A Video Dataset for an Efﬁcient Camcording Attack Evaluation

Asma Kerbiche

, Saoussen Ben Jabra

, Ezzeddine Zagrouba

and Vincent Charvillat

Lab. LIMTIC, High Institute of Computer Science, University of Tunis El Manar,

2 Rue Abou Rayhane Bayrouni, 2080 Ariana, Tunisia

Lab IRIT, Team of Research VORTEX, ENSEEIHT, INP Toulouse, University of Toulouse, France

Keywords:

Dataset, Video Watermarking, Camcording Attack, Robustness Evaluation, Camcording Simulators.

Abstract:

Any video watermarking scheme dedicated to copyright protection should be robust against several attacks and

especially against malicious and dangerous attacks such as camcording. Indeed, this attack has become a real

problem for cinematographic production companies. However, until now the researchers don’t evaluate the

robustness of their video watermarking approaches against this attack or they consider it as a combination of

some usual attacks. To resolve this problem, several studies proposed camcording simulators which encourage

and help researchers in video watermarking domain to include the camcording in the robustness evaluation.

In this paper, a dataset of camcorder videos dedicated to an efﬁcient robustness evaluation of watermarking

schemes is proposed which can help researches on camcording simulators’ creation. In this dataset, videos

are captured in realistic scenarios in the cinema and are recorded using ﬁve capture devices and from four

positions. In more, the proposed dataset contains marked versions of the proposed videos using three different

video watermarking techniques. This allows researchers comparing their approaches with these techniques.

Experimental results show that the robustness evaluation based on the proposed dataset is more efﬁcient than

simulators based evaluation thanks to the diversity of the used capturing devices and the real conditions of

videos recording.

1 INTRODUCTION

With the evolution of hacking techniques and the

performance of smartphones that are, nowadays,

equipped with high quality cameras and powerful pro-

cessors allowing users to conveniently record, edit,

and share videos, movies’ hacking risks in screen-

ing rooms and movie theaters became a very dan-

gerous problem. Hence, video watermarking algo-

rithms that can resist to this kind of attack, called

”camcording”, and can help producers to protect their

copyrights should be developed. Unfortunately, un-

til now, researchers either still ignore this attack dur-

ing the watermarking evaluation, or they consider it

as a combination of usual attacks (rotation, compres-

sion, cropping ...) and this doesn’t reﬂect in any case

this attack. In order to facilitate and encourage re-

searchers to evaluate the robustness of their proposed

approaches against this attack, several researches are

developed to propose efﬁcient camcording simula-

tors. Despite of their performance, these simulators

are insufﬁcient to obtain a real comcorded video. In

this paper, we present the efﬁcient methodology used

to collect a dataset of Camcorded videos captured in

realistic scenarios in a cinema using different captur-

ing devices placed in various positions. In addition,

some videos of this dataset are marked by three video

watermarking schemes in order to evaluate their ro-

bustness against camcording attack. The proposed

dataset can be used in order to help researchers to de-

velop efﬁcient camcording simulations or to compare

their video watermarking schemes with existing tech-

niques used in the proposed dataset.

This paper is organized as follows: the ﬁrst sec-

tion presents a survey of camcording attack where

we present the existing watermarking methods ded-

icated to this attack, the usual robustness evaluation

method and the developed simulators for camcording

attack. In the second section we explain the captur-

ing methodology and we give the characteristics of

the proposed dataset. In the section 3, the evaluation

of the proposed dataset is provided by comparing the

classical robustness evaluation and the proposed one.

In section 4, we enumerate some potentials applica-

tions of the proposed dataset. Finally, a conclusion

and some perspectives are drawn.

150

Kerbiche, A., Jabra, S., Zagrouba, E. and Charvillat, V.

A Video Dataset for an Efﬁcient Camcording Attack Evaluation.

DOI: 10.5220/0006617201500158

In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP, pages

150-158

ISBN: 978-989-758-290-5

2 SURVEY OF CAMCORDING

ATTACK

Robustness is the most critical and important con-

straint that each watermarking algorithm must satisfy.

