On Line Video Watermarking

A New Robust Approach of Video Watermarking based on Dynamic Multi-sprites

Generation

Ines Bayoudh, Saoussen Ben Jabra and Ezzeddine Zagrouba

Research Team on Intelligent Systems in Image and Artiﬁcial Vision (SIIVA), RIADI laboratory, ISI,

University Tunis El Manar, Abou Raihane Bayrouni 2080, Ariana, Tunisia

Keywords:

Digital Video Watermarking, Multiples Sprites, LSB, Collusion, Invisibility, Robustness.

Abstract:

With the development of the emerging applications, watermarking methods require a new type of constraints.

In fact, robustness to malicious attacks and processing time reduction have became two important constraints

which must verify a watermarking approach. In this paper, a new scheme of digital watermarking for video

security is proposed. This scheme is based on dynamic multiple-sprites. These last ones allow obtaining

robustness in front of collusion which presents a dangerous attack for marked video. First, original video is

divided into groups of images, and then a sprite will be generated from each group. Finally, the signature

will be inserted in the low bits of the obtained sprite and marked frames will be generated from the marked

sprites. Experimental results show that the proposed scheme is robust against several attacks such as collu-

sion, compression, frame suppression and transposition, and geometric attacks. In more, processing time of

watermarking is reduced.

1 INTRODUCTION

The variety of digital technologies have been engen-

dered the risk of media insecurity. In fact, digital

document manipulation and transfer became easier

than before and traditional methods of security like

cryptography and steganography have become insuf-

ﬁcient. Digital watermarking was appeared as a solu-

tion to protect multimedia contents from illegal ma-

nipulations. It consists to embedding an impercep-

tible signature into a numeric text, image, audio or

video sequence, and then to try to detect the signature

after an eventual distortion done on the marked data.

Watermarking must verify several constraints such as

invisibility of embedding, high capacity of insertion

and robustness against almost of attacks. Nowadays,

a new challenge appeared by emerging applications

development such as broadcasting, pay TV and video

conference. This challenge is the reduction of pro-

cessing time.

Different techniques of video watermarking have

been proposed but each of them is developped for

a praticular application (Gopika and Chiddarwar,

2013), (Hood and Janwe, 2013). In fact, there are

methods which care to guarantee a better robustness

against almost of attacks, others try to maximize in-

visibilty or aim to reduce time processing. This de-

pends on where and how the signature will be in-

serted. In fact, it exists mainly two insertion domains:

spatial domain where signature will be embedded by

modifyingdirectly original data or frequential domain

which is based on DCT, TFD or Wavelet transforma-

tion of original data to embed signature. Every in-

sertion domain presents its own advanatges and in-

convenients. In fact, spatial domain presents the best

imperceptibility but it is not robust to malicious at-

tacks. However, frequential domain allows robust-

ness in front of many attacks but it reduces invisibilty.

There is a second critera which can be used to classify

watermarking methods : it is insertion mode which

can be additive or substitive mode. The signature ca-

pacity depends on the insertion mode. This capacity

increased when the mode is substitive and it is low in

the second case.

In this paper, the proposed approach aims to max-

imize the compromise invisibilty, robustness against

collusion with a reduction of processing time. It’s

why we focused on existing watermarking techniques

proposed for real time applications. So, we proposed

to classify them into two classes: techniques designed

for real time applications and techniques robust to col-

lusion.

158

Bayoudh I., Ben Jabra S. and Zagrouba E..

On Line Video Watermarking - A New Robust Approach of Video Watermarking based on Dynamic Multi-sprites Generation.

DOI: 10.5220/0005316801580165

In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISAPP-2015), pages 158-165

ISBN: 978-989-758-091-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

For the ﬁrst class, processing time depends on

frames emission rates per second which can be 25f/s

or 30f/s depending on the standard system used in

each country. Considering this rate equal to 30f/s,

30 frames can be transmitted in one second it means

0.033seconds (33 milliseconds) is allocated for every

frame. Thus, a real time scheme must treat a frame no

more than 33 milliseconds.

In (Lee and Seo, 2013) a real time watermark-

ing based on temporal modulation was proposed. It

operates in spatial domain with an error correcting

code to ameliorate robustness. Other methods are

proposed in transformed domain (Maity and Kundu,

2009), (Wang et al., 2009), (Bhaisare et al., 2013)

as in (Lee and Im, 2012) where authors propose to

embeed signature into compressed domain to mini-

mize the proceeding time. In fact, the video is par-

tially compressed to generate DCT coefﬁcients where

the signature is inserted using the Quantization In-

dex Modulation QIM. This last one consists to select

the nearest byte in the coefﬁcient from the mark to

guarantee a good insertion quality. The proposed per-

ceptual watermarking by (P.Mohanty and Kougianos,

2011) aims to develop an architectural idea which im-

prove the treatment time using the parallel implemen-

tation.

