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Compressed and raw video steganography techniques:

a comprehensive survey and analysis

Ramadhan J. Mstafa

1

&Khaled M. Elleithy

1

Received: 11 April 2016 /Revised: 28 September 2016 / Accepted: 6 October 2016 /

Published online: 9 November 2016

#Springer Science+Business Media New York 2016

Abstract In the last two decades, the science of covertly concealing and communicating data

has acquired tremendous significance due to the technological advancement in communication

and digital content. Steganography is the art of concealing secret data in a particular interactive

media transporter, e.g., text, audio, image, and video data in order to build a covert commu-

nication between authorized parties. Nowadays, video steganography techniques have become

important in many video-sharing and social networking applications such as Livestreaming,

YouTube, Twitter, and Facebook because of the noteworthy development of advanced video

over the Internet. The performance of any steganographic method ultimately relies on the

imperceptibility, hiding capacity, and robustness. In the past decade, many video steganogra-

phy methods have been proposed; however, the literature lacks of sufficient survey articles that

discuss all techniques. This paper presents a comprehensive study and analysis of numerous

cutting edge video steganography methods and their performance evaluations from literature.

Both compressed and raw video steganography methods are surveyed. In the compressed

domain, video steganography techniques are categorized according to the video compression

stages as venues for data hiding such as intra frame prediction, inter frame prediction, motion

vectors, transformed and quantized coefficients, and entropy coding. On the other hand, raw

video steganography methods are classified into spatial and transform domains. This survey

suggests current research directions and recommendations to improve on existing video

steganography techniques.

Keywords Vide o st eg ano gr ap hy .Compressed domain .Raw domain.Video processing .Smart

video transmission .Smart video tracking .Imperceptibility.Hiding capacity

Multimed Tools Appl (2017) 76:21749–21786

DOI 10.1007/s11042-016-4055-1

*Ramadhan J. Mstafa

rmstafa@my.bridgeport.edu

Khaled M. Elleithy

elleithy@bridgeport.edu

1

Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604,

USA

1 Introduction

In spite of the fact that the Internet is utilized as well-known venues for users to access desired

data, it has likewise opened another entryway for attackers to obtain precious and intellectual

information of other users with little exertion [11]. Steganography has functioned in a

complementary capacity to offer a protection mechanism that prevents eavesdroppers from

any ongoing communication between an authorized transmitter and its recipient [23]. Stega-

nography is characterized as the art of concealing secret information in specific carrier data,

establishing covert communication channels between official parties [105]. Subsequently, a

stego object (steganogram) should be same as an original data that has the same statistical

features. Carrier data is also referred as cover or host data [61,84]. Carriers can be acknowl-

edged in various forms such as text, audio, image, and video. A hidden message can also

appear in any form of data such as such as text, audio, image, and video [24,59]. The primary

objective of steganography is to remove any hacker’s suspicion to the transmission of hidden

messages and provide security and anonymity for legitimate parties. Simple way to observe the

steganogram visual quality is to determine its accuracy which is achieved through the human

visual system (HVS). The HVS cannot identify slight distortions in steganogram, thus

avoiding suspiciousness [97]. However, if the size of the hidden message in proportion with

the size of the carrier object is large, then the steganogram’s degradation will be visible to the

human eye resulting in a failed steganographic method [34]. Figure 1represents the general

model of steganographic method.

Embedding efficiency, hiding capacity, and robustness are the three major requirements

incorporated in any successful steganographic methods [19,26]. First, embedding efficiency

can be determined by answering the following questions [68,83]: 1) How safe is the

steganographic method to conceal the hidden information inside the carrier object? 2) How

precise are the steganograms’qualities after the hiding procedure happens? and 3) Is the secret

message undetectable from the steganogram? In other words, the steganographic method is

highly efficient if it includes encryption, imperceptibility, and undetectability characteristics.

The high efficient algorithm conceals the covert information into the carrier data by utilizing

Original

data

(carrier)

Encryption

and

encoding

methods

Secret

message

Embedding

algorithm

Stego data

Unsecure

Network

Fig. 1 General diagram of the steganography method

21750 Multimed Tools Appl (2017) 76:21749–21786

some of the encoding and encryption techniques prior to the embedding process to enhance the

security of the algorithm [22,90].

Obtained steganograms with low alteration rate and high quality do not draw the hacker’s

attention, and thus will avoid any suspicion to the sending of covert information. If the

steganography method is more effective, then the steganalytical detectors will find it more

challenging to detect the hidden message [21,89].

The hiding capacity is the second fundamental requirement which permits any steganog-

raphy method to expand the size of hidden message taking into account the visual quality of

steganograms. The hiding capacity is the quantity of the covert messages needed to be inserted

inside the carrier object. In ordinary steganographic methods, both hiding capacity and

embedding efficiency are contradictory [13,90]. Conversely, if the hiding capacity is expand-

ed, then the quality of steganograms will be diminished which decreases the algorithm’s

efficiency. The embedding efficiency of the steganographic method is directly affected on its

embedding payload. To expand the hiding capacity with the minimum alteration rate of the

carrier object, many steganographic methods have been presented using different strategies.

These methods utilize linear block codes and matrix encoding principles which include Bose,

Chaudhuri, and Hocquenghem (BCH) codes, Hamming codes, cyclic codes, Reed-Solomon

codes, and Reed-Muller codes [20,113].