Despite the importance of this constraint, the majority

of researchers evaluate the robustness of their algo-

rithms against attacks that no longer represent a real

risk to the video stream and ignore new techniques

of piracy. In fact, a video watermarking algorithm

can resist to usual attacks such as noise or geomet-

ric transformations while it may be inefﬁcient against

more dangerous attacks such as camcording.

This last one is a malicious attack that consists

of capturing a video projected on a screen or in a

movie theater using a given capture device like cam-

era, smartphone, and camcorder in order to broadcast

it in an illegal way after having applied some trans-

formations to destroy the mark that has been inserted.

This attack has become a major problem for ﬁlm’s in-

dustry in these recent. Hence, the robustness of video

watermarking algorithms against this attack has be-

come a necessity.

The Motion Picture Association afﬁrms that the

annual loss caused by pirated movies is 6.1 billion

dollars and that over 90% of the pirated new release

titles are illegal recordings made by camcorder piracy

(of America, 2005), (Association., ) which is explic-

itly banned in many countries by law.In the United

Sates, for example, the law of Family Entertainment

and Copyright Act, prohibits the uses of recording de-

vices in theatres. The law also imposes a strict penalty

on anyone who pre-release works not just ﬁlms. Also

in Japan, to avoid the increasing of the loss of box-

ofﬁce revenues, an anti-camcorder law, which pro-

hibits recording movies even for private uses, has

been enforced and then permitted by the previous

prevent any person from making illegal recordings.

2.1 Existing Video Watermarking

Techniques Dedicated to

Camcording Attack

To avoid the camcorder piracy, several watermark-

ing techniques have been proposed in order to iden-

tify the illegally Camcording position (B. Chupeau

and Lefbvre, 2008). The research and development

unit of Kodak proposed a robust video watermarking

method which permits to identify the cinema where

the projection took place as well as the time and date

of broadcast (Chandramouli R., 2001). Gosavi et

al. (Gosavi and Mali., 2017) proposed a video wa-

termarking technique which aims to detect the cam-

corder piracy based on DCT transform where the po-

sition of pirate is estimated by comparing original

video frames with watermarked ones. Nakashima et

al. (Yuta Nakashima and Babaguchi., 2009) proposed

a deterrent to camcorder piracy, by developing a sys-

tem for estimating the recording position from which

a camcorder recording is made. The system is based

on spread-spectrum audio watermarking for the mul-

tichannel movie soundtrack. It utilizes a stochastic

model of the detection strength, which is calculated

in the watermark detection process. Experimental re-

sults show that the system estimates recording posi-

tions in an actual theater with a mean estimation error

of 0.44m. The results show that the method does not

signiﬁcantly spoil the subjective acoustic quality of

the soundtrack. Lee et al. (Min-Jeong Lee and Lee.,

2010) proposed also a video watermarking based on

spread spectrum way that satisﬁes the requirements

for protecting digital cinema and it enables the de-

tector to estimate the position where the camcorder

recording was made. The proposed position estimat-

ing model can detect the seat in a theater with a mean

absolute error of 33.84, 9.53, 50.38 cm.

2.2 Camcording Evaluation for Video

Watermarking Methods

Only a few researchers have tried to evaluate the ro-

bustness of their approaches against the Camcording

attack. In order to make this evaluation, some con-

sider the Camcording as a combination of usual at-

tacks and simply test robustness against this combi-

nation. However, this combination can’t provide, in

any way, the real case of the application of Cam-

cording. On the other hand, some researchers test

the robustness against Camcording by capturing their

watermarked video projected on standard screen like

LCD.

Do et al. (Hoseok Do, 2008) propose a blind dig-

ital video watermarking scheme, robust to camcorder

recording and to a variety of common video pro-

cessing and geometric distortions. They test the ro-

bustness against Camcording by capturing the water-

marked videos without specifying the camcorder and

the projection’s screen models. They tested three sce-

narios: recorder, recorder with rotation and recorder

with cropping but they didn’t specify the parameters

of each attack. Choi et al. (Dooseop Choi, 2010) pro-

pose a new blind MPEG-2 video watermarking algo-

rithm robust to camcorder recording and other attacks.

