real-time compresseddomain video watermarking resistance to geometric distortions

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Real-Time

Compressed-Domain VideoWatermarking

Resistance toGeometricDistortions

Liyun Wang, Hefei Ling, Fuhao Zou, and Zhengding LuHuazhong University of Science and Technology, China

Digital watermarking has been an

important technique for the

copyright protection that embeds

copyright information into the

digital works. In video watermarking, the water-

mark can be added either to uncompressed data

or compressed video streams. Practical video

storage and distribution systems store and

transmit the video sequences in compressed

format, such as using a video on demand

(VoD) service system. In these cases, the water-

mark should be embedded into the compressed

video data to avoid the process of fully decod-

ing and encoding.

The geometric attacks in videos can be eas-

ily implemented by using a nonlinear editor.

However, they are difficult to handle because

they can desynchronize the watermark infor-

mation. Besides resisting geometric distor-

tions, most video watermarking applications

require the watermark to be embedded and

detected in real time. This article focuses on

the video watermarking used for copyright

protection in VoD applications, where both

real-time performance and resistance to geo-

metric distortions are important require-

ments. (See the Related Work in Digital

Watermarking sidebar for previous research.)

Because the histogram shape of the low-

frequency subband of the discrete wavelet

transform (DWT) is invariant to rotation, scal-

ing, and other geometric distortions, we pro-

pose a method to embed the watermark into

histogram bins of frames in the one-level

DWT domain. The video data are partially

decoded to obtain block discrete cosine trans-

form (DCT) coefficients. Which are subse-

quently used to construct one-level DWT. To

lower the computational complexity, we use

a fast intertransformation between one-levelDWT and block DCTs. Thus, our method can

resist many geometric distortions and meet

the real-time requirement.

The main contributions of this work are as

follows. First, we have proposed a geometrically

invariant watermarking method by exploiting

the fact that the histogram shape of the low-

frequency subband in DWT domain is insensi-

tive to various geometric distortions. Second,

we use a fast intertransformation to obtain

the DWT coefficients directly from the com-

pressed data instead of using the traditional

method that first decompresses the block

DCTs of frames into pixel data and then applies

DWT to these data. Thus, we significantly re-

duce the computational cost and meet the

real-time requirement.

Basic Principles

Compared with DCT, the Wavelet transform

is closer to the human visual system (HVS)

becuase it splits the input image into several

statistically frequency bands that can be pro-cessed independently. DWT also causes fewer

visual artifacts than DCT because the wavelet

transform does not decompose the image

into blocks for processing.1

In addition, an images histogram in the spa-

tial domain is approximately invariant to geo-

metric attacks. We can extend this invariance

property to the low-frequency subband of the

DWT domain in order to design a geometric-

invariant watermarking method.

Most compressed video data are stored as

block DCT coefficients and motion vectors.

Multimedia in Forensics, Security,and Intelligence

A proposed real-time

video watermarking

scheme is

transparent and

robust to geometric

distortions, including

rotation with

cropping, scaling,

aspect ratio change,frame dropping, and

swapping.

1070-986X/12/$31.00 c 2012 IEEE Published by the IEEE Computer Society70


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Equation 2 only gives us a block of the image

X, so the whole image can be expressed as

X

B1 0 00 B1 0..

...

..

.

.

0

0 0 0 B1

266664

377775

1

LSLS

C11 C12 C1MC21 C22 C2M

.

.

...

. ...

CL1 CL2 CLM

266664

377775

BT1 0 00 BT1 0..

...

..

.

.

0

0 0 0 BT1

2666664

3777775

1

MSMS

3

The three matrices on the right of Equation 3are denoted as B4, Cpartand B5, respectively.

We can also compute the one-level DWT

coefficients of image X. Here, the DWT will be

taken using the Haar wavelet, which is the sim-

plest possible wavelet. It is both separable and

symmetric and can be expressed in matrix form

RHXQT (4)where Hand Qare transformation matrices.