Robustness is the third requirement which calculates the steganographic method’s strength

against attacks and signal processing operations [42]. These operations contain geometrical

transformation, compression, cropping, and filtering. A steganographic method is robust when

the recipient obtains the hidden information accurately, without any flaws. High efficient

steganography methods withstand against both adaptive noises and signal processing opera-

tions [69,110]. Recently, a large number of video steganography techniques have been

proposed in the literature. Unfortunately, the literature lacks of video steganography survey

articles. Therefore, this leads to present an extensive study that includes all video steganog-

raphy techniques for the past decade. This paper unlike others provides a comprehensive

survey and analysis of the state-of-the-art video steganography methods in both compressed

and raw domains. In addition, this survey not only investigates the existing video steganog-

raphy techniques but also provides recommendations and future directions to enhance those

methods. The remaining parts of the paper are organized as follows: Section 2explains

steganography versus cryptography and watermarking. A comprehensive study and analysis

of the state-of-the-art video steganography methods in compressed and raw domains is given

in 3. Section 4presents some of well-known performance assessment metrics. Section 5

summarizes the key findings of this survey, advises some recommendations to improve the

existing methods, and suggests future research directions.

2 Steganography versus cryptography and watermarking

The common objective of both steganography and cryptography is to provide confidentiality

and protection of data. The steganography Bprotected writing^establishes a covert communi-

cation channel between legitimate parties; while the cryptography Bsecret writing^creates an

overt communication channel [1]. In cryptography, the presence of the secret data is recog-

nizable; however, its content becomes unintelligible to illegitimate parties. In order to increase

additional levels of security, steganography and cryptography can operate together in one

system [14,63].

Multimed Tools Appl (2017) 76:21749–21786 21751

Tab le 1 Comparison of steganography, cryptography, and watermarking techniques

Description Steganography Cryptography Watermarking

Goal achieved: Communication channels are covert Data content of communication channels

are covert

Copyright protection exists

Goal failed: Communication is detected Plain-text is retrieved Watermark is erased or exchanged

Common carrier

file:

Text, audio, image, or video Plain-text or image Image or video

Secret information: Any type of data plain-text watermark

Secret keys: May exist Must exist May exist

Extraction phase: Carrier data is unnecessary Carrier data is unnecessary during

deciphering process

Carrier data availability depends on the application

Output file: Steganogram Cryptogram Watermarked object

Security level: Depends on the embedding algorithms Depends on the secret keys Depends on the watermarking algorithms

Information

transparency:

Invisible Visible Transparency depends on the application

Robustness level: Against detection Against deciphering Robust watermarking, fragile watermarking, and

semi-fragile watermarking

Common attacks: Steganalysis Cryptanalysis Signal processing operations

Requirements: Embedding efficiency, embedding payload,

undetectability, and robustness

Robustness Robust watermarking requires robustness while fragile

and semi-fragile watermarking do not need robustness

21752 Multimed Tools Appl (2017) 76:21749–21786

Digital watermarking techniques use a preservation mechanism to protect the copyright

ownership information from unauthorized users. This process is accomplished by concealing

the watermark information into overt carrier data [40]. Like steganography, watermarking can

be used in many different applications such as content authentication, digital fingerprints,

broadcast monitoring, copyright protection, and intellectual property protection [28,29,40,49,

87]. Different watermarking techniques can be found in the literature [4,8,33,41,48,50,51,

85,88]. Table 1shows the general similarities and differences between steganography,

cryptography, and watermarking techniques.

3 Video steganography techniques

Due to the advancement of Internet and multimedia technologies, digital videos have become a

popular choice for data hiding. The video data contains a massive amount of data redundancy

which can be utilized for embedding secret data. Recently, there are many useful applications

of video steganography techniques such as video error correcting [47,70,71,86,109],

military services [81], bandwidth saving [67,96], video surveillance [62,72,112], and medical

video security [73,74,76]. Video steganography techniques are classified into compressed and

uncompressed domains. Figure 2clarifies the hierarchy of the overall system security includ-

ing video steganography, which is the main focus of this survey.

3.1 Video steganography techniques in compressed domain

The H.264 standard has increased the efficiency of video compression when compared to the

previous versions. Some new features of H.264 video codec include flexible macroblock

ordering, quarter-pixel interpolation, intra prediction in intra frame, deblocking filtering post-

processing, and multiple frames reference capability [52,94,106,108]. Usually, H.264 codec

comprises a number of group of pictures (GOP). Every GOP includes three types of frames:

intra (I) frame, predicted (P) frame, and bidirectional (B) frame. During the video compression

process, the motion estimation and compensation processes minimize the temporal

Fig. 2 Disciplines of overall system protection. The red color indicates the focus of this study

Multimed Tools Appl (2017) 76:21749–21786 21753

redundancy. Since the video stream is a number of correlated still images, a frame can be

predicted by using one or more referenced frames based on the motion estimation and

compensation techniques. First, frames are divided into 16 × 16 macroblocks (MB) wherein

each MB contains blocks that may include the smallest size of 4 × 4. When applying a few

searching algorithms, block Cin the present frame is compared, individually, to one of the

selected block R

∼in the referenced frame F

∼in order to find a corresponding block C.The

prediction error between two blocks (Cand R

∼) of size bcanbemeasuredusingsumof

absolute differences (SAD).

e¼SAD C;~

R

¼X

1≤i;j≤b

Ci;j−~

ri;j

ð1Þ

Where ci;jand r

∼i;jrefer to block values. The best matched block will have a minimum SAD

using C‘s prediction denoted by P

∼. The motion vector (MV) and differential error D¼C−P

∼

are required for the coding process. Video steganography techniques in compressed domain

are categorized according to the video coding stages as venues for data hiding such as intra

frame prediction, inter frame prediction, motion vectors, transformed and quantized coeffi-

cients, and entropy coding. Figure 3illustrates the H.264 video codec standard indicating some

venues for information hiding.