They test the robustness of the proposed algorithm

against Camcording by making several recordings of

each video using a digital camcorder Sony, HDR-SR1

A Video Dataset for an Efﬁcient Camcording Attack Evaluation

151

on a tripod 2.5m away from a 24-inch LCD screen

Dell, 2405FPW. Two scenarios are used for testing:

Recording 1: The recorded videos are resized to orig-

inal size and recompressed by Xvid, 1000 Kbit per

second. Recording 2: The recorded videos are re-

sized to original size and recompressed by Xvid, 500

Kbit per second. Asikuzzaman et al. (Md. Asikuz-

zaman and Pickering, 2014) propose a blind video

watermarking algorithm where the watermark is em-

bedded into both chrominance channels using a dual-

tree complex wavelet transform. This algorithm is

robust to downscaling in arbitrary resolution, aspect

ratio change, compression, and Camcording. The ro-

bustness against Camcording was tested by display-

ing watermarked video sequences at the rate of 25

fps and 30 fps on a 24-inch Samsung monitor and

recorded the content with an iPhone 4S. Li et al.

(Li Li, 2015) proposes an H.264/AVC HDTV water-

marking method that is robust to camcorder record-

ing, transcoding, recoding, and other geometric at-

tacks. They test the robustness against camcorder at-

tack by recording the watermarked video using a cam-

corder Sony HXR-MC1500C on a tripod 2 m away

from a 24 in. LCD monitor.

2.3 Review of Camcording Simulators

Most of robustness tests against Camcording attack

are carried out on a single screen model, using a sin-

gle capture model and applying, generally, only one

scenario of equipment’s disposition. In fact, bench-

marking of the camcorder path is far from being a

frequent practice today due to the heavy logistical ob-

stacles associated with this evaluation process. To

solve this problem, some researchers are now focus-

ing on the study of the impacts and distortions caused

by the Camcording with the aim of designing pre-

cise simulators for this attack. In addition, the re-

sults of this study and analysis could be reused to im-

prove video watermarking techniques. This process

has been adopted in some previous works to model the

printing and scanning process of still-image water-

marking and the acoustic path transmission but Cam-

cording is relatively not enough studied compared

to this works focusing only on spatial deformations.

Owing to the interaction between several devices, the

displayed content of the camcorder changes video in

some various ways, including temporal transforma-

tions, geometric distortions, variable and non-uniform

luminance transformations, alteration colors satura-

tion... It is necessary to understand the different phe-

nomena involved to design effective and precise sim-

ulators that imitate these effects.

Ben Zid et al. (Cherif Ben Zid and Doerr, 2013)

have study the luminance transforms due to the Cam-

cording process and investigate three different alter-

ations which are the spatial non-uniformity, the steady

state luminance response, and the transient luminance

response. To do this, they performed several con-

trolled experiments where they simulated different

conﬁgurations of the Camcording process. They used

two alternative displays and one camcorder device

which are a 24 ”LCD monitor Dell U241014, a home

theater video projector, Christie HD5Kc15 and a Sony

HDR-CX200ETM camcorder. They then excited the

system with several visual stimuli and looked at the

recorded answers to infer the underlying mechanisms

that take place as well as their characteristics. This

study can be improved because it is focused on only

three distortions that video content undergoes along

Camcording process and uses only two displays and

one capturing device.

Hajj-Ahmad et al. (Adi Hajj-Ahmad and Wu,

2017) have lead an investigation of the lumi-

nance ﬂicker that is naturally present in camcorded

recordings due to the interplay between liquid-

crystaldisplay (LCD) screen and camcorder. To do

this, they have break down the acquisition pipeline

into three stages which are the emission of a back-

light signal by the screen, the integration of the light

emitted by the screen with a sensor of the camcorder,

and the sequential sampling of the different rows of

a video frame. They initially model the ﬂicker signal

and demonstrate that its parameters are related to such

internal characteristics of the capture devices as the

back-light frequency of the LCD screen and the read-

out time of the camcorder. Then, they introduce an

estimation strategy to recuperate these hidden param-

eters directly from camcorded recordings and demon-

strate that such forensic cues could provide intelli-

gence on the pirate devices. They additionally dis-

cuss on how to recuperate the shape of the low power

ﬂicker signal and demonstrate that it could be used to

infer which back-light technology employed in the pi-

rate LCD screen. The authors set out the prospects to

better understand the applicability of ﬂicker forensics

which will involve large scale validation experiments

with a wide diversity of devices, hence the utility of

our proposed dataset.

