ROBUST CONTENT-BASED VIDEO WATERMARKING

EXPLOITING MOTION ENTROPY MASKING EFFECT

Amir Houmansadr, Hamed Pirsiavash

Department of electrical engineering, Sharif University of Technology, Azadi Ave., Tehran, Iran

Shahrokh Ghaemmaghami

Electronics Research Center, Sharif University of Technology, Azadi Ave., Tehran, Iran

Keywords: Digital watermarking, Video sequence, Entropy masking.

Abstract: A major class of image and video watermarking algorithms, i.e. content-based watermarking, is based on

the concept of Human Visual System (HVS) in order to adapt more efficiently to the local characteristics of

the host signal. In this paper, a content-based video watermarking scheme is developed and the concept of

entropy masking effect is employed to significantly improve the use of the HVS model. Entropy masking

effect states that the human eye’s sensitivity decreases in high entropy regions, i.e. regions with spatial or

temporal complexity. The spatial entropy masking effect has been exploited in a number of previous works

in order to enhance the robustness of image-adaptive watermarks. In the current research, we use the

temporal entropy masking as well to achieve a higher performance in video watermarking. Experimental

results show that more robust watermarked video sequences are produced considering temporal entropy

masking effect, while the watermarks are still subjectively imperceptible. Robustness enhancement is a

function of temporal and spatial complexity of the host video sequences.

1 INTRODUCTION

During the past two decades, digital industries have

shown an explosive growth. Digital media has

captured homes and offices by a storm through the

internet and high quality digital disks such as CD’s

and DVD’s. Unfortunately, copyright protected

digital media can be easily copied and distributed

without permission of the content owner. This

causes a huge damage to the relevant industries

especially multimedia producing companies. As a

result, enforcement of the intellectual property rights

has become a critical issue in recent years and a vast

amount of fund has been allocated to research in this

area.

At the end of the 20

century, digital

watermarking was introduced as a complementary

solution for the aim of protecting digital owners’

rights. A digital watermark is a visible or an

invisible mark, which is embedded into the digital

media such as audio, image, and video to identify

the customer and/or providing additional

information about the media such as its producer, the

customer’s permissions, etc. Regarding the

applications, watermarking schemes are classified

into two major classes: source-based and

destination-based schemes (Podilchuk, 1998).

Source-based watermarking schemes in which a

watermark identifying the owner is inserted into

different distributions of a digital media aim the

ownership identification/authentication. This is

while destination-based schemes intend to trace

lawbreaking customers by inserting a distinct

watermark identifying each customer in the digital

production. In this category, it is assumed that the

original media is available during the detection

process, while in the first category the watermark

extraction should be performed just using the

watermarked media.

The embedded mark should have some

significant features to promise its functionality (Pan,

2004). The watermarked media must be perceptually

similar to the original host signal, i.e. the mark

existence in the media should be inaudible or

invisible (imperceptibility requirement). In addition,

252

Houmansadr A., Pirsiavash H. and Ghaemmaghami S. (2006).

ROBUST CONTENT-BASED VIDEO WATERMARKING EXPLOITING MOTION ENTROPY MASKING EFFECT.

In Proceedings of the International Conference on Signal Processing and Multimedia Applications, pages 252-259

DOI: 10.5220/0001572302520259

 SciTePress

the watermark should survive in the digital media

after performing various attacks. The watermarking

attacks consist of intentional attacks made to the

marked media to remove or change the watermark

and various signal processing schemes such as

compression, resampling, halftoning, cropping, etc.

This feature of the watermarking schemes is

addressed as robustness requirement. In the case of

destination-based watermarks, another characteristic

should also be considered defining how widely it

can be distributed. This is called watermark’s

capacity, which states the ability to detect different

watermarks with a low probability of error, as the

number of distinct watermarks increases. There is

always a tradeoff between these three requirements.

Different models of the Human Visual System

(HVS) have been proposed to be used in the image

and video compression applications. Due to the fact

that both compression algorithms and watermarking

schemes aim to find redundant data of the digital

media (to get removed in the former and to be used

for inserting watermark in the latter), visual models

employed for the compression applications can also

be used in the watermarking schemes.

To be imperceptible, an image or video

watermark should consider the characteristics of the

HVS. Depending on how HVS models are used,

watermarking schemes can be classified into two

major categories: image-independent and image-

adaptive (or generally content-based) watermarking

schemes (Wolfgang, 1993). Algorithms belonging to

the first class are based on the Modulation Transfer

Function (MTF) of the human eye only, but do not

mention any particular characteristic of the

particular image or video frames. On the other hand,

image-adaptive watermarking schemes depend not

only on the frequency response of the human eye,

but also on the properties of the image itself.