3.1.1 Video steganography techniques in intra frame prediction

During the video compression process, the macroblocks are encoded using a number of intra

prediction modes. In H.264 codec, the numbers of intra prediction modes are nine of 4 × 4

blocks and four of 16 × 16 blocks which are illustrated in Fig. 4and Fig. 5, respectively. Also,

the high efficiency video coding (HEVC) codec can support up to 35 intra prediction modes

Fig. 3 H.264 hybrid video codec standard shows venues for data hiding

21754 Multimed Tools Appl (2017) 76:21749–21786

for each 64 × 64, 32 × 32, 16 × 16, 8 × 8, and 4 × 4 block sizes as shown in Fig. 6.Fordata

concealing purposes, these modes can be mapped to one or more of secret message bits. Liu

et al. [53] presented a new secure data hiding technique which performs entirely in a

compressed domain. The framework of this algorithm consists of four stages. First, in the

video sequences parser stage, the video sequences are coded, and discrete cosine transform

(DCT) coefficients are obtained. In addition, the motion vectors, and the intra coded macro-

blocks are acquired. In the second stage, scene detection is performed on the consecutive intra

frames to identify the fluctuation scenes. The fluctuation scene is identified using a histogram

variation of DC coefficients within intra frame DCT coefficients. In the third stage, the

embedding process is achieved using only intra frames of fluctuation scenes. The last stage

is called video steganalysis. Here, the security level of the stego video is statistically measured

to determine whether it is high or low. If the stego video cannot be passed by the steganalysis,

then it will adjust the scale factor to make it stronger. The algorithm introduced by Liu et al.

has limited capacity for hidden data because the fluctuation scenes of intra frames are only

used for data embedding.

Chang et al. [9] presented a data concealing algorithm using HEVC utilizing both DCT and

discrete sine transform (DST) methods. In this scheme, HEVC intra frames are used to conceal

the hidden message without propagating the error of the distortion drift to the adjacent blocks.

Blocks of quantized DCT and DST coefficients are selected for embedding the secret data by

using a specific intra prediction mode. The combination modes of adjacent blocks will produce

three directional patterns of error propagation for data hiding, consisting of vertical, horizontal,

Fig. 4 H.264 intra prediction modes for 4 × 4 blocks

Fig. 5 H.264 intra prediction modes for 16 × 16 blocks

Multimed Tools Appl (2017) 76:21749–21786 21755

and diagonal. Each of the error propagation patterns has a range of intra prediction modes that

protect a group of pixels in any particular direction. The range of the modes begin at 0 and

ends at 34. Chang et al.‘s algorithm lacks the embedding payload because the selection of

blocks for the embedding process must meet certain conditions. Similarly, both Hu et al. [31]

and Zhu et al. [115] presented data hiding methods using intra prediction modes for H.264/

AVC. During the intra frame coding process, the secret message is embedded into the 4 × 4

luminance block. These algorithms utilize the 4 × 4 intra prediction modes in order to hide one

bit of secret information per block. The 4 × 4 intra prediction modes are divided into two

subsets based on the predefined mapping rules between the secret message and intra prediction

modes in order to embed 0 or 1 of the secret message bits. Table 2illustrates the mapping rule

of 4 × 4 intra prediction modes of the Hu et al. method, which shows that each most probable

mode and its candidate modes mapped to 0 or 1. Both Hu et al. and Zhu et al. methods achieve

Fig. 6 The 35 HEVC intra prediction modes [111]

Table 2 Mapping rules of 4 × 4

intra prediction modes [31]Most Probable Mode Candidate Modes

Mapping to 0

Candidate Modes

Mapping to 1

Mode0 1,2,3,4 5,6,7,8

Mode1 0,3,4,8 2,5,6,7

Mode2 0,3,4,8 1,5,6,7

Mode3 0,5,6,8 1,2,4,7

Mode4 0,3,6,8 1,2,5,7

Mode5 0,3,6,8 1,2,4,7

Mode6 0,3,4,8 1,2,5,7

Mode7 0,5,6,8 1,2,3,4

Mode8 0,1,3,4 2,5,6,7

21756 Multimed Tools Appl (2017) 76:21749–21786

a negligible degradation of video quality as well as a small increase on the bit rate. In general,

the steganographic techniques that use the intra frame prediction as venues for data hiding

have low capacities to embed secret messages.

3.1.2 Video steganography techniques in inter frame prediction

In many video steganography methods, the seven block sizes that include 16x16, 16x8, 8x16,

8x8, 8x4, 4x8 and 4x4 of H.264 inter frame prediction are commonly utilized as a venue to

embed the secret message by mapping each block type to a number of secret bits. Kapotas

et al. [36] proposed a data concealing algorithm for scene change detection in H.264 coding.