The most complete simulator that is already avail-

able as open source tool to researchers is the Cam-

Mark developed by Schaber et al. (P. Schaber, 2014).

This tool simulates a re-acquisition of a video from

a camcorder to support watermarking development

by enabling automated test cases for such camcorder

copy attacks (ﬁg 1). The authors are thus trying to

simulate the typical artifacts of a camcorder captur-

ing: geometric modiﬁcations (aspect ratio changes,

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152

cropping, perspective and lens distortion), tempo-

ral modiﬁcations (unsynchronized frame rates and

the resulting frame blending), sub-sampling (rescal-

ing, ﬁltering, Bayer color array ﬁlter), and histogram

changes (AGC, AWB), camera movement (e.g., a

hand-held camera) and background insertion. To do

this, they developed separate models for the various

effects that appear at various stages of the Camcord-

ing process and apply their model in a natural order

that resembles the actual physical order. In fact, there

are effects that are caused from display like crop-

ping, AR changes and visible parts of display, from

optics they consider the perspective distortion, lens

distortion and movement, from shutter there is frame

blending due to frame rate changes, from sensor there

is Resolution changes and bayer pattern interpolation

and ﬁnally the processing induces automatic gain con-

trol and automatic white balance. The results ob-

tained by CamMark are comparable to actual cam-

corder copies but cannot yet represent a real case of

camcording process. The authors set out the prospects

to consider the characteristics of different displaying

devices in future releases of CamMark tool. More-

over, this tool is not easy to use. In fact, it is based on

command line and the graphical user interface was a

part of the original ﬁrst version and was removed in

version 1.1 and we are still waiting for a new Cam-

Mark interface which will offer a web-based interface

to an entirely server-side processing.

Figure 1: CamMark simulation.

3 PROPOSED DATASET FOR

CAMCORDING VIDEOS

In this paper, we propose a video dataset dedicated to

evaluate the robustness of watermarking approaches

against camcording attack. This dataset can help re-

searchers to build their own camcording simulators by

studying the distortion caused by camcorded videos.

In more, it gives the researchers the possibility to

compare their approaches of video watermarking with

the robustness of tested techniques. In fact, the pro-

posed dataset contains different videos (marked and

not marked) recorded from different positions in a

movie theater using different capturing devices.

3.1 Chosen Benchmark Videos

The ﬁrst step in the dataset building is the choice of

test videos. Indeed, we have chosen ﬁve color free

right video (ﬁg2) which present the most sensitive

scenes for a video watermarking. The ﬁrst video is the

sequence Stephan which is a benchmark for video wa-

termarking algorithms evaluation and researches on

video processing. The second video is a sequence

of the famous cartoon HD ”Big Buck Bunny”. The

third one is a sequence of a HD cartoon ”Sintel”

which presents complex scenes with a fast luminance

changing. The fourth video is a video clip ”Varoshse-

quence” which presents a rock concert. The last one

is an Italian advertising video ”Segugio”. The charac-

teristics of these videos are presented in Table 1.

Table 1: Video tests characteristics.

Videos Resolution Size Duration

Stephan SD

346*280

7.29 Mo 0 :12 sec

Big Buck

Bunny

854*480

6.08 Mo 0 :20 sec

Sintel HD

1280*720

10 Mo 0:16 sec

Varosh Se-

quence

1280*720

9.94 Mo 0:16 sec

Segugio HD

1280*720

9.63 Mo 0:16 sec

Figure 2: Chosen videos.

Subsequently, in addition of the original versions,

we have marked these videos by using three differ-

ent video watermarking algorithms: multi-frequency

insertion (DWT-SVD-DCT) and feature region based

approach proposed by Kerbiche et al. (A. Kerbiche,

A Video Dataset for an Efﬁcient Camcording Attack Evaluation

153

2012), the algorithm proposed by Agilandeeswari et

al. (L. Agilandeeswari, 2013) and based on the

two transforms DWT-SVD, and spatial algorithm pro-

posed by Datta et al. (S. Datta, 2014) which is

based on the LSB method. We choose to test three

robust and efﬁcient video watermarking algorithms

which embed signature in different domain of inser-

tion (multi-frequency and spatial) in order to study

their robustness against a realistic case of camcording

attack. In addition, these camcorded marked videos

can be used by researches to compare their propos-

als with these videos. Moreover, the usefulness of

these camcorded watermarking versions, was to eval-

uate the proposed dataset and compare it with existing

camcording methods, and this by testing their robust-

ness.

3.2 Used Devices

The materials and capture devices that we used to

record the chosen videos are: 3 tripods, laser meter,

two smartphones (Samsung galaxy note 2 and iPhone

4s), a webcam Logitech V-u0028, a Panasonic camera

HDC-tm900 and a Canon camera EOS 450D. Each

capturing device has at least 16 GB of storage ca-

pacity and the videos are recorded with frame rate

ranging from 20 to 30 frames per second. The char-

acteristics of these capture devices are illustrated in

Table 2.

Table 2: Capture devices characteristics.

Smartphones

Processor Camera Resolution

Galaxy

Note 2

1.6 Ghz 8 Mpx 1920×1080

Quad core

2Go Ram

Iphone 4s 1 Ghz 8 Mpx 1920×1080

Apple A5

Cameras

Capturing system Resolution

Panasonic

HDC-

tm900

3MOS (2 × 2.53 Mpx) 1920×1080

Canon

EOS

450D

CMOS (12.2 Mpx) 1280×720

Webcam

Logitech

V-u0028

5 Mpx 800×600

3.3 Capturing Scenarios

Once videos are ready we displace to the Utopia cin-

ema in Toulouse which was at our disposal for 2

hours. This room is 9.78m in width, 21.7m in length,

2.84m height at the back and 3.67m in the middle. It

is composed of 224 seats (14 × 16) and the size of the

projection screen is 8.16 meters (ﬁg3).

Figure 3: Utopia cinema.

Turning to the capture step, to camcorder our

videos we have placed the capturing devices in sev-

eral places (ﬁg4): in front, in the left, in the right and

in the projection room given that the employees of

the cinema afﬁrmed that the majority of camcordred

videos are captured from the projection room. For this

reason we used two different scenarios:

Concerning the ﬁrst one, we exposed our captur-

ing devices as follows (ﬁg5):

1. The Panasonic camera to the right of the screen to

12m of the projection screen, 50cm from the wall

and 1.50m height.

2. The Canon camera to the left of the screen, to

16.6m of the projection screen, 1m from the wall

and 1.20m height.

3. Samsung Galaxy Note 2 in front of the screen

to 10.5m of the projection screen, 4.5m from the

wall and 1.50m height.

4. IPhone 4s in the left of the screen to 6.34 m of

the projection screen, 1m from the wall and 1.80m

height.

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154

Figure 4: Videos’ camcordering.

5. Logitech Webcam in the projection room in front

of the screen at 19m, 4.5m from the wall and 2.5m

height.

Figure 5: Capturing’s Scenario 1.

In the second scenario, we exposed our capturing

devices as follows (ﬁg6):

1. The Panasonic camera in the projection room in

front of the screen at 19m, 4.5m from the wall and

2.5m height.

2. Samsung Galaxy Note 2 in the right of the screen

to 13.5m of the projection screen, 1.5m from the

wall and 0.95m height.

3. IPhone 4s in the right of the screen to 13m of the

projection screen, 1.3m from the wall and 0.95m

height.

4. Logitech Webcam in front of the screen at 15m,

4.5m from the wall and 1.10m height.

3.4 Dataset Characteristics

The proposed dataset contains 180 camcordred wa-

termarked videos. Figure 7 presents some of those

Figure 6: Scenario 2.

videos: (a) Segugio video camcordred with Panasonic

in the right, (b) Sintel video camcordred with Pana-

sonic in the projection room, (c) Big Buck Bunny

video camcordred with Samsung Galaxy Note2 in the

right, (d) Stephan video camcordred in the right with

IPhone 4s and (e) VaroshSequence camcordred in the

front with webcam.

Camcordred videos by IPhone 4s and Canon cam-

era are .mov ﬁles which is a common multimedia con-

tainer ﬁle format developed by Apple and compati-

ble with both Macintosh and Windows platforms and

it commonly uses the MPEG-4 codec for compres-

sion. The video obtained by the Panasonic camera are

.MTS ﬁles which is a ﬁle extension for an AVCHD

(Advanced Video Coding High Deﬁnition) video clip

format for high-deﬁnition video. The MTS ﬁle format

supports 1080i (a high deﬁnition video format with

1080 horizontal scan lines, interlaced) and 720p (720

horizontal scan lines, progressive scan, rather than in-

terlaced) in a relatively small ﬁle size. AVCHD ﬁles

are based on the MPEG 4 codec. Finally, the video

obtained by the webcam and the Samsung Galaxy

note 2 are .mp4 ﬁles. Table3 presents the characteris-

tics of the camcordred videos.

Table 3: Camcordred videos’ characteristics.

Format Resolution

Panasonic .mts 1920 × 1080

Canon .mov 1280 × 720

IPhone 4s .mov 1920 × 1080

Galaxy note 2 .mp4 1920 × 1080

Webcam .mp4 800 × 600

The whole dataset will be soon available

on our website http://camcordingvideos.github.io/

dataset.html for download or you can drop an email

to the authors. The dataset website allows users to

search videos by watermarking algorithm (not water-

marked version, algo1, algo2, algo3) and capturing

device. The users may choose to download all result-

ing videos, or select a subset from them.

A Video Dataset for an Efﬁcient Camcording Attack Evaluation

155

Figure 7: Camcordred videos.

4 DATASET EVALUATION

The proposed dataset was evaluated based on robust-

ness results obtained after the evaluation of the three

watermarking approaches which were applied on the

proposed benchmark videos. Indeed, in order to prove

the efﬁciency of the proposed dataset, we compare

the obtained results with those obtained after applying

other techniques of camcording which are: the Cam-

Mark simulator and the results obtained after cam-

cording with a Smartphone (Samsung note 2) using a

monitor screen Dell Professional 469-3134 19” LED

LCD. Figure 8 presents camcordred versions of the

watermarked video Stephan with these three cam-

cording techniques. In more, we compare these re-

sults with those cited in the papers corresponding to

the tested approaches (given in table 4) in order to

prove that usual evaluation based on classic attacks

only, can not reﬂect the real performance of video wa-

termarking algorithms.

The robustness was evaluated on ﬁve videos ob-

tained from the proposed dataset:

• The Video Bug Buck Bunny camcordred in the

projection room with the Panasonic camera.

• The video Stphan camcordred in the right of the

projection screen with the smartphone Samsung

Galaxy note2.

• The video Sintel camcordred in the left of the

Figure 8: Different versions of camcorded Stephan video.

Table 4: Performances of the watermarking algorithms as

cited in their papers.

Algorithms (A. Ker-

biche,

2012)

(L. Ag-

ilan-

deeswari,

2013)

(S. Datta,

2014)

Robust- MPEG4 Good Good Poor

ness Collusion Good Poor Poor

Rotation Good Good Good

Cropping Good Good Good

Noise Good Good Poor

Invisibility Good Good Good

screen projector with IPhone 4s.

• The video VaroshSequence camcordred in front of

the screen projector with the Logitech webcam.

• The video Segugio camcordred in the left of the

screen projector with the Canon camera.

For the CamMark simulator we have used the

standard conﬁguration presented in ﬁgure 9 and pro-

jected on the three simulated screens: SonyHDR-

TD20 Monitor BrightEnv, SonyHDR-TD20 Projec-

tion BrightEnv and SonyHDR-TD20 Projection Dark-

Env. Then, the robustness was tested on these videos:

• The Video Bug Buck Bunny simulated with the

projector SonyHDR-TD20 Monitor BrightEnv.

• The video Stephan SonyHDR-TD20 Projection

BrightEnv.

• The video Sintel SonyHDR-TD20 Projection

DarkEnv.

• The video VaroshSequence SonyHDR-TD20

Monitor BrightEnv.

• The video Segugio SonyHDR-TD20 Projection

DarkEnv.

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156

Finally, for the third type of camcording, we have

camcorded the same videos and positions used for

the test of the proposed dataset using the smartphone

Samsung note 2 and a LCD screen monitor. The ob-

tained robustness results are shown in the table 5.

Figure 9: Conﬁguration of the CamMark simulator.

Table 5: Comparative study of Camcording techniques

based on robustness of the tested watermarking algorithms.

Algorithms (A. Ker-

biche,

2012)

(L. Ag-

ilan-

deeswari,

2013)

(S. Datta,

2014)

Proposed

dataset

(a) X X -

(b) - - -

(d) X X -

(e) X - -

CamMark (a) X X X

(b) X X -

(d) - X -

(e) X - -

Camcording (a) X X X

on a LSD (b) X - -

screen (c) X X -

monitor (d) X X -

(e) - X -

According to this comparative study, we can no-

tice that despite the performance proved by the au-

thors of the tested watermarking algorithms and the

good robustness results obtained with usual attacks,

some algorithms have not resist to camcording at-

tack and we have failed to detect the presence of the

mark on several tested videos whatever the camcord-

ing techniques used. In fact, the algorithm proposed

by Datta et al. (S. Datta, 2014), which is based on

the insertion in spatial domain, doesn’t succeed to de-

tect the mark on any video while for the algorithm

proposed by Kerbiche et al. (A. Kerbiche, 2012) we

were able to detect the presence of the mark on the

most of selected videos.

Moreover, we can also notice that for each cam-

cording technique we have obtained different results.

In fact, we success to detect the inserted mark on

some videos camcordred by the CamMark simulator

and by using the standard capturing scenario (Cam-

cording on a LCD screen monitor) but we fail to de-

tect it on the dataset’s video. In fact, for the algorithm

proposed by Datta et al. (S. Datta, 2014) the detector

fails to extract the mark for the ﬁve dataset’s videos

but successes for the CamMark simulated video and

the camcorded video on a LCD screen. These results

are logical as the proposed dataset represents the most

realistic case of camcording which causes a variety of

distortions that are not yet well simulated. In addition,

the test of camcording using only a standard screen

monitor and using one capture device is insufﬁcient

to evaluate the robustness against this attack.

5 POTENTIAL APPLICATIONS

Among applications that can use the proposed dataset

and beneﬁt from the availability of such a dataset, we

can enumerate:

• Evaluation protocols of video watermarking algo-

rithms: The proposed dataset can be used in or-

der to compare the efﬁciency of video watermark-

ing algorithms against the Camcording attacks.

In fact, the captured watermarked videos can be

used directly by video watermarking researches

to compare their algorithms directly with the wa-

termarked videos in the proposed dataset or after

applying combination of attacks on them.

• Camcorder’s simulators development: This

dataset can greatly helps the studies on camcorder

simulators. In fact, these videos are captured with

several capture devices and with different dispo-

sition scenarios of these tools, which will allows

researchers in this ﬁeld to study the effects and

impacts caused by camcorder process and try to

simulate them.

A Video Dataset for an Efﬁcient Camcording Attack Evaluation

157

6 CONCLUSION

This paper presents a survey of Camcording attack

and proposes a camcordred videos dataset which con-

tains recorded videos from a movie theater with sev-

eral capture devices and from different shooting. This

dataset can be useful for the comparative study of

video watermarking algorithms. In fact, it will allow

researchers comparing their approaches with the tech-

niques tested in the proposed dataset. Moreover, ex-

perimental results show that the robustness evaluation

based on the proposed dataset is more realist and efﬁ-

cient than the evaluation based on Camcording simu-

lators thanks to the diversity of the used capturing de-

vices and the real conditions of videos recording. For

this reason, the proposed dataset is useful, specially,

to improve the camcorder’s simulators development

by helping researches studding the impact of Cam-

cording and the caused distortion using several cap-

ture devices on videos. In future, we intend to carry

out an in-depth study of the distortions caused by the

camcording on the dataset’s videos.

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