung-Kyu LeeDepartment of Computer Science, Korea Advanced Institute of Science and Technology

Daejoen, South KoreaEmail: twoh@mmc.kaist.ac.kr

Abstract—This paper introduces an invisible logo watermark-ing method for high-definition videos. The proposed methodimperceptibly embeds a logo watermark into the spatial domainof every frame on the basis of the spread spectrum watermarkingmethod, and visualizes the invisibly embedded logo by exploitingthe temporally statistical difference between signals estimatedfrom watermarked and unwatermarked regions within eachframe. In the embedding process, this method embeds twopatterns, which are a logo pattern and a temporal sync pattern.The logo pattern, which represents copyright information, ispermutated by a secret key in the temporal axis. The temporalpermutation of the logo watermark pattern guarantees the secu-rity that only an authorized user identifies the embedded logo.The temporal sync pattern is to find the starting interval of thepermutation for the logo pattern in the logo extracting process. Inthe logo detecting process, after finding the starting interval, theembedded logo is visualized by accumulating the estimated signalon basis of the secret key and post-processing the accumulatedresult. Extensive experiments show that the proposed methodhas good performance in respect of invisibility, security, real-timeprocessing, and robustness against geometrically and temporallymixed attacks.

I. INTRODUCTION

With the growth of digital multimedia industry, the copy-right protection for digital contents has become an importantissue. Digital watermarking technique comes into the spotlightas the solution for the problem. Digital watermarking isto insert a signal into the digital contents. The embeddedwatermark signal stands for copyright information such asownership.There have been various video watermarking researches,

which are broadly classified into two categories accordingto the property of the embedded watermark: visible logowatermark and invisible pseudo-noise-like sequence water-mark. The schemes for the former watermark embed visuallymeaningful marks like a logo or seal representing contentsowner. These make it possible for non-technical arbitratorsto easily convince the watermark [1]. Also, the embeddedlogo is identified in spite of errors caused by various at-tacks, because human visual system (HVS) has the ability tomeaningfully recognize the logo degraded by some noises [2].The embedded video contents, however, are visually degradedbecause of the opaque or semi-transparent logo watermark,and the visible logo can become the target removed byvarious object inpainting methods [3], [4]. The watermarkingschemes using pseudo-noise-like sequence provide a numerical

value to confirm the existence of the watermark. Since theseschemes imperceptibly embed the watermark as random noisesignals, the embedded watermark does not largely degradethe quality of the target video and the pirates cannot easilyestimate and remove the embedded watermark. However, theinvisibly embedding schemes are susceptible to various signalprocessing and geometrical attacks, and the numerical valueis unfamiliar to laymen.In order to integrate the advantages of the above mentioned

two watermark categories, the invisible logo watermarkingschemes have been researched. They invisibly embed a mean-ingful logo symbol into digital contents. These schemes pro-vide the imperceptibility of the embedded mark, and the ex-tracted logo represents a visually meaningful ownership. In [5],X. Niu et al. presented multiresolution logo watermarkingmethod. In this method, the watermark logo is decomposedinto hierarchical structure, and the decomposed result is furtherdecomposed into 8 bitplanes. After random permutation anderror correction coding of the bitplanes, they are embedded inthe corresponding resolution of the 3-D or 2-D decomposedoriginal video. D. Kundur and D. Hatzinakos proposed robustinvisible logo watermarking scheme based on multiresolutionimage fusion approach [6]. The host image and the logo imageare transformed into the discrete wavelet domain in L leveland 1 level, respectively. The resulting coefficients in non-overlapping block are fused with the transformed logo water-mark considering HVS characteristics in DWT domain. In [1],A. A. Reddy and B. N. Chatterji improved the fusion approach.This method embeds the watermark into each wavelet coef-ficient with the weight factor calculated in pixel unit insteadof block unit. Invisible logo watermarking method based oninsertion and extraction in discrete cosine transform (DCT)domain was proposed by [7]. The method firstly finds out thesensitive and perceptually important region in DCT domain ofthe target image, and creates a compound watermark by fusinga watermark logo and a synthetic image generated from theperceptually important region. Then, the compound watermarkis imperceptibly embedded by fusing with the correspondingblocks of the perceptually important region. However, theabove mentioned methods are impractical owing to the non-blind detecting system that needs original contents when thewatermark is detected. Also, the traditional methods are notsuitable for high-definition (HD) video contents because of thehigh complexity of their algorithm. In addition, they are not

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robust against geometrical attacks such as rotation, translation,and scaling.We propose a new invisible logo watermarking method

for HD videos. The proposed method invisibly embeds alogo pattern into each frame by using spread spectrum (SS)method [8]. The embedded logo is visualized by accumulatingand thresholding based on statistical analysis of the temporaldistribution of the signals estimated from the watermarkedframes. Since the logo pattern is temporally permutated usinga secret key in order to provide the security, an unauthorizeddetector cannot easily identify the embedded logo. In additionto the logo pattern, the proposed method embeds a temporalsync pattern to find the starting point of the permutationfor the logo pattern. In the logo extracting process, afterdetecting the starting point, the embedded logo is visualized byaccumulating the estimated logo patterns based on the secretkey and post-processing the accumulated result. The proposedmethod blindly detects the watermark logo without an originalvideo and takes into account geometrically mixed attacks aswell as temporal attacks such as frame rate conversion andtemporal clipping. Also, the low complexity of our methodmakes it possible to be processed in real-time for HD videos.The rest of the paper is organized as follows. Section II

introduces the basic principle for the visualization of theinvisible signal embedded by the SS method. In section III, thedetails of the proposed watermarking method are described.In section IV, we present experimental results showing theeffectiveness of the proposed method. Finally, Section Vprovides concluding remarks.

II. VISUALIZATION OF SIGNALS IMPERCEPTIBLYEMBEDDED BY SS METHOD

For video watermarking, a watermark signal is repetitivelyembedded into the equal location within the spatial domainof every frame on the basis of the additive SS watermarkingscheme as shown in Eq. (1).

I ′ (i, j, k) = I (i, j, k) + λ · w (i, j) (1)

where I (i, j, k) is the pixel value in (i, j) coordinate of k thframe and I ′ is the watermarked result. Also, λ and w are

Fig. 1. The distribution of the host noises in the temporal axis

the embedding strength and a watermark signal, respectively.Each element of the watermark signal is ‘-1’ or ‘1’ generatedby a generator following N(0, 1). In general, the embeddedwatermark is estimated using a de-noising filter F as describedin Eq. (2).

I ′ (i, j, k)− F (I ′ (i, j, k)) = w̃ (i, j, k) + nh (i, j, k) (2)

where w̃ and nh are the estimated watermark and a hostnoise, respectively. The host noise includes an inherent noisein each frame and a noise caused by the imperfect operationof the de-noising filter, and obstructs the exact estimationof the watermark. The host noise can be approximated fromunwatermarked frames as follow.

I (i, j, k)− F (I (i, j, k)) ≈ nh (i, j, k) (3)

In order to analyze the temporal distribution of the host noise,the host noise was calculated from spatially same coordinatewithin each frame of an unwatermarked video. We used anadaptive Wiener filter as the de-noising filter. Figure 1 showsthat the histogram of the temporal distribution of the hostnoises follows a Laplacian distribution whose mean is zero.Since the distribution is symmetric about zero, the result of thetemporal accumulation of the host noise at a coordinate (i, j) isclose to zero if the number of the accumulated frames is largeenough. Thus, the temporal accumulation in the region withand without a watermark signal results in following results.◦ In the unwatermarked regionm∑

k=0

[I (i, j, k)− F (I (i, j, k))] ≈m∑

k=0

nh (i, j, k) ≈ 0

◦ In the watermarked regionm∑

k=0

[I ′ (i, j, k)− F (I ′ (i, j, k))] =m∑

k=0

[w̃ (i, j, k) + nh (i, j, k)]

=m∑

k=0

w̃ (i, j, k) +m∑

k=0

nh (i, j, k) ≈m∑

k=0

w̃ (i, j, k)

≈ α (i, j) · w (i, j)(4)

where m is the number of the accumulated frames, andα is a sequence of positive real numbers. The larger thenumber of the accumulated frames is, the larger the valueof the α element is. In other words, the accumulating resultin the unwatermarked region is close to zero, and it in thewatermarked region is a positive or negative large valueaccording to the sign of the embedded watermark element.Then, we can distinguish between the marked and unmarkedregions by using a threshold value. The thresholding methodwill be specifically described in the post-processing step ofsection III-C. Although the proposed method has additionalprocesses for the visualization of the hidden logo signal, it isdesigned on the basis of the above mentioned principle.

III. PROPOSED WATERMARKING METHODA. The Region of Interest and the Temporal Interval forWatermarkingThe proposed method embeds both the logo pattern and the

temporal sync pattern into each region of interest (ROI) in

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Fig. 2. The ROIs (dotted squares : embedding ROIs / red squares : detectingROIs)

the luminance channel of every frame. As shown in Fig. 2,we denote the embedding ROI and detecting ROI for the logopattern and those for the temporal sync pattern by ROIE LP ,ROID LP , ROIE TP , and ROID TP , respectively. The em-bedding and detecting ROI size of each pattern is differenteach other. In the case of logo pattern, the size of thedetecting ROI is larger than it of the embedding ROI becausethe detecting ROI should include the entire region of themoved logo even if the geometrical distortions happen to theembedded video, as shown in Fig. 2(c). On the contrary to this,the size of the detecting ROI for the temporal sync pattern issmaller than it of the embedding ROI, because the proposeddetection method exploits the part of the embedded temporalsync pattern. If possible, the ROID TP should solely containthe part of the embedded temporal sync pattern in spite ofgeometrical distortions as shown in Fig. 2(c).We adopt redundant embedding and cumulative detecting

system [9] in order to increase the accuracy of the estima-tion for the embedded patterns. Each pattern is redundantlyembedded into consecutive frames during pre-defined secondsand cumulatively estimated from those frames. In this paper,the basic set of consecutive frames is called as an interval.The frames within same interval have the same patterns.

B. Embedding ProcessFigure 3 describes the proposed embedding procedure. The

inputs are the luminance channel of the frames and the binarylogo that consists of background area (black: 0) and symbolarea (white: 1). The proposed method embeds the logo patternand the temporal sync pattern. The logo pattern representscopyright information as a watermark. Since the logo patternis scrambled in the temporal axis of the video, the temporalsynchronization is very important to exactly extract the logopattern. The temporal sync pattern is to solve the temporalsynchronizing problem. The detail of embedding process is asfollows.Step 1: the generation of the logo pattern, the temporal

sync pattern, and a binary sequence. In the case of the logo

Fig. 3. Embedding procedure

pattern, 2-D random noise-like signal S following N(0, 1) isfirstly generated. Its size is same as the size of the binary logo.Then, the logo pattern LP is made by modulating the binarylogo L, which consists of ‘0’ and ‘1’, by the 2-D randomnoise-like signal S as shown in Eq. (5).

LP (i1, j1) = L (i1, j1)× S (i1, j1) (5)

where (i1, j1) is the spatial coordinate within the ROIE LP .Since the values of the symbol area and the background areawithin the logo L are respectively 1 and 0, the logo pattern LPhas the signals in only coordinates within the symbol area.The temporal sync pattern TP is a 2-D random noise-like

signal following N(0, 1). The size of the generated temporalsync pattern is the same as it of ROIE TP .A binary sequence BS, which consists ‘1’ and ‘-1’, is

generated using a secret key in order to temporally permutatethe logo pattern. Since the binary sequence is randomlycreated by a key-dependent pseudo-random number generatorfollowing N(0, 1), the proportions of ‘1’ and ‘-1’ in thegenerated sequence BS are similar.Step 2: perceptual modeling. The insertion of the patterns

should not lower the perceptual quality of the video. We adopta perceptual masking model, which controls the embeddingstrength by exploiting the noise visibility function (NVF) [10].The local weighing factor λ is calculated by

λ (i, j) = (1− nvf (i, j)) · S0 + nvf (i, j) · S1 (6)

where S0 and S1 are the upper bound in edged and texturedregions and the lower bound in flat and smooth regions,respectively. This process is performed in the both ROIE LP

and ROIE TP . The nvf is given by

nvf (i, j) =1

1 + (D/σ2max) · σ2 (i, j)

(7)

where σ2 and D are a local variance and a scaling constant,respectively. σ2

max is the maximum of the local variance.Step 3: the insertion of the logo pattern. The logo pattern

LP is embedded by multiplying with the local weighting fac-tor λ after controlling its direction according to the generatedbinary sequence BS.

I ′(i1, j1) = I(i1, j1) + λ(i1, j1) · LP (i1, j1) ·BS(l) (8)

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where I is the luminance data within the ROIE LP and I ′ isthe embedded result. l is the index of the binary sequence.Step 4: the insertion of the temporal sync pattern. The

temporal sync pattern TP is embedded as its negative signalduring the starting interval, which means that the index of thebinary sequence is ‘0’. In the other intervals, it is embeddedas its positive signal. More mathematically, we have

I ′(i2, j2) =

{I(i2, j2) + λ(i2, j2) · TP (i2, j2) · (−1), if l = 0I(i2, j2) + λ(i2, j2) · TP (i2, j2) · (1), if l �= 0

(9)where (i2, j2) is the spatial coordinate within the ROIE TP .(Step 2) ∼ (Step 4) are performed in every frame. The

index l of the binary sequence is increased when the intervalis changed. If the basic interval is t seconds and the framerate of the target video is 30 fps (frame per sec.), the index isrenewed every 30× t frames. When the index reaches the lastvalue, it is initialized to ‘0’. The basic embedding interval (tsec.) and the length of binary sequence are sent to a detectoras side information after the embedding process is finished.

C. Detecting ProcessThe extraction of the embedded watermark logo is processed

in two passes, as shown in Fig. 4. In the first pass, a referencepattern to be used for correlation in the second pass isobtained. In the second pass, the extraction of the embeddedwatermark logo is performed after the starting interval is foundby correlating the obtained reference pattern with the patternestimated from the ROID TP .(First pass)It is very important to find the starting interval of the

insertion for the logo watermark because the logo pattern istemporally permutated by a secret key. Since the temporalsync pattern is embedded as its negative signal in the onlystarting interval and as its positive signal in the remainingintervals, the starting interval can be known by finding thenegative correlating peak in the ROID TP . The correlationis performed between the pattern estimated from the framesand a reference pattern. Unlike the traditional method usingan original reference pattern, the reference pattern is extracted

Fig. 4. Detecting procedure

from the marked video. We obtain the reference pattern byaccumulating the patterns estimated from the ROID TP ofall frames in the watermarked video. Since the temporal syncpattern is embedded as its positive pattern in most intervalsexcept for the starting interval, the accumulating result issimilar to the part of the positive pattern. In each frame,the embedded pattern is estimated by an adaptive Wienerfilter [11] as the de-noising filter, because the patterns areembedded like invisible noise by the additive method. Wedenote the obtained reference pattern by RP .(Second pass)The basic time interval for the second pass is half (t/2 sec.)

of the basic interval for the embedding process. This makesit possible to find more exactly the starting interval even ifa temporal attack such as clipping happens. As described inFig. 5, when the front frames of the embedded video are lostby the clipping, the half interval is more efficient for findingthe starting interval. The second pass is processed by followingsteps.Step 1: Finding the starting interval. First, the part of

the embedded temporal sync pattern is estimated from theROID TP using the adaptive Wiener filter, and the estimatedpattern is accumulated for t/2 seconds. The accumulated resultAPT is correlated with the reference pattern RP to find thestarting interval. The normalized correlation is calculated by

NC =

R−1∑i′2=0

C−1∑j′2=0

RP (i′2, j′2) ·APT (i

′2, j

′2)

√R−1∑i′2=0

C−1∑j′2=0

(RP (i′2, j′2))

2 ·√

R−1∑i′2=0

C−1∑j′2=0

(APT (i′2, j′2))

2

(10)where R and C denotes the height and width of the ROID TP

square, respectively. (i′2, j′2) is the spatial coordinate withinthe ROID TP . Since the reference pattern is obtained fromthe ROID TP in the first pass, it always has spatial syncwith the APT estimated from the ROID TP . Thus, it ispossible to get the stable correlation result in spite of variousgeometrical distortions. This process is repeated in the nextintervals (t/2 sec.) until the normalized correlation result NCis smaller than a pre-defined negative threshold. The thresholdcan be computed by approximate Gaussian method [12]. Weset the threshold to 0.089 so that the false positive errorprobability is about 10−8. When the negative peak is obtained,the normalized correlation is calculated one more time in

Fig. 5. Embedding interval and detecting interval

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Image ProcessingImage Analysis 221

the next interval (t/2 sec.), and then the interval that hassmaller negative one of the two results is chosen as the startinginterval.Step 2: The accumulation of the logo patterns. The

accumulation of the logo pattern is started in the startinginterval. The logo pattern is estimated from the ROID LP

using the adaptive Wiener filter. The estimated patterns areaccumulated for t/2 seconds. The accumulated result APL

is added to a watermark logo buffer LB after modulating itaccording to the binary sequence BS generated by the secretkey.

LBnext(i′1, j

′1) = LB(i′1, j

′1) +BS(l) ·APL(i

′1, j

′1) (11)

where l is the index of the binary sequence and increasesevery interval. (i′1, j′1) is the spatial coordinate within theROID LP . The next interval for the accumulation is t/2seconds away from the current interval, as shown in Fig. 5.This accumulation is performed until the l reaches the end.Step 3: The post-processes for visualizing the accumu-

lated logo pattern. The data of the watermark logo bufferis visualized by post-processes. The basic principle of thevisualization was introduced in section II. First, a thresholdfor the binarization is obtained with respect to the absolutevalues of LB by exploiting Otsu method [13], and the data isbinarized by the threshold τ .

L′(i′1, j′1) =

{0, if |LB(i′1, j

′1)| ≤ τ

1, if |LB(i′1, j′1)| > τ

(12)

where L′ is the binarized result. However, the binarized resultmay be noisy in the background and have some holes in thesymbol area. The noises in background are removed by themedian filter. Then, the dilation as a morphological algorithmis applied to the median-filtered result in order to fills the holesin the symbol area.

IV. EXPERIMENTAL RESULTSExtensive experiments were conducted to measure imper-

ceptibility, robustness, security, and real-time performanceof the proposed method. The test videos were three Full-HD MPEG-2 videos, which are 8 minutes in length and30 fps in frame rate. The videos are from drama, ac-tion movie, and music show, as shown in Fig. 6 . The400×120 binary logo was embedded, and the size of theROIE LP , ROID LP , ROIE TP , and ROID TP was respec-tively 400×120, 600×200, 192×192, and 64×64 in Full-HDresolution. The basic embedding interval (t) was 1 sec. andthe length of binary sequence BS was 200.

(a) drama (b) action movie (c) music show

Fig. 6. Test videos

A. Invisibility Test

The quality of the watermarked video was tested usingpeak signal-to-noise ratio (PSNR) and the structural similarity(SSIM) [14] as objective measure. In this test, we set the S0

and S1 of the NVF function to 3 and 1, respectively. After theembedding, the average PSNR and SSIM values in embeddingROIs were 46.2 dB / 0.9992 for drama, 47.0 dB / 0.9989 foraction movie, and 45.6 dB / 0.9992 for music show. In additionto the objective test, the double-stimulus impairment scalemethod in the ITU-R Rec. 500-11 [15] was exploited as sub-jective measure. The subjective test involved ten participants,who are familiar with the proposed method and are able todetect visual artifacts. The videos were displayed on SamsungPAVV 650 LCD TV (46-inch) in the room of 5-lx brightness.They watched the videos, once without and once with thewatermark, at the five times distance of the TV screen height,and were asked to score on the videos with the watermark.The range of the test score is from 1.0 (very annoying) to5.0 (imperceptible). The result scores were 4.7 for drama, 4.8for action movie, and 4.7 for music show. These results provethat the proposed method guarantees the visual quality of thewatermarked videos.

B. Robustness Test

To evaluate the robustness of our method, we tried to detectthe watermark logo from the variously attacked videos, whichwere watermarked by the embedding strength mentioned inSection IV-A. Figure 7 shows the test results about geo-metrically and temporally mixed attacks. The geometricallymixed attack is the mixture of MPEG-4 recompression, scal-ing 50%, rotation 5◦, and translation by 20 pixels in rightand bottom direction. The temporally mixed attack includesMPEG-4 recompression, frame-rate conversion from 30 fps to24 fps, and temporal clipping. The temporal clipping meansthat the watermarked videos were removed by each 1 min.in front part and rear part of the videos, and consequentlyhad 6 min. running time. As shown in Fig. 7 (b) ∼ (g),the extracted binary logo can be identified in spite of thesevere mixed attacks. The proposed method can extract theembedded logo even though marked videos are geometricallydistorted, because the logo patterns are embedded into spatiallysame location of every frame and the embedded patterns areequally distorted by geometrical distortions and consequentlythe embedded patterns always keep the spatial synch eachother. Also, since the proposed method can find the startinginterval for the pattern insertion by exploiting the temporalsync pattern, it is robust against temporal attacks.

C. Security Test

The logo watermark embedded by the proposed methodshould be only identified by a user who owns the secret key. Totest the security, we tried to detect the logo watermark usingdifferent keys. Figure 7 (h) ∼ (j) shows the detected results,which look like a random noise and do not represent theembedded logo. The logo watermark could be only extracted

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(a) original Logo

(b) drama (c) action movie (d) music show

(e) drama (f) action movie (g) music show

(h) drama (i) action movie (j) music show

Fig. 7. Test results: (b)∼(d) results extracted from geometrically mixedattacked videos / (e)∼(g) results extracted from temporally mixed attackedvideos / (h)∼(j) security test results

TABLE IRESULTS OF COMPUTATIONAL COMPLEXITY TEST (SEC. PER FRAME)

Decoding Watermarking Display TotalTime Process Time Time Time

Embedding0.0091

0.00140.0029

0.0134

Detecting 1st pass 0.0001 0.01212nd pass 0.0032 0.0152

in the case using the correct key. The longer the length of thebinary sequence is, the securer the proposed method is.

D. Real-time Performance TestSince the proposed method mainly focuses on the HD

videos, the real-time process is very important. We imple-mented our method based on Intel integrated performanceprimitives library, and tested on Intel Core-2 quad CPU 2.5GHz, 4 GB RAM. In order for the method to be processedin real-time, three sub-functions for decoding video streams,embedding or detecting the patterns, and displaying the videoshould be processed within 0.03 sec/frame at the 30 fps video.Table I shows the average processing time results for the Full-HD videos. The results prove that the embedding process hasthe real-time performance. Although it is impossible for thedetector to be processed in real-time because of the two-passscanning, the time results prove that each pass is fast enough.

V. CONCLUSION

In this paper, a practical logo watermarking scheme for HDvideos was presented. The proposed method invisibly embedsthe logo watermark based on the SS method. The embeddedlogo pattern is visualized using the temporally statistical dif-ference between signals estimated from the watermarked andunwatermarked regions within each frame. The logo patternto be embedded is temporally permutated by a secret key toguarantee its security. The proposed method also embeds a

temporal sync pattern in every frame. This pattern makes itpossible to find the starting interval of the permutation forthe embedded logo pattern. After finding the starting intervalby exploiting the temporal sync pattern, the watermark logocan be visualized by the accumulation based on the secret keyand the post-processes. Extensive experimental results provethat the proposed method satisfies imperceptibility, robustness,security, and real-time performance.

ACKNOWLEDGMENTThis research project was supported by Ministry of Cul-

ture, Sports and Tourism(MCST) and from Korea CopyrightCommission in 2011.

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