Consequently, image-adaptive watermarking

schemes can maximize the watermark robustness,

while satisfying the transparency requirement. In

other words, a content-based watermark is

perceptually adapted to local characteristics of the

host image or video. The main theme of this paper is

to develop a content-based watermarking scheme for

video host signals.

Various applications have been proposed for

video watermarking (Doerr, 2003). Video

watermarking has been suggested for preventing

illegal copying in the future DVD devices (Bloom,

1999). Also video watermarking can be used for the

aim of automatically checking whether a specified

program, e.g. commercial advertisement, has been

broadcasted by some channels in a specific period of

time (Depovere, 1999). Inserting a unique

watermark ID for each customer transmitted through

Pay-Per-View and Video-On-Demand services

enables it to trace back traitor users (Lin, 2001). But

its first targeted application is copyright protection,

in which the owner inserts its registered watermark

into the digital property to prove its paternity in the

case of finding an illegally copied version (Qiao,

1998).

In this paper, a content-based method for

watermarking of video streams is developed

regarding motion entropy of the host video. In

Section 2, some discussion on the content-based

watermarking schemes is given and the entropy

masking effect is introduced. Section 3 extends

image watermarking methods to the video contents.

The proposed algorithm using motion entropy is

described in Section 4. Section 5 presents

implementation results and a comparison with the

traditional methods. Finally, Section 6 concludes the

paper.

2 CONTENT-BASED

WATERMARKING SCHEMES

In this section, some image-adaptive watermarking

methods based on DCT transform are described. The

IA-DCT method proposed by Podilchuk et al. is a

good paradigm of image-adaptive watermarking

schemes (Podilchuk, 1998). The method is the dual

of the image-independent scheme proposed by Cox

et al. in (Cox, 1995). In the IA-DCT method,

Watson’s visual model (Watson, 1993) is used to

insert watermark in the DCT coefficients of the host

image. First, the image is divided into 8*8 non-

overlapping blocks of pixels and then the watermark

signal, which is a Gaussian zero-mean random

process with variance equal to one, is inserted in the

DCT transform of the blocks, as:

⎩

⎨

⎧

≥+

otherwiseX

jndXifwjnd

vub

vubvubvubvub

vub

,,,,,,,,

(1)

where X

b,u,v

is the (u,v)-th coefficient of DCT

transform over b’th block, jnd is the corresponding

Just Noticeable Difference (JND) which is evaluated

using Watson’s visual model, w

b,u,v

is the

corresponding watermark bit, and X

b,u,v

is the

resultant watermarked DCT coefficient.

In (Watson, 1997), Watson introduced a new

masking effect called entropy masking, which is due

to unfamiliarity of the observer. In fact, entropy

masking is due to weakness of human brain in

processing simultaneous complex phenomena. By

ROBUST CONTENT-BASED VIDEO WATERMARKING EXPLOITING MOTION ENTROPY MASKING EFFECT

253

performing a number of experiments, Watson

showed that this masking phenomenon is distinct

from previously known masking effects such as

contrast masking. Contrast masking refers to the

early visual system effective gain reduction, whereas

entropy masking takes place in the human’s brain.

According to this phenomenon, as the number of

image details increases, the HVS decreases its

sensitivity to the image details. Likewise, HVS

decreases its sensitivity to regions in a video stream

where a lot of motion complexity exists.

Entropy masking, as defined and introduced by

Watson in (Watson, 1997), has rarely been exploited

in the watermarking schemes. In some earlier works,

entropy masking has been confused by the contrast

masking in an awkward manner. This is due to the

fact that spatial complexity results in both higher

contrast masking and higher entropy masking.

However, these two kinds of masking stem from

absolutely different sources. In such works, entropy

masking has been treated as the contrast masking; so

the extreme capability of entropy masking has not

been exploited in these works and not a high

improvement has been achieved compared to

traditional schemes. Exploiting entropy masking in

applying visual models to watermarking schemes

can lead to drastic improvements in the

watermarking system's functionality, i.e. its

robustness. In our previous work, the spatial entropy

masking has been exploited to enhance the

watermark power in IA-DCT scheme; hence the

watermark resilience against common attacks is

improved. It has been shown that the achieved

enhancement is clearly higher than what is gained in

similar works. The Shannon's entropy of pixels

intensity histogram within each non-overlapping

block is defined as its complexity and is used for

elevating JND coefficients in high spatial

complexity regions.

3 EXTENSION OF IMAGE

WATERMARKING SCHEMES

TO VIDEO

In this section, previously discussed image

watermarking algorithms will be extended to video.

MPEG standard is the most popular video

compression method; hence, the watermarking

algorithm should be robust against this attack.

In MPEG-1 compression method, some frames

are coded as intraframes and the others are predicted

from them. In order to reduce the prediction error

propagation, for each group of frames (typically 12

consequent frames), one intraframe should be

included. In addition, because of large prediction

error in shot change frames, these frames are usually

considered as intraframes. According to MPEG-1

standard, intraframes should be coded similar to

JPEG standard algorithm for still images. The other

frames are predicted from their neighborhood frames

using block-wise motion vectors and the difference

frame is coded in the DCT transform similar to

JPEG algorithm using a different quantization

matrix. During this process, most details of predicted

frames are omitted, so the watermark inserted in

these frames cannot be detected efficiently.

In (Wolfgang, 1993), the IA-DCT technique is

simply extended to video streams. The watermark

detection from predicted frames is very difficult and

inaccurate; hence the watermark should be inserted

in the intraframes. Because the compression

procedure is independent of watermarking system,

intraframe positions are not known; so we prefer to

embed the same watermark in the frames belonging

to each group of typically 12 frames. Consequently,

in the detection procedure, the detection correlation

is computed for all frames. This correlation value

will be significantly higher for intraframes.

In this research, we extend the IA-DCT for video

streams considering both spatial and temporal

entropy masking effects. Results of these

implementations are presented in Section 5.

4 THE PROPOSED ALGORITHM

In this section, our proposed method for video

watermarking which uses motion information is

discussed. In content-based image watermarking

methods, human eye sensitivity to the spatial

characteristics is considered, and for instance in

(Houmansadr, 2005) which is discussed briefly in

section 2, the watermark energy is increased in

spatially complex regions of the image, considering

entropy masking effect, in order to achieve better

results. In video watermarking, temporal sensitivity

should be considered as another important factor. As

mentioned earlier, entropy masking is due to

weakness of human brain in processing

simultaneous complex phenomena. In the case of an

image, these complex phenomena can be high

entropy information corresponding to complex

regions of the image. Likely, brain's weakness in

processing high entropy regions of a video signal,

i.e. moving parts, leads to an increase in the

detection thresholds of the noticeable differences

(JND), which can be exploited to strengthen the

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Current frame

image

Four neighboring

frames

Covering with 8*8

blocks

DCT

JND

computing

Spatial entropy

computing

Motion entropy

computing

JND

modification

IDCT

Watermarked

frame

Watermark

embedding

Figure 1: Block diagram of the proposed watermarking method.

embedded watermark. This fact, motion entropy

masking effect, has also been noticed in the

literature without any adequate reasoning. Wang et

al. in (Wang, 2002) state that the HVS has lower

sensitivity to moving regions of scenes.

To achieve this aim, we need a criterion to

measure the temporal complexity of video frames. A

primary approach is to extend the spatial entropy

blocks in temporal domain and compute the

Shannon's entropy for pixels in some cubes instead

of blocks. Yet, according to our experiments, this

method is inefficient because motion cannot

considerably change the pixel values histogram in

the cubes.

The overall block diagram of the proposed

method is presented in Figure 1. First, each frame is

divided into 8*8 non-overlapping blocks of pixels.

The resultant frames are transformed to the DCT

domain and the JND coefficients are evaluated using

the Watson’s visual model. Then spatial complexity

and temporal complexity are evaluated and used to

modify the JND values in respect to the entropy

masking effect. The modified JND coefficients are

as follows:

jnd

=jnd.Spatial_mask(1+ α.Motion_mask) (2)

where jnd is the unmodified JND coefficient

derived from the Watson’s visual model,

Spatial_mask is the spatial entropy masking,

Motion_mask is the temporal complexity parameter,

and

is a parameter which should be adjusted

tentatively. Increasing this parameter leads to visible

artifacts in the moving areas of the video sequence.

This parameter is set to 0.001 in our

implementations. The modified JNDs are used in (1)

to insert the watermark in the DCT domain of the

video frames and an IDCT transform produces the

final watermarked video frames.

In (Houmansadr, 2005), we have computed the

spatial entropy mask using pixel values histogram of

each block. Because few pixels, 8*8=64, are

considered in the entropy calculation, the result is

not so confident. Hence, the intensity values are

quantized to 32 levels. In this research, i.e. video

watermarking, the quantized histogram is smoothed

using a Gaussian kernel to suppress the sparseness

effect perfectly. In fact, having a small number of

input data makes the histogram impulsive and leads

to inaccurate Shannon's entropy value. The linear

function that elevates the spatial entropy mask from

the spatial complexity parameter is optimized to new

modifications in this research as:

Spatial_mask=(0.7*En+0.4) (3)

Input frame

image

Covering with

8*8 blocks

32 levels

histogram for

each block

Convolving

with Gaussian

kernel

Filling all the

block with

entropy value

Spatial

Complexity

(En)

Figure 2: Block diagram of the spatial complexity computation.

ROBUST CONTENT-BASED VIDEO WATERMARKING EXPLOITING MOTION ENTROPY MASKING EFFECT

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where En is the spatial complexity parameter

evaluated by the Shannon’s entropy as discussed

earlier. The spatial complexity computing block

diagram is shown in Figure 2.

The block diagram of the motion mask production

process is shown in Figure 3. First, the frame to be

watermarked and its four neighboring frames are

considered, and the variance of the intensity values

for each pixel is computed for these five frames.

Then, the mean of these variance values over 8*8

non-overlapping blocks are computed. This simply

computed value for each block represents the

temporal complexity of that particular block because

it is higher for blocks having larger motion. This is

the Motion_mask parameter used in (2) with which

parameter has been adjusted in a trade off

between robustness and imperceptibility.

5 IMPLEMENTATION RESULTS

AND COMPARISONS

The proposed algorithm is implemented in Matlab

and tested on 200 frames of some standard

sequences. A randomly generated watermark is

embedded in the frames of the video using IA-DCT,

spatial entropy based, and the proposed method prior

to the MPEG-1 coding. A set of subjective

experiments has been performed to consider the

transparency of the watermarked video sequences.

As table 1 illustrates, output sequences are

subjectively similar to the original ones, and the

watermark is perfectly invisible. The correlation

values between recovered watermark and the

original are computed for all frames of the

sequences. Mean and maximum values of the

correlation for different sequences are normalized to

the IA-DCT method considered as the reference

algorithm and are presented in Table 2. In all cases,

the maximum value corresponds to one of the

intraframes.

As shown in Table 2, correlation of the proposed

method in all cases significantly improves over the

two previous methods. The increased correlation

makes the algorithm more robust and allows the

system designer to decrease the watermark energy

and makes it more invisible. Claire sequence in our

test is a little faster than the ordinary ones and has a

large flat background which has very low spatial

complexity. So, the spatial entropy masking cannot

increase it so much, but using the motion mask, the

performance is increased considerably because of

high watermark embedding capacity in fast moving

regions.

Sequence name IA-DCT Spatial entropy

mask

Entropy and motion

mask

Mom and daughter 4.3 4.0 4.1

Coastguard 4.5 4.4 4.4

Foreman 4.3 4.4 4.4

Claire (fast) 4.4 4.4 4.6

Average 4.37 4.3 4.37

Current frame

image

Four neighbo

ing

frames

Temporal

variance for

each pixel

Mean va

iance

over each 8*8

block

Motion

Entropy

Table 1: Results of the subjective experiments (MOS of 5) on the video sequences watermarked with three differen

algorithms.

Figure 3: Motion mask computing block diagram.

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Table 3: Mean normalized correlation results for 30

different random croppings.

Sequence name

Spatial entropy

mask

Entropy and

motion mask

Mom & daughter 1.5211 2.7978

Coastguard 1.5266 2.1205

Foreman 1.1889 1.9922

Claire (fast) 1.0784 4.0784

Average 1.3748 2.5795

The effect of different watermarking attacks is

investigated on the proposed power-improved

watermarking schemes. The watermarking algorithm

should be robust against cropping attack. In these

implementations, 30 different randomly selected

regions are cropped (keeping one-sixteenth of the

original) and the watermark is detected. Table 3

shows the mean of the normalized correlation

results. From the table, considering motion entropy

in addition to the spatial entropy masking results in

more robustness to the cropping, especially in video

sequences with high spatial and temporal

complexity.

Frame rotating with different degrees is also

investigated on the watermarked video sequences.

Tables 4 and 5 compare the correlation coefficient of

three watermarking schemes for 5 and 10 degrees of

rotation, respectively. Again, the correlation

coefficients show considerable elevation in respect

to the IA-DCT method, particularly for high entropy

video sequences.

Also, we passed the watermarked streams

through Additive White Gaussian Noise (AWGN)

channels with different PSNR values and compared

the resulted correlation values. Tables 6 to 8

illustrate these correlations for different PSNR

values. Again, it is observed that considering motion

mask and spatial entropy effects in the watermark

insertion stage leads to more robust watermarked

sequences, and the robustness enhancement is a

function of the spatial and temporal complexity of

the video stream.

Table 4: Mean normalized correlation results after rotation

by 5 degrees.

Sequence name

Spatial entropy

mask

Entropy and

motion mask

Mom & daughter 1.4012 2.6821

Coastguard 1.4116 2.0523

Foreman 1.0962 1.8862

Claire (fast) 1.0565 3.9562

Average 1.2413 2.6442

Table 5: Mean normalized correlation results after rotation

by 10 degrees.

Sequence name

Spatial entropy

mask

Entropy and

motion mask

Mom & daughter 1.4035 2.6855

Coastguard 1.3865 2.0985

Foreman 1.1168 1.8654

Claire (fast) 1.0432 3.8562

Average 1.2375 2.6264

Table 6: Mean normalized correlation results for AWGN

channel with PSNR=42.

Sequence name

Spatial entropy

mask

Entropy and

motion mask

Mom & daughter 1.3561 2.4235

Coastguard 1.3265 1.8956

Foreman 1.0921 1.5786

Claire (fast) 1.0562 3.5890

Average 1.2077 2.3716

Table 7: Mean normalized correlation results for AWGN

channel with PSNR=36.

Sequence name

Spatial entropy

mask

Entropy and

motion mask

Mom & daughter 1.4312 2.5684

Coastguard 1.4290 1.9453

Foreman 1.1036 1.6521

Claire (fast) 1.0486 3.9231

Average 1.2531 2.5222

Mean correlation

Maximum correlation

Sequence name

Spatial

entropy mask

Entropy and

motion mask

Spatial

entropy mask

Entropy and

motion mask

Coastguard 2.1137 2.7333 1.8176 2.3077

Mom and daughter 1.2309 2.2448 1.1410 2.1239

Foreman 1.4471 2.4762 1.1202 2.6176

Claire (fast) 1.1000 3.0600 0.9817 2.4292

Average 1.4729 2.6286 1.2651 2.3696

Table 2: Normalized correlation of original and detected watermarks on MPEG-1 coded watermarked video for some

sample sequences.

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Table 8: Mean normalized correlation results for AWGN

channel with PSNR=30.

Sequence name

Spatial entropy

mask

Entropy and

motion mask

Mom & daughter 1.4301 2.5622

Coastguard 1.4358 1.9602

Foreman 1.1213 1.7562

Claire (fast) 1.0584 3.9650

Average 1.2614 2.5609

The proposed algorithm has a little more

computational complexity over previous methods;

hence it could be used in real time applications. By

using motion vectors in the motion mask computing

process, the algorithm could perform better while is

more time consuming. By increasing the watermark

energy, it might be visible. Therefore, it might be

observed because of watermark stationary,

particularly in moving areas. In these cases,

watermark could be moved in agreement with the

motion vectors. In order to have a fast algorithm,

these approaches are not implemented in this

research.

Three consecutive frames of “Mom and daughter”

sequence, motion mask, spatial entropy mask, the

final watermark in DCT domain and spatial domain,

and the watermarked image for the central frame are

shown in Figure 4. It is obvious that motion mask

has high values for mother’s hand and lips which

move fast.

Figure 4: Outputs for Mom and daughter sequence. From top to bottom and left to right: (a) frame 10, (b) frame 12, (c)

frame 14, (d) motion mask for (b), (e) spatial entropy mask for (b), (f) embedded watermark in DCT domain, (g)

embedded watermark in spatial domain, (h) watermarked output for (b), where Sub-figures (d-g) are exaggerated.

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6 CONCLUSIONS

In this paper, a content-based video watermarking

algorithm is proposed which uses temporal

complexity, i.e. motion, in addition to spatial

entropy to better adapt the watermark to the video

contents. In other words, because of invisibility of

high spatial frequencies in moving fields, the

watermark energy can be increased in these regions.

In this paper a simple method for motion mask

calculation is proposed. This algorithm is

implemented in Matlab and tested for some standard

sequences. The experiments show considerable

improvement in watermark resilience against

different watermarking attacks in comparison with

the traditional content-based algorithms.

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