Concerning the collusion attack, it became a ma-

jor threat for video security. In fact, the attacker tries

to predict the original document exploiting the tempo-

ral redundancyin the sequence video. There were two

types of collusion. The ﬁrst one estimates the embed-

ded mark then eliminates it from the document. This

method is possible when the hacker has a lot of differ-

ent marked images using same signature. For the sec-

ond type of collusion, the attacker must have a large

combination of similar images which are marked dif-

ferently and he averages them to destroy the signa-

ture and obtain the unmarked video. To resolve col-

lusion problem, movement and redundancy must be

reduced (Dorr and Dugelay, 2005). So, the better idea

to obtain robustness against this attack consists to em-

bedding signature into mosaic image generated from

the video. In fact, the mosaic presents an accumula-

tion of dispersed information into the sequence where

the same point repeated along the video will be repre-

sented by a single reference in the panoramic image.

Therefore, all similar pixels are marked with the same

way. Koubaa et al (Koubaa et al., 2012) proposed

to insert the signature with a wavelet watermarking

into the feature regions selected from the mosaic im-

age. Bayoudh et al (Bayoudh et al., 2013) also used

the mosaic image generated from the original video

to embed the signature. The insertion is done using

frequential domain and the Krawtchouk moments to

improve robustness and invisibility of the signature.

Kerbiche et al (Kerbiche et al., 2012) proposed an-

other watermarking based on mosaic image. They se-

lected feature regions where the objects move. First,

the wavelet transform is applied on the selected re-

gion. Then, obtained frequencies are marked using

two different approaches to verify the best compro-

mise invisibility/robustness. These techniques present

a good robustness but they require an important pro-

cessing time allocated to generate the mosaic. So,

they can’t be applied for real time applications.

In this paper, a new video watermarking scheme

is proposed. It aims to resist to almost of attacks

with a higher capacity, imperceptibility and time pro-

cessing reduction. In fact, the new approach is based

on dynamic multisprites generation to guarentee a ro-

bustness against collusion attacks with a processing

time minimization. In more, the signature embedding

is based on spatial domain which allows obtainning

high invisbility and an independency from standard

of compression. Finally, the proposed algorithm is

blind. This last criterion permits to reduce processing

time at detection step. The remaining sections of this

paper are organized as follows. The proposed water-

marking is described in section 2 and the experimen-

tal results are provided in section 3. Finally, section 4

summaries the proposed work and presents the future

work.

2 PROPOSED WATERMARKING

The main idea of the proposed scheme is to embed

signature into mosaic image generated from origi-

nal video to guarantee the robustness against collu-

sion attacks. Different recent techniques are based

on panoramic video image to resist collusion (Koubaa

et al., 2012), (Kerbiche et al., 2012), (Bayoudh et al.,

2013). For all these techniques, the ﬁrst step consists

to generate mosaic from different images of original

video sequence. Then, the obtained mosaic will be

marked using a spatial or frequential embedding. Ev-

ery technique try to improve robustness against differ-

ent attacks with a high invisibility but the mainly lim-

itation of these techniques is processing time which

is important. In fact, the generation of a panoramic

image from the whole video sequence requires an im-

portant processing time with an eventual distortion of

the reconstructed video sequence. To resolve these

two problems, multi-sprites are proposed in this pa-

per. The use of multi-sprites will offer a reduction of

processing time of mosaic generation and will reduce

the distortion effect due to the video reconstruction

from the mosaic image.

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2.1 Multi-sprites Generation

Multi-sprites generation from a video sequence can

be static (standard) or dynamic.

2.1.1 Standard Multiples Sprites Generation

For a single sprite, a single image is generated from

the whole sequence. Obtained image presents a large

size with a hard distortion. In Figure 1 we show an ex-

ample of a single sprite generated from the sequence

”Stefan” which contains 300 images. The generation

Figure 1: Single sprite generated from ”Stefan” sequence.

of a single mosaic consists to choosing the reference

image, and then, each image will be transformed by

this reference and warped in the same space to con-

struct the mosaic.

For multi-sprites case, multiple mosaics are gen-

erated from the original video sequence. In fact, the

video will be subdivided into parts depending on the

camera parameters (Krutz and Glantz, 2008). Figure

2 presents the different sprites obtained from ”Stefan”

sequence using the estimation proposed in (Krutz and

Glantz, 2008) with parameters variation. We can con-

clude that sprites number depends on the parameters

used to subdivide the sequence. So, multi-sprites gen-

eration scheme is based on selecting the segmentation

classiﬁer then from every segment the standard gen-

erator of single mosaic will be adopted.

As the goal of the proposed approach is to mini-

mize the processing time, we propose to replace the

step of choosing segmentation classiﬁer by a standard

fragmentation without treatment. We propose to se-

lect an independent part from video each second, and

from every part a sprite will be generated. However,

the time to build the mosaic is also signiﬁcant. This is

caused by the algorithm of mosaic generation. In fact,

to generate a mosaic we should save all images, then

for every new image the reference can be changed so,

all the mosaic will be generated using the whole set of

images. To resolve these problems, a new sprite gen-

eration scheme is proposed in this paper. This scheme

will be adapted to the watermarking constraints.

2.1.2 Dynamic Multiple-sprites Generation

The contribution of this proposed approach is to use

dynamic multi-sprites to embed signature. This al-

lows reducing processing time. This allows reducing

Figure 2: Multiple sprites generated from ”Stefan” se-

quence.

processing time. In more, to verify robustness against

collusion, dynamic aspect will be proposed at mosaic

generation step.

For a static mosaic we need all the sequence to

generate the panoramic image. It means that we can’t

produce the mosaic while the whole set of frames is

not obtained. The mosaic will be created by manipu-

lating all the images relatively to the reference.

However the generation of a dynamic mosaic de-

pends only on the received images and not all the se-

quence. So, every current image will be treated in

function of previous received images d didn’t need

the achievement of the sequence. Dynamic mosaic

or mosaics on line are proposed in literature (chaﬁk

Bakkay et al., 2011), (Kuo and Chen, 2009).

2.1.3 Proposed Multiples Sprites Generation

The proposed multi-sprites generation scheme can be

composed to the following steps:

1. Giving an original video sequence, it will be di-

vided in n seconds

2. Each i seconds, the ﬁrst received frame will be

considered as the reference image of the i

mosaic

3. The ﬁrst frame of each second is considered as the

current mosaic

4. Every new received frame is transformed based on

the reference image

5. The mosaic will be updated based on the current

mosaic and the current received image

6. Steps 4 and 5 will be repeated every second.

In more, the proposed approach improves mosaic

updating step to increase robustness and to reduce

processing time. In fact, when a new image is re-

ceived, Sift parameters are calculated to determine

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Figure 3: Updating step (a): the current generated mosaic

(b): the new mosaic generated based on the current image.

the applied transformation between this image and the

reference image. Therefore, the image is transformed

at the same space of the latest generated mosaic. Fi-

nally, to obtain the new mosaic, only the transformed

image and the current mosaic will be used. Figure 3

shows the updating step of the current mosaic based

on the current image.

Besides the advantage of processing time reduc-

tion, the proposed algorithm allows a memory space

reduction by minimizing the used parameters for the

mosaic generation. In fact, contrary to standard mo-

saic updating which depends on the whole set of im-

ages, in the proposed generation only the latest mo-

saic and the current image are needed for updating.

2.1.4 Static and Dynamic Multi-sprites

Generation Comparison

Figures 4 and 5 show respectively sprites generated

from test original videos using static generation, and

the results of dynamic multiple- sprites generation.

Due to the averaging way used to generate static mo-

saic, the moved objects are eliminated whereas they

exist for dynamic mosaic.

Table 1 shows average time allowed to generate a

sprite. This last one is progressively reduced in case

of dynamic mosaic.

Table 1: Average time oh the generation of a sprite in sec-

onds.

Approach Stefan Soccer Granguardia

Static 25.07 s 119.91 s 82.49 s

Dynamic 16.85 s 70.67 s 49.2 s

2.2 Proposed Embedding Scheme

The proposed scheme aims to obtain a new water-

marking scheme for real time application with a high

Figure 4: Examples of Static multiple-sprites generated

from (a) ”Granguardia” (b)”Soccer” sequence.

Figure 5: Examples of dynamic multiple-sprites generated

from (a) ”Granguardia” (b)”Soccer” sequence.

invisibility and robustness against almost attacks es-

pecially collusion attacks. Based on dynamic multi-

ples sprites generation, the proposed approach guar-

antees robustness against collusion although a reduc-

tion of treatment time. The proposed watermarking

scheme is presented in Figure 6 and it will be repeated

each second for the continuous set of images. It can

be decomposed in different steps:

1. Dynamic sprites are generated from the received

frames every second

2. Embedding scheme is applied to the ﬁnal sprite

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obtained

3. Marked images set will be reconstructed from the

marked sprite

4. All steps will be repeated until sequence emission

achievement.

Figure 6: Proposed watermarking scheme.

2.2.1 Sprite Generation

The ﬁrst step of the proposed approach consists to

generate the on-line sprite every second. To optimize

the sprite generation time, we proposed to improve

the way of mosaic update. In fact, instead of us-

ing averaging of the different frames calculation to

construct the mosaic, we propose to maximize the

latest version of mosaic with the most new received

frame. This allows reducing memory space, updat-

ing time as well as robustness. Figure 7 presents the

mark to insert and Figure 8 shows mark detection re-

sults using dynamic mosaic update by averaging (a)

and detection results using proposed mosaic updating

(b). These results show that the detection is more per-

formed in the second case.

Figure 7: Original mark.

2.2.2 Sprite Conversion to YUV Space

After sprite generation, it will be converted to the

YUV space. This space is described by its three com-

ponents: Y which presents the luminance in image

and U, V which present image chrominance (blue and

red colors).

The Y component is selected to be marked be-

cause it represents the more important information

on the image. In more, almost of compression tech-

niques modify chrominance components so the lumi-

Figure 8: Mark extraction, (a) using dynamic generation

with averaging, (b) using proposed dynamic generation.

nance will not be modiﬁed and then the embedded

signature will resist to compression manipulation.

2.2.3 Spatial Embedding Scheme

The signature embedding scheme contains two steps

as shown in Figure 9. First, signature spreading is

applied based on sprite size. Then, LSB (Low Signif-

icant Bit) algorithm is applied to insert the mark. The

low bit of every pixel in the luminance component is

substitute with the corresponding bit from the spread

mark to obtain the marked image. Finally, the marked

sprite is reconverted to the RGB space and marked

frames will be reconstructed. The LSB scheme is ap-

Figure 9: Insertion scheme.

plied thanks to its high invisibility. In addition, the

use of a substitution mode of insertion allows obtain-

ing blind detection and increases the mark capacity.

2.3 Detection Stage

To detect the signature from the watermarked video,

different steps are applied where the three ﬁrst steps

are similar to those of insertion scheme. In fact,

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different sprites are generated from the continuous

frames received each second. Then, every ﬁnal gen-

erated sprite is converted to the YUV space and the

luminance component is selected to extract the signa-

ture from the last signiﬁcant bit. Finally, the spread

mark is extracted and divided into a set of signatures

where the one which presents the best correlation is

selected.

3 EXPERIMENTAL RESULTS

AND EVALUATION

To evaluate the proposed approach it was applied on

three test videos. These videos are ”Stefan”, ”Soccer”

and ”Granguardia” (Figure 10). Each video presents

different criteria such as scene frame number, move-

ment frequency and background texture (Table 2).

Figure 10: Sequences tests, (a) ”Stefan”, (b) ”Soccer”, and

Table 2: Test videos characteristics.

Criteria Stefan Soccer Granguardia

Number 300 150 50

Movement Fast Variable Slow

Texture High Mixed Unifom

Size 176x144 352x288 324x276

Figure 11: Watermarked frame of sequences tests, (a) ”Ste-

fan”, (b) ”Soccer”, (c) ”Granguardia”.

Proposed approach evaluation is tested based on

three criteria: invisibility, robustness and time of pro-

ceeding.

3.1 Invisibility

Figure 11 shows that there is no difference between

original and marked frames. To prove this invisibility,

PSNR (Peak Signal to Noise Rapport) and the RMSE

(Root Mean Square Error) between the original and

the marked video frames are measured (Table 3). The

lowest mean value of PSNR is 70.613db and the high-

est is under 82.797db and RMSE are 0.018 and 0.076.

The best values of invisibility are obtained for ”Gran-

guardia” sequence and the wrothest are obtained for

”Stefan” sequence. In fact, this is due to the frames

texture which is uniform in case of ”Granguardia”.

Table 3: The Average Value of PSNR (db) and RMSE.

Criteria Stefan Soccer Granguardia

PSNR 82.797 79.888 70.613

RMSE 0.018 0.028 0.076

3.2 Robustness

To evaluate the robustness of the proposed approach,

various attacks are applied to the mark video and then

the correlation is calculated between the extracted and

the original signatures.

Two types of attacks are tested: the usual attacks

such as geometric attacks, ﬁltering, noises, compres-

sion and temporal attacks such as frame suppression,

insertion and transposition and ﬁnally, collusion at-

tacks which aim to estimate the signature and remove

it from the marked sequence. Two binary signatures

are used in evaluation tests. These two signatures are

shown in Figure 12 where (a) presents the copyright

signature with a low density and a small size of 12x9

and (b) refers to the second signature characterized by

a high density with a large size of 63x24.

Figure 12: used signatures (a) low capacity signature, (b)

high capacity signature.

Obtained results are described in Table 4 where

the extracted marks with the correlation value are pre-

sented. The proposed watermarking is robust against

several usual and temporal attacks and even collusion

attacks.

First, geometric attacks are tested as translation,

scaling, and rotation with various angles. In addition,

we have applied different ﬁlters and noises such as

means ﬁlter, high and low pass ﬁlter, also Gaussian

and salt and pepper noise. The proposed approach

succeeds to detect signature after all these attacks.

This is obtained thanks to the invariance of the points

used to embed signature in front of these attacks.

The proposed watermarking is robust against

mpeg compression until 500kb/s. This can be ex-

plained by choosing the luminance components to

embed signature. For temporal attacks frames trans-

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Table 4: Robustness results.

Attacks Correlation Parameters

O attack 1 -

Collusion 1 -

Compression 1 500kb/s

Cropping 1 3 windows

Frame changes 1 20f/s,30f/s

Rotation 1 180,90,45

Gaussian noise 0.399 0.01,0.001

Salt pepper 1 0.1,0.01

Gaussian ﬁlter 1 3x3,5x5/0.5,0.9

Medium ﬁlter 0.441 3x3,5x5

High pass ﬁlter 0.596 -

pose and drop, insertion of unmarked frames and of a

frame that don’t make part of the considered sequence

are tested. After application of all these attacks, the

proposed watermarking can detect signature. In addi-

tion, the mark is detected after modifying the frames

rate between 20f/s to 30f/s. This is obtained thanks to

the use of sprite where same points in different images

are marked similarly.

Furthermore, detection succeeds after a cropping

attack thanks to multi-sprite use. In fact, in case of

cutting a part of video, if the signature is not detected

from the ﬁrst sprite, it can be extracted from other

sprites.

3.3 Processing Time Comparison

The proposed approach aims to improve the com-

promise between invisibility, robustness against usual

and temporal attacks, and processing time reduction.

To achieve this goal, we proposed to adopt sprite gen-

eration to real time watermarking. In fact, the best

solution proposed in literature to resist collusion is to

insert the signature into the mosaic image generated

from the video sequence. Instead static mosaic gen-

eration cannot be applied at real time applications for

many raisons. In fact, static generation depends on

the whole sequence, and then the standard generation

can take a long time. As a solution, multiple-sprites

are proposed to reduce the time of waiting to complete

the all sequence. Besides the amelioration proposed

using the online generation decreases also the time of

generation of a sprite as described in Table 1.

In Table 5, the proposed approach is compared

with two existing approaches. The ﬁrst one is based

on static mosaic to embed signature (Koubaa,2012)

and the second is developed for real time application

(Lee, 2013). Obtained results show that the proposed

approach allows obtaining the best compromise invis-

ibility, robustness and processing time reduction.

Table 5: Comparative study.

Approach PSNR Robustness Time

Proposed 65-88 Geometric, Reduced

temporal

attacks

ﬁlters,

noises and

collusion

Mosaic 45 Rotation, High

based collusion

compression

Real time 48 Geometric Real

based attacks time

4 CONCLUSIONS

This paper presents a new video watermarking ap-

proach based on on-line multiple-sprites. These last

ones allow obtaining robustness to collusion with an

important reduction of processing time. In fact, orig-

inal video is divided into groups of images, and then

a sprite will be generated from each group. Finally,

the signature will be inserted in the low bits of the

obtained sprite and marked frames will be generated

from the marked sprites. In sprite generation step, the

mosaic updating stage is improved to minimize pro-

cessing time. In addition, signature is embedded in

the luminance components of YUV space to enhance

the robustness against compression. The insertion is

done with substitutive mode which allows reducing

detection time. In more, extraction is blind and this

permits to decrease the memory needed to save origi-

nal video. Experimental results show a high invisibil-

ity with robustness against the most important attacks

including collusion attacks. Also, we have reduced

the time of treatment comparing with others existing

methods. In future work we will try to improve the

proposed approach by proposing a faster sprite gener-

ator.

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Generation

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