For the Haar wavelet transform, Hcontains

the Haar basis functions, hk(z). They are

defined over the continuous, closed interval

z2 [0, 1] for k 0, 1, 2, . . ., LS 1, whereLS 2e. To generate H, we define the integerk such that k 2p q 1, where 0 pe 1, 0 q e 1, q 0 or 1 for p 0,and 1q2p for p6 0. Then the Haar basisfunctions are

h0z h00z 1ffiffiffiffiffiffiLS

p ; z2 0; 1 5

and

hkz hpqz

1ffiffiffiffiffiffiLS

p2p=2; q 12p z


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To evaluate the invariance to geometric dis-

tortions of the histogram shape in the DWT do-

main, we compute the relative relations of each

two successive bins in the number of low-

frequency DWT coefficients, denoted by A(k).

Ak

Gk 1Gk

; 1

k

Lg

1

10

whereG is the histogram vector and Lgis the

number of bins. We took one frame (of size

720 480) from the carriage test video as an ex-ample. We implemented four typical geometric

distortions including rotation, scaling, aspect

ratio change, and cropping. Figure 1 shows

that the relative relations in the number of

DWT coefficients among groups of two neigh-

boring bins are relatively stable under these ge-

ometric distortions. This means the histogram

shape is invariant to various geometric distor-tions, which implies that if we embed the

watermark based on the relative relations we

can expect the watermark to resist those geo-

metric distortions.

Proposed Scheme

In this section, we will introduce our video

watermarking algorithm. We first describe

the watermark embedding and then present

the watermark detection.

Watermark Embedding

Because the watermark-embedding process is

performed in the one-level DWT domain, the

compressed video should be partially decoded

to obtain the 2D block DCT coefficients of the

frames luminance. For P- and B-frames, the

interblock should be updated by adding its ref-

erence block in I- or P-frames. For intrablocks

in P-frames, no updating is necessary.

Figure 2 shows the watermark-embedding

process. In one video sequence, continuous

frames are chosen to form a basic carrier unitfor watermark embedding, which we call the

watermark minimal sequence (WMS). For each

frame in one WMS, we compute the DWT coef-

ficients directly from the block DCTs. Then, we

embed the watermark into the histogram bins

calculated from the low-frequency subband of

the DWT domain.

The binary watermark is denoted as W {wi | i 1, 2, . . .,Lw}. Each bit ofWis either 1 or 0, andLw is the watermark length. The watermark W

is divided into Fequal-sized segments, each of

which is embedded into the histogram shape

of one frame in each WMS, in order. The

steps of the embedding process are as follows.

Step 1. For each WMS, we calculate the

one-level DWT coefficient matrix of every

frame from the block DCTs using Equation 8.

Step 2. We compute the histogram shape

in the DWT domain and acquire the number of

coefficients in each bin.

Figure 1. The effect of the geometric distortions on the histogram shape. We

implemented four typical geometric distortions: (a) rotation, (b) scaling,

(c) aspect ratio change (d), and cropping. The relative relations in the number

of discrete wavelet transform (DWT) coefficients among groups of two

neighboring bins are relatively stable under these geometric distortions.

5 10 15 20 25 300

1

2

3

4

nBINs

A(k)

20rotation

Original

5 10 15 20 25 300

1

2

3

4

nBINs

A(k)

Scaling of 0.8

Original

5 10 15 20 25 300

1

2

3

4

nBINs

A(k)

10% cropping

Original

5 10 15 20 25 300

1

2

3

4

nBINs

A(k)

Aspect to 4:3Original

(a)

(b)

(c)

(d)

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For low-frequency subband coefficients for

every frame, the average value is calculated as V.

An embedding range U 1 V; 1 V is determined, where is a parameter set to

0.6. The histogram vector produced is denotedbyGq{gq(j) | j 1, 2, . . ., Lg}, 1q F. wheregq(j) is the number of coefficients in the jth bin

of theqth frame. In order to embed all the bits,

Lgshould be not less than 2Lw/F.

Step 3. We embed each watermark bit

into two neighboring bins by reassigning the

number of coefficients in the two bins. Let E1andE2be two consecutive bins in the extracted

histogram vector. These bins include gq(j) and

gq(j

1) coefficients, respectively. We control

the relative relation of the two bins in order

to embed one bit of information:

gqjgqj 1 T; ifwi1

gqj 1gqj T; ifwi0

( 11

where Tis a threshold that controls the number

of modified coefficients. We select the thresh-

old by considering the watermark robustness

performance and the embedding distortion.

Afterward, we embed one bit in two consecu-

tive bins. First, we consider the case when wiis 1.

If gq(j)/gq(j 1) T, no operation is needed.

Otherwise, ifgq(j)/gq(j1) < T, some randomlyselected coefficients will be moved to E1 from

E2, satisfying gq(j)0/gq(j1)0 T.n1denotes the

number of these selected coefficients. Then, if

wi is 0 and gq(j 1)/gq(j) < T, some randomlyselected coefficients will be moved to E2 from

E1, satisfying gq(j 1)00/gq(j)00 T. n2 denotesthe number of these selected coefficients. This

is the rule for reassigning the coefficients:

c1mi c1i M; 1in1c2mj c2j M; 1jn2

( 12

wherec1(i) andc2(j) are theith andjth selected

coefficients inE2and E1, respectively.Mis the

bin width. The modified c1m(i) and c2m(j) be-

long to E1 and E2, respectively. n1 and n2 canbe calculated as follows:

n1 Tgqj gqj 11 T

n2 Tgqj 1 gqj1 T

( 13

We repeat this procedure until watermark bits

are embedded in the corresponding frame in

one WMS.

Step 4. The modified differences of all

DWT coefficients are inversely transformed

to the modified differences of block DCT

BlockDCTs to

DWT

Co

efficients

mo

dification

WatermarkW

Low-frequencysubband modified

coefficients ofDWT

+

All the coefficients arezeros except the modified

coefficients

DWTto block

DCTs

+

+

Block DCTdata

(after DQT)

FContinuous video frames areselected

Differenceof the low-frequency

subbandof DWT

Watermarkedframes

(block DCT data)

Differenceof blockDCT data

Low-frequencysubband

coefficients ofDWT

Extracting

histogram

Figure 2. The

watermark-embedding

process. For each frame

in one watermark

minimal sequence

(WMS), we compute

the discrete wavelet

transform (DWT)

coefficients directly

from the block discrete

cosine transform (DCT)

coefficients.

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extracted watermark is. Ifis smaller than the

BER thresholdBER, we can successfully extractthe watermark in the WMS. Otherwise, there is

no watermark hidden in the WMS. Then, we

slide the window onto the next frame to

form a new WMS.

Computational Complexity Analysis

A watermarking schemes real-time performance

is inversely proportional to its computational

complexity. Most video watermarking applica-

tions require real-time performance, so the

watermarking algorithms complexity should

be as low as possible.In our proposed algorithm, the whole water-

mark-embedding process includes partial

decoding, which consists of variable length

decoding and dequantization, the intertrans-

formation between the DWT coefficients and

block DCTs, coefficient modification, and par-

tial encoding. Here we focus on the computa-

tional cost of the intertransformation because

it accounts for much of the time required for

the watermark embedding.

We compared our method based on inter-

transformation with the traditional method

using the IDCT and DWT. For the sake of con-

venience, suppose the size of one frame isN N.BothA1and A2are sparse matrices, which con-tributes to significant savings in computational

cost. Our fast method costsO(N2) time to com-

pute the DWT coefficients and transform the

watermarked DWT coefficients back to the

block DCTs, while the traditional method

costs O(N2 ffiffiffiffi

Np

) time. This means our method

isffiffiffiffi

Np

times faster than the traditional method,

which helps our proposed watermarking

scheme achieve a substantial savings in compu-

tational cost.

Experiments and Discussion

As Figure 4 shows, to test our proposed

scheme, we used four video sequences, all of

which are widely used for video watermarking

tests. The first two sequences are MPEG-2

encoded at 6 megabits per seconds (Mbps),

and the rest are MPEG-1 encoded at 2.2 and

1.5 Mbps, respectively. In the experiments,

the length of the embedded watermark is 60.

The threshold Tis set to 3. The BER threshold

BERis set to 0.23.

(b)(a)

(c) (d)

Figure 4. Four test

video sequences. The

(a) mobile and

(b) carriage sequences

are MPEG-2 encoded at

6 megabits per seconds

(Mbps), and the

(c) Paris and (d) farmer

sequences are MPEG-1

encoded at 2.2 and

1.5 Mbps, respectively.

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We can objectively assess the visual quality

by measuring the peak signal-to-noise ratio

(PSNR) of watermarked frames compared with

the original frames. The average PSNR values

of four watermarked video sequences are 39,

40, 39, and 48 decibels (dB), separately. All

the values are higher than 37 dB. Perceptually,

the original video and the watermarked video

are visually indistinguishable. This implies

that the watermarking scheme can achieve

visual transparence.

Estimation of robustness

We implemented a video watermarking attack

tool named VBMark based on the VirtualDub

program to modify the watermarked videos

with various types of attacks. We considered

the watermarking scheme to be robust if the

computed BER is less than the threshold BER(0.23).

Commonly, the cropping rotations are

slight for video signals and the rotation angles

are no more than 5 degrees. When the rotated

angles gradually increase to 35 degrees, the

BER values remain less than the threshold BER.

In our experiment, we also applied several

scale factors0.7, 0.9, 1.2, and 1.5to the

test video signals.

Frame aspect ratio changes convert the size

of the target video frame. Digital frames come

in several aspect ratios. The most common are

4:3, 11:9, and 16:9. We applied aspect ratios

to the test video signals that differed from

their original ratio.

Table 1 shows the experimental results of

each robustness measure: cropping rotations,

scaling, frame aspect ratio changes, and Gaus-

sian low-pass filtering. In each case, the BER

values are less than the threshold BER, indicat-

ing that our algorithm is robust to these attacks.

Because the watermark is embedded in one

WMS in each GOP repeatedly, it can bedetected successfully even if only one WMS is

left. That means a frame-dropping attack is

not a threat to our proposed method. In our

experiment, we dropped two frames and bor-

rowed two frames from the next GOP. As

Table 1 shows, the BER is equal to zero, which

means we can still extract the watermark

correctly.

Frame swappinginvolves switching the order

of frames randomly within one GOP. However,

too many frame swaps will degrade video qual-

ity. Therefore, we swapped frames three times

during our experiments. In fact, this caused

no significant changes within one GOP. Three

swaps do not cause much difference in tempo-

ral frequency domain. Thus, as Table 1 shows,

our scheme is robust against frame swapping.

In our last robustness test, we measured

robustness against file-format conversion,

which is significant because a video datas file

format is easily changed by some software

tools. Our test watermarked video sequences

were originally in MPEG-2 or MPEG-1 formats.

We converted them into the MPEG-4, Divx,Xvid, and H.264 formats and extracted the

watermark. The BER is nearly reaches zero

(see Table 1), which suggests that our algo-

rithm is robust against common file-format

conversions.

Robustness Performance Comparison

We compared our scheme to the algorithm

proposed by Yulin Wang and Alan Pearmain

(Wangs algorithm), which is a typical video

watermarking algorithms resistant to geomet-

ric attacks.3 Our method proved robust to the

Table 1. Experimental results for four watermarked sequences.

Average bit error rate (%)

Attack Mobile Carriage Paris Farmer

Rotation with cropping (1) 0.0 0.0 0.0 0.0


Rotation with cropping (5) 0.0 0.0 0.0 0.0Rotation with cropping (10) 0.0 0.0 0.0 0.0






Scaling to 0.7 1.7 0.0 3.3 3.3

Scaling to 0.9 0.0 0.0 0.0 1.7

Scaling to 1.2 0.0 0.0 0.0 0.0

Scaling to 1.5 0.0 0.0 0.0 0.0

Aspect to 4:3 0.0 0.0 0.0 0.0

Aspect to 11:9 0.0 0.0 0.0 0.0

Aspect to 16:9 0.0 0.0 0.0 0.0

Guassian low-pass filtering 0.0 0.0 0.0 3.3

Frame dropping 0.0 0.0 0.0 0.0

Frame swapping 0.0 0.0 0.0 0.0

MPEG-4 compression (3,000 Kbps) 0.0 0.0 0.0 1.7

Xvid compression (2,500 Kbps) 0.0 0.0 0.0 1.7

Dvid compression (2,000 Kbps) 1.7 1.7 1.7 3.3

H.264 compression (1,000 Kbps) 3.3 4.6 3.3 6.7

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attacks listed in Tables 1 and 2, and it outper-

forms Wangs algorithm. For rotation (RST)

attacks, the average BER of our method wasmuch lower thanBER, even when the rotation

angle is up to 35 degrees. The average error

rate for the Wangs algorithm was 2.13 percent

for a 1 degree rotation, but it failed when the

rotation angle was larger than 2 degrees.

Both algorithms are robust to rescaling.

However, our method can resist an aspect

ratio attack, with an average BER of approxi-

mately 0 percent, but the Wangs method can-

not resist this attack.

For the performance against video compres-

sion, format conversion, and other specialized

attacks, both method can resist format conver-

sion and frame dropping. However, the Wangs

algorithm is sensitive to frame swapping while

our method is not.

Real-Time Performance

In our final measure, we used the normal

decoding as a baseline to check whether the

watermark embedding and detection can

achieve real-time performance rates. We con-

sidered the watermarking process acceptable

if the consumed time is less than that of the

normal decoding because then can be finished

when the decoding ends.

We compared our process with the DEW4

and Wangs algorithms.3 DEW has demon-

strated excellent performance in real time, but

it is vulnerable to geometric distortions. The

DEW algorithm also has a low complexity be-cause it embeds the watermark by shifting the

end of block (EOB) marker, which avoids

re-encoding.

In the real-time experiments, we used three

schemes to embed the same watermark into

the carriage video sequence and then

attempted to detect the watermark. Figure 5

shows the consumed time of each method

and the normal decoding time of the test

video. It shows that all methods meet the

real-time requirements. The DEW algorithm

only consumes 353 and 324 ms, respectively,

during the watermark embedding and detec-

tion, while the Wangs algorithm took 1,362

and 1,521 ms, respectively. Although the con-

sumed time of our proposed algorithm during

the watermark embedding and detection was

about 1,032 and 857 ms, respectively, our

schemes consumed time is still less than half

of that of the normal decoding, which means

the watermark embedding and detection pro-

cesses can meet the real-time requirement.

The DEW algorithm outperformed our methodbecause it doesnt take into consideration resis-

tance to the geometric distortions.

Security Analysis

In our proposed scheme, we achieve robustness

using the invariance of the histogram shape of

the low-frequency subband coefficients of the

one-level DWT domain. The watermark embed-

ding is designed by modulating the relative

relations of each two successive bins in the

number of low-frequency DWT coefficients.

Assuming the bin width is set to an appropriate

Figure 5. Time consumed during the watermark embedding and detection

process for a carriage sequence. The experimentwas done using a PC with

a 2.8-Gbyte CPU and 512M DDR2 memory.

Table 2. Robustness performance comparison with Wangs method.3

Average bit error rate (%)

Attack Our method Wangs method*

Rotation with cropping (1) 0.0 2.13

Rotation with cropping (2) 0.0




Scale to 0.7 1.7 0.0

Scale to 0.9 0.0 0.0

Aspect to 11:9 0

Aspect to 4:3 0.0

Format conversion 0.0 0.0

Frame dropping 0.0 0.0

Frame swapping 0.0

* The indicates that the watermark detection failed.

0Watermark embedding Watermark detection Normal decoding

500

1,000

1,500

2,000

Consumedtime(ms)

2,500

3,000

3,500

4,000

Our method

Wang's method

Dew

Full decoding

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Our real-time video

watermarking scheme

is suitable for other

DCT-based compressed

videos because the DWT

domain could be directly

acquired from block

DCTs of any size.

value and it is unknown, an adversary attempt-ing to remove the watermark by modifyingcoefficients randomly will fail. Because randommodifications are unlikely to significantlychange the number of low-frequency DWTcoefficients in each bin, the relative relationsof each two successive bins will be unchanged.This implies that the watermark will be stillextracted correctly in the watermark-detectionprocess. Consequently, the security of ourscheme has been guaranteed.

ConclusionIn this work, we presented a real-time video

watermarking scheme with high robustness in

the compressed domain. Although we only

tested our proposed scheme on video in the

MPEG-1 and MPEG-2 format, it is suitable for

other DCT-based compressed videos such as

MPEG-4 and H.264 because the DWT domain

could be directly acquired from block DCTs

of any size. This algorithm can be used for

data hiding in many applications such as

authentication and copyright protection. In

the future, we will consider other attackssuch as camera capturing. We will also adapt

the algorithm for video data in MPEG-4 and

H.264 format. MM

Acknowledgments

This work is supported by the National Science

Foundation of China under grants 60873226

and 60803112, the Fundamental Research

Funds for the Central Universities, and the

Wuhan Youth Science and Technology Chen-

guang Program.

References

1. X.Y. Wang and H. Zhao, A Novel Synchroniza-

tion Invariant Audio Watermarking Scheme Based

on DWT and DCT, IEEE Trans. Signal Processing,

vol. 54, no. 12, 2006, pp. 4835-4840.

2. B.J. Davis and S.H. Nawab, The Relationship of

Transform Coefficients for Differing Transformsand/or Differing Subblock Sizes, IEEE Trans.

Signal Processing, vol. 52, no. 5, 2004,

pp. 1458-1461.

3. Y. Wang and A. Pearmain, Blind MPEG-2 Video

Watermarking Robust Against Geometric Attacks:

A Set of Approaches in DCT Domain,IEEE

Trans. Image Processing, vol. 15, no. 6, 2006,

pp. 1536-1543.

4. G.C. Langelaar and R.L. Lagendijk, Optimal Dif-

ferential Energy Watermarking of DCT Encoded

Images and Video, IEEE Trans. Image Processing,

vol. 10, no. 1, 2001, pp. 148-158.

Liyun Wangis a PhD student in computer science at

the Huazhong University of Science and Technology,

China. Her research interests include digital finger-

printing and digital rights management. Wang has

a BE in computer science from Huazhong University

of Science and Technology. Contact her at

[email protected].

Hefei Lingis an associate professor in the College of

Computer Science at the Huazhong University of

Science and Technology, China. His research interests

include copy and near-duplicate detection, digital

watermarking and fingerprinting, and content secu-

rity and protection. Ling has a PhD in computer

science from the Huazhong University of Science

and Technology. He is a member of IEEE. Contact

him at [email protected] (corresponding author).

Fuhao Zouis an associate professor in the College of

Computer Science at the Huazhong University of

Science and Technology, China. His research interests

include copy and near-duplicate detection. Zou has aPhD in computer science from the Huazhong Univer-

sity of Science and Technology. Contact him at

[email protected].

Zhengding Lu is a professor at the Huazhong Uni-

versity of Science and Technology, China. His

research interests include distributed computing,

distributed database systems, heterogeneous sys-

tem integration, and information security. Lu has

a PhD in computer science from the Huazhong

University of Science and Technology. Contact

him at [email protected].

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real-time compresseddomain video watermarking resistance to geometric distortions

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