This method uses four different block sizes. Each one is mapped onto one pair of a secret

message. In this algorithm, the secret message consists of scene change frames information

that will be embedded into the encoded videos. This embedded information will help the scene

change detection algorithm, in H.264 video stream, functioning in real time. However, the data

hiding methods of the intra frame prediction have a very limited embedding capacity.

For example, let BNY^is the secret information that must be embedded into the inter

frame prediction blocks in H.264 codec. By using mapping rules of different block

sizes the embedding goal can be achieved. Figure 7illustrates the embedding process

using mapping rules.

3.1.3 Video steganography techniques in motion vectors

Motion vector characteristics such as horizontal and vertical components, amplitude, and

phase angles are utilized in embedding secret information. Xu et al. [107] proposed a

compressed video stream steganography. In this scheme, the embedding process relies on I,

P, and B frames. First, the hidden data is concealed into the motion vectors of, both, P and B

frames. Only the motion vectors that have a high magnitude are chosen. Here, each macro-

block has a motion vector; however, the selected macroblocks are moving rapidly. Secondly,

the control information is embedded into I frames. This control information includes the

capacity payload and segment range of each GOP. Each GOP contains one I frame which

carries the control information necessary for the data extraction phase. In addition, each GOP

has a number of P and B frames which contain secret messages in their high magnitude motion

vectors. Xu et al.‘s method has a low embedding payload because it only used the motion

vectors with a high magnitude. Pan et al. [78] presented a new steganography method in the

H.264 video standard based on the motion vectors and linear block codes. The embedding

process is achieved by using motion vectors of inter frames macroblocks, and, then discarding

Fig. 7 Using mapping rules for prediction block type to conceal BNY^characters

Multimed Tools Appl (2017) 76:21749–21786 21757

the surrounding macroblocks. By using a predefined threshold, a group of motion vectors are

selected in each video inter frame. The values (0 or 1) of selected motion vectors (MV

r

)are

obtained by calculating the phase angles (φ) illustrated in Fig. 8. By definition, phase angles

are the arctangents of both vertical (MV

v

) and horizontal (MV

h

) motion vectors’components as

given in Eq. 2.

φ¼arctan MVv

MVh

0

Å≤φi<360

Å

ð2Þ

Once the MV

r

values are obtained, the hidden information is concealed into the motion vector

array utilizing the linear block code principle. The reason for using the linear block codes is to

minimize the motion vectors’alteration rate and increase embedding capacity. The results of

this algorithm have demonstrated that in every 6 bits of motion vector array, 4 bits of the secret

data can be hidden. The pick signal to noise ratio (PSNR) of the obtained stego videos is

37.45 dB, which is proven by reducing alteration rate of motion vectors. However, this method

has a limited hiding capacity due to it is based on the number of motion vectors. The data

concealing and extracting phases of the Pan et al. method are illustrated in Eq. 3–6as follows:

SY ¼MVrHTð3Þ

b¼SY⊕Sð4Þ

MVw

r¼MVr⊕Ebð5Þ

S0¼MVrHT⊕EbHTð6Þ

Where Sand S

'

are embedded and extracted messages. MV

r

and MVw

rare original and stego

selected motion vectors. SY,E

b

,andH

T

are syndrome, coset leader of b, and transpose of parity

check matrix, respectively [78]. Comparatively, Bin et al. [7] presented a new data concealing

algorithm using the motion vector and matrix encoding processes. The naked eye can realize

Fig. 8 Motion vector

representation in [78]

21758 Multimed Tools Appl (2017) 76:21749–21786

the difference that happens when the object moves tardily, while if the object transfers rapidly,

then the change will be unnoticeable. The motion vectors that have large amplitudes are

produced from the macroblocks that move quickly. The sizable motion vectors will be utilized

for concealing the hidden message. The selected motion vectors for data embedding include

two properties: 1) the motion vector’s amplitude must be greater than the predefined threshold

T; and 2) both the vertical and the horizontal motion vector components must not be equal.

Moreover, the best component (MV

w

) of both the vertical (MV

v

) and the horizontal (MV

h

)

motion vectors are chosen based on their phase angles (θ). Then, the process of hiding the

secret message is performed using matrix encoding, reducing the modification rate of selected

motion vectors. The least significant bit (LSB) of the selected motion vectors (MV

w_LSB

)is

utilized for embedding secret bits. The average PSNR of the stego videos is 38.18 dB [7].

However, this algorithm has a low embedding capacity because the selected motion vectors

have restricted conditions. The embedding stage of the algorithm introduced by Bin et al. can

be carried out as follows:

MVw¼MVh;0≤θ<π=4

MVv;π=4≤θ<π=2

ð7Þ

θ¼arctan MVv=MVh

jj ð8Þ

MVw LSB ¼

unchanged ;if MVw¼0

1;if MVw LSB ¼0and MVw≠0

0;if MVw LSB ¼1and MVw≠0

8

<

:

ð9Þ

In a different work, Jue et al. [35] designed a new algorithm for H.264/AVC video steganog-

raphy using motion vectors as cover data. In this scheme, the luminance macroblocks for inter

frames (P and B) video coding is used. Using a predefined threshold, the motion vectors with a

large magnitude will be selected, while the motion vectors of slow objects will be discarded.

Then, the hidden data bits will be concealed into the difference of both horizontal and vertical

components for the selected motion vectors. This algorithm has improved the utilization ratio

and the embedding efficiency. The modified motion vector’sfeature(P

̂i) including the secret

message can be calculated as follows:

^

Pi¼

mod Vdx

jj

−Vdy

;2

;if Pi¼Si

mod Vdx þ0:25jj−Vdy

;2

;if Pi≠Siand

Vdx

jj

−Vdy

≥0

mod Vdx

jj

−Vdy þ0:25

;2

;if Pi≠Siand

Vdx

jj

−Vdy

<0

8

>

>

>

>

>

>

>

<

>

>

>

>

>

>

>

:

ð10Þ

P

i

and S

i

are motion vector features and secret message bits. V

dx

and V

dy

are horizontal and

vertical motion vector components, respectively. However, Jue et al.‘s scheme is limited to

the embedding payload due to the high value of the predefined threshold. Commonly, the

steganographic techniques that utilize the motion vectors as carrier objects to hide the secret

messages, have low embedding capacities. Moreover, a high modification rate on the

motion vectors will negatively influence the quality of the stego videos.

Multimed Tools Appl (2017) 76:21749–21786 21759

3.1.4 Video steganography techniques in transform coefficients (DCT, QDCT, and DWT)

The DCT, quantized discrete cosine transform (QDCT), and discrete wavelet transform

(DWT) coefficients of the luminance component are also good candidates to conceal

the secret message due to their low, middle, and high frequency coefficients for data

embedding. Huang et al. [32] presented reliable information bit hiding using the DCT

and communication theory. In order to enhance the robustness of this method, the

BCH codes and soft-decision decoding have been used. Moreover, the robustness is

also achieved by testing both the common signal processing operations and a

StirMark attack. The secret data is hidden into the DCT coefficients, especially, in

DC with the highest energy coefficient and low-frequency AC coefficients. Barni

et al. [5] presented a watermarking technique of MPEG-4 video coding based on

the video object planes. This scheme hides the watermark information into the

selected inter and intra macroblocks of each video object. Depending on the computed

frequency mask, the DCT coefficients that are greater than the predefined threshold

are chosen for the embedding process. Barni’s is flexible and easy to use for many

applications. Moreover, it is robust against some common signal processing. Addi-

tionally, Shahid et al. [93] proposed a reconstruction loop for information embedding

of intra and inter frames for H.264/AVC video codec. This method embeds the secret

message into the LSB of QDCT coefficients. Only non-zero QDCT coefficients are

chosen for data hiding process, utilizing the predefined threshold which directly

depends on the size of secret information. Edges, texture, and motion regions of intra

and inter frames are utilized in the concealing process. Shahid et al.‘salgorithm

extracts the hidden message easily and maintains the efficiency of compression

domain. On the other hand, Thiesse et al. [100–102] presented a steganography of

motion data in the chrominance and luminance of video frame components. In order to control

the modification of the sum bitrate in the H.264 codec, the motion vector indices are embedded

into the selected DCT coefficients of both luminance and chrominance components. In addition,

the hidden indices minimize the distortion drift propagation of the prediction process to the next

frames utilizing the rate-distortion optimization. The summation of the selected QDCT coeffi-

cients (Sw

i)ismodifiedasfollows:

Sw

i¼Si;if Si

jjmod2¼Ii

Siþmi;if Si

jj

mod2≠Ii

ð11Þ

Si¼X

N

n¼1

anð12Þ

Where a

n

represents quantized coefficients, and S

i

represents the summation of quantized

coefficients of the i

th

block. I

i

is the prediction index and m

i

represents shifted coeffi-

cients. Meuel et al. [64] proposed information concealing in H.264 codec for lossless

reconstruction of the region of interest (ROI). This method protects the facial features

of video stream by embedding facial regions into the DCT coefficients. Two LSBs of

non-zero QDCT coefficients are utilized to embed the facial information. Only the

skip mode is used during inter coded prediction of the ROI. Both DC and AC DCT

coefficients of ROI macroblocks are set to 1 and 0, respectively, in order to guarantee

21760 Multimed Tools Appl (2017) 76:21749–21786

predicting the original ROI macroblocks during the decoding process. The facial

pixels are determined as skin pixels if the Euclidean distance is lower than the

predefined threshold value dusing the following formula:

ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

Pu−Ref u

ðÞ

2þPv−Ref v

ðÞ

2

q<dð13Þ

Where P

u

and Ref

u

are the Cb and its reference components, respectively, P

v

and Ref

v

are the Cr and its reference components, respectively. The suggested method of Meuel

et al. achieved a high quality of the region of interest based on the lossless recon-

struction. In a different work, Yilamz et al. [109] proposed error concealment of video

sequences by steganography. In the first stage, this method detects the location of the

error which is the macroblocks that have been damaged. In the second stage, when

the error location has been found, the distortion drift direction must be reversed,

avoiding error propagation from other macroblocks. In the third stage, the reconstruc-

tion of the damaged macroblocks values is performed to fulfill successful error

concealment. In Yilamz et al.‘s algorithm, the edge information is hidden into the

non-zero QDCT coefficients with the maintained bit-rate and channel utilization, and

improved video quality. Later, Li et al. [44] proposed recoverable privacy protection

for the video content distribution. This method utilizes DWT sub-bands of the region

of interest in order to generate both a hidden message and a carrier. The middle and

high frequency DWT coefficients are considered as carrier data, while the low

frequency DWT sub-band is considered as secret information. The process of embed-

ding is applied only on the luminance component. Additionally, Stanescu et al. [96]

presented a video steganography algorithm called BStegoStream^, which embeds the

subtitle messages into the MPEG-2 video streams without using an extra bandwidth.

In MPEG-2 compressed videos, the intra frames are self-dependent frames. Only the

intra frames are used for the embedding process to hide video subtitles as secret

messages. After the quantization process and the necessary predefined threshold T has

been reached, the number of blocks are selected for data hiding. The LSBs of the

non-zero DCT coefficients of the selected blocks that do not match with the hidden

information bits alter; otherwise, the LSBs of the non-zero DCT coefficients remain

unchanged. The video subtitle must appear in certain time. However, choosing an

inconvenient threshold will cause the video subtitle to appear on the screen, incor-

rectly. Moreover, since the common MPEG-2 videos have 4–5 intra frames every one

second, the video subtitles will not repeat continuously. Moreover, Li et al. [45]

proposed a new algorithm for H.264 video steganography. During the video coding

process, the quantized coefficients in each 4 × 4 luminance of inter frame macro-

blocks are used for embedding the secret message. The majority zero values of

quantized coefficients are located on the bottom-right corner because it is a high

frequency region. Conversely, the majority of non-zero values of quantized coeffi-

cientsbelongingtolowfrequencybandarelocatedonthetop-leftcorner.Anarrayof

inverse zigzag scan mode equaled to every 16 quantized coefficients will be produced

in order to obtain the last non-zeros more efficiently. Using a predefined threshold T

(0–15), based on the scan point, the last non-zero coefficient is selected in every macroblock.

Multimed Tools Appl (2017) 76:21749–21786 21761

Depending on the parity of odd and even, the secret message of 1-bit per block is concealed. If

the hidden bit is 1, then the selected DCT coefficients (V) modifies as follows:

^

V¼

V;if Vmod2¼1

V−1;if Vmod2¼0and V >0

j

Vþ1;if Vmod2¼0and V <0j

8

<

:

ð14Þ

Otherwise, the selected DCT coefficients (V) are modified as follows:

^

V¼

V;if Vmod2¼0

Vþ1;if Vmod2¼1and V >0

j

V−1;if Vmod2¼1and V <0

j

8

<

:

ð15Þ

Li et al.‘s method has limited data embedding payload because the selected blocks embed

only one bit per 4 × 4 block. Correspondingly, both Ma et al. [60] and Liu et al. [54] presented a

video data hiding for H.264 coding without having an error accumulation in intra video frames.

In the intra frame coding, the current block predicts its data from the encoded adjacent blocks,

specifically from the boundary pixels of upper and left blocks. Thus, any embedding process

that occurs in these blocks will propagate the distortion, negatively, to the current block. In

addition, the distortion drift will be increased toward the lower right intra frame blocks. To

prevent this distortion drift, authors have developed three conditions to determine the directions

of intra frame prediction modes. The 4 × 4 blocks have nine prediction modes (0–8)and16×16

blocks have four prediction modes (vertical, horizontal, DC, and plane). In the 4 × 4 block, the

first condition is the right mode {0, 3,7}; the second condition is both the under-left mode {0,

1, 2, 4, 5, 6, 8} and the under mode {1, 8};and the third condition isthe under right-mode {0, 1,

2, 3, 7, 8}. To select 4 × 4 QDCTcoefficients of the luminance component for data embedding,

the three conditions must be presented together. However, the two methods have a low

embedding payload because only the luminance of the intra frame blocks that meet the three

conditions are selected for hiding data. Later, Liu et al. [55,56] presented a robust data hiding

using H.264/AVC codec without a deformation accumulation in the intra frame based on BCH

codes. By using the directions of the intra frame prediction, the deformation accumulation of the

intra frame can be prevented. Some blocks will be chosen as carrier object for concealing the

covert message. This procedure will rely on the prediction of the intra frame modes of adjacent

blocks to prevent the deformation that proliferates from the neighboring blocks. The authors

used BCH encoding to the hidden message before the embedding phase to enhance the method

performance. Then, the encoded information is concealed into the 4 × 4 QDCT coefficients

using only a luminance plane of the intra frame. Liu et al. defined Nas a positive integer and Y

∼

ij

as selected DCTcoefficients (i, j = 0,1,2,3). The embedding process of this method is carried out

by the following steps:

1. If Y

∼

ij

¼Nþ1or Y

∼

ij

≠N, then the Y

∼

ij will be modified as follows:

Y

∼

ij ¼

Y

∼

ij þ1ifY

∼

ij≥0and jY

∼

ijj¼Nþ1

Y

∼

ij−1ifY

∼

ij <0and jY

∼

ijj¼Nþ1

Y

∼

ij if jY

∼

ijj≠Nþ1orjY

∼

ijj≠N

8

>

>

<

>

>

:

ð16Þ

21762 Multimed Tools Appl (2017) 76:21749–21786

2. If the secret bit is 1 and Y

∼ij

¼N, then the Y

∼ij will be changed as follows:

Y

∼

ij ¼Y

∼

ij þ1ifY

∼

ij≥0and Y

∼

ij ¼N

Y

∼

ij−1ifY

∼

ij <0and Y

∼

ij ¼N

(ð17Þ

3. If the secret bit is 0 and Y

∼

ij

¼N, then the Y

∼

ij will not be modified.

Overall, the previous methods that use DCT, QDCT, and DWT coefficients as

venues to hide secret information are restricted to the limited number of coefficients

in the embedding phase. Moreover, many mentioned algorithms did not include the

secret message and cover data preprocessing stages which are necessary to improve

security and robustness of any of the steganographic methods.

3.1.5 Video steganography techniques in entropy coding CAVLC and CABAC

During the H.264 compression, context adaptive variable length coding (CAVLC) and

context adaptive binary arithmetic coding (CABAC) entropy coding can be used as

host data to carry secret messages within many video steganography techniques. Ke

et al. [38] presented a video steganography method relies on replacing the bits in

H.264 stream. In this algorithm, CAVLC entropy coding has been applied in the data

concealing process. During the video coding and after the quantization stage, authors

used non-zero coefficients of high frequency regions for the luminance component of

the embedding process. Here, non-zero coefficients in high frequency bands are

almost B+1^or B-1^.Theembeddingphasecanbecompletedbasedonthetrailing

ones sign flag and the level of the codeword parity flag. The sign flag of the trailing

ones changes if the embedding bit equals B0^and the parity of the codeword is even.

Also, the sign flag changes if the embedding bit equals B1^and the parity of the

codeword is odd. Otherwise, the sign flag of the trailing ones does not change. The

trailing ones are modified as follows:

Trailing Ones ¼even codeword ;if secret bit ¼0

odd codeword ;if secret bit ¼1

ð18Þ

The modification of high frequency coefficients does not have an impact on the video

quality. However, the embedding capacity is low because Ke et al.‘smethodis

established on the non-zero coefficients of the high frequencies that consist of a large

majority of zeros. Similarly, Liao et al. [46] proposed real-time data concealing in

H.264/AVC codec. During the process of CAVLC in 4 × 4 blocks, the trailing ones

are utilized for embedding the secret data. The performance of this method was

achieved through low computational complexity, negligible degradation of the video

quality, and an unchangeable bit-steam size almost. This method employed random

Multimed Tools Appl (2017) 76:21749–21786 21763

sequences as secret data. It is embedded into the selected blocks of CAVLC trailing

onesasfollows:

^

TOnes ¼

2;if w ¼0and T railing Ones ≥3

1;if w ¼1and T railing Ones ¼2;

or

w¼1and Trailing Ones ¼0

0;if w ¼0and T railing Ones ¼1

unchanged ;otherwise

8

>

>

>

>

>

>

>

>

<

>

>

>

>

>

>

>

>

:

ð19Þ

Where wrepresents secret data that is hidden into the trailing ones codeword

within range of 0 to 3. T̂Ones represents modified trailing ones. Additionally, Lu

et al. [58] proposed real-time frame dependent video watermarking in VLC coding.

In order to achieve the real-time detection, the CAVLC encoder is applied during

this algorithm. During the process of video coding, the secret data is embedded

into the run-level pairs of each frame’s macroblocks keeping the bit-rate almost

unchangeable. Table 3illustrates run-level pairs (r, l) and codewords of the

CAVLC encoder. The diagram of the data hiding process was introduced by Lu

et al., and is illustrated in Fig. 9.

Mobasseri et al. [65]proposedwatermarkingofMPEG-2standardinacom-

pressed domain by utilizing CAVLC mapping. During the CAVLC encoder, there are

some run-level pairs that cannot systematically meet each other in intra frame blocks

called unused pairs. The secret data is embedded into the codewords of unused run-

level pairs of the CAVLC entropy coding. This method achieved a low modification

rate of the selected run-level pairs which keeps the visual quality and bit-stream size

of the watermarked video nearly unchanged. In a different work, Wang et al. [104]

presented a real-time watermarking method in the H.264/AVC codec based on the

CABAC features. The CABAC encoder uses a unary binarization, which is a

process of concatenating all binary values of syntax elements. A certain number

of motion vectors for both P and B frames are utilized for the data hiding process

using the CABAC properties. The secret watermark is concealed by displacing the binary

sequence of the selected syntax elements orderly. This method achieves a low degradation of

the video quality because of the difference between the original code and the replacement code

is very small (at most 1 bit is altered out 8-bits of the selected motion vector). This small

Table 3 VLC table (s denotes the

sign bit) (run, level) Variable length code Bit length

(0,1) 11 s 3

(0,2) 0100 s 5

(0,3) 0010 1 s 6

(0,4) 0000 110 s 8

(0,5) 0010 0110 s 9

21764 Multimed Tools Appl (2017) 76:21749–21786

difference is also the reason of achieving a little bit-rate increase. The percentage of the

increased bit-rate μis calculated as follows:

μ¼m−u

u100%ð20Þ

where uand mindicate the bit-rate of the original and the watermarked videos respectively.

The flowchart of this method is illustrated in Fig. 10. The diagram of the CABAC encoder is

shown in Fig. 11. Generally, the previous methods that utilize CAVLC and CABAC entropy

coding as venues to conceal secret messages are limited in capacity due to the restricted

number of selected blocks in the embedding stage. Moreover, when using the entropy coding,

the quality of the steganogram is severely distorted.

Table 4summarizes video steganography methods that operate in compressed domain, empha-

sizing each of embedding capacity, video quality, robustness against attackers, video preprocessing,

and secret messages preprocessing. Table 5clarifies the advantages and limitations of each venue for

Fig. 9 Diagram of the hiding process in method [58]

no

St

E

ABS(mv

ABS(mv

Replace th

the water

yes

yes

tart

End

d) [1, 8]

vd)%2!=W

i

he mvd with

marked one

no

yes

ABS(mvd)

Mvd=1

no

)==9

10

Fig. 10 The data embedding

framework in [104]

Multimed Tools Appl (2017) 76:21749–21786 21765

concealing secret messages in compressed domain. These venues include intra frame prediction,

inter frame prediction, motion vectors, DCT coefficients, QDCT coefficients, DWT coefficients,

CAVLC entropy coding, and CABAC entropy coding.

3.2 Video steganography techniques in raw domain

Unlike the compressed video, raw video steganography techniques deal with the video as a

sequence of frames with the same format. First, digital video is converted into frames as still

images, and then each frame is individually used as carrier data to conceal the hidden

information. After the embedding process, all frames are merged together to produce the stego

video. Raw video steganography techniques operate in both spatial and transform domains [75].

3.2.1 Video steganography techniques in spatial domain

There are many steganographic techniques that rely on the spatial domain such as LSB

substitution, bit-plane complexity segmentation (BPCS), spread spectrum, ROI, histogram

manipulation, matrix encoding, and mapping rule. Basically, these techniques utilize the pixel

intensities to conceal the secret message. Zhang et al. [114] presented an efficient embedder

utilizing BCH encoding for data hiding. This embedder hides the covert information into a

block of carrier object. The concealing phase is achieved by modifying different coefficients in

the input block to set the syndrome values null. This method enhances embedding payload and

execution duration compared to others. The error correcting code (ECC) and steganographic

model of this method is shown in the Fig. 12. Zhang et al.‘s method modifies the complexity of

the algorithm from exponential to linear. On the other hand, Diop et al. [16] presented an

adaptive steganography method utilizing the low-density parity-check codes. The method

discusses how to reduce the influence of hidden information insertion by this codes. This

algorithm demonstrated that the low-density parity-check codes are better for encoding algo-

rithms than other codes. The process of embedding and extraction can be accomplished by

Eq. 21 and Eq. 22.

S¼Embedding I;mðÞ ð21Þ

m¼Extraction mðÞ¼HS ð22Þ

Fig. 11 General block diagram of the CABAC encoder

21766 Multimed Tools Appl (2017) 76:21749–21786

Tab le 4 Venues, embedding capacity, video quality, robustness, video and message preprocessing of the surveyed video steganography techniques that utilize compressed domain for

data hiding

Technique Domain / venue

for data hiding

Embedding capacity Video quality Robustness Video

preprocessing

Message

preprocessing

Liu et al. [53] Compressed domain /

Intra frame prediction

Low embedding capacity

(only luma DCT coefficients

of scene change

Intra frames are used)

PSNR ranges 36 –42 dB Robust against video

compression

Not used Encryption

Chang et al. [9] Compressed domain /

Intra frame prediction

Average of embedding capacity

ratio is 1.04 % (N bits

per N × N DCT block

of Intra frames)

37 dB when the bitrate

is 20,000 Kb/s

Robust against HEVC

compression

Not used Not used

Hu et al. [31] Compressed domain /

Intra frame prediction

modes

At most 1 bit per qualified

Intra 4 ×4 luma block

Almostthesameas

compressed video

Robust against H.264/AVC

compression

Not used Not used

Zhu et al. [115] Compressed domain /

Intra frame prediction

modes

At most 1 bit per qualified

Intra 4 ×4 luma block

Almostthesameas

compressed video

Robust against H.264/AVC

compression

Not used Not used

Kapotas et al. [36] Compressed domain /

Inter frame prediction

modes

Low embedding capacity

(at most 3960 bits’

capacity with the bitrate

variation 85 % for 20

scene change frames of luma

component of resolution

176 × 144)

Almostthesameas

compressed video

Robust against H.264

compression

Scene change

detector

Not used

Xu et al. [107] Compressed domain /

Motion vectors

Low embedding capacity

(at most 537 bits in 990

P-frame macroblocks,

4519 bits in 2640 B-frame

macroblocks,

and control information of

each GOP in I-frame)

I-frame 35.22 dB, P-frame

34.61 dB, and B-frame

33.31 dB

Robust against MPEG

compression

Not used Not used

Pan et al. [78] Compressed domain /

Motion vectors

Low embedding capacity

(at most 4 bits in 6 bits

of high amplitude motion

vectors and the modification

of 2 bits)

Average PSNR is 37.45 dB Robust against H.264

compression

Not used Not used

Bin et al. [7] Compressed domain /

Motion vectors

Low embedding capacity

(motion vector

amplitude must exceed the

Average PSNR is 38.18 dB Robust against H.264

compression

Not used Not used

Multimed Tools Appl (2017) 76:21749–21786 21767

Tab le 4 (continued)

Technique Domain / venue

for data hiding

Embedding capacity Video quality Robustness Video

preprocessing

Message

preprocessing

threshold value and

both components

must not be equal)

Jue et al. [35] Compressed domain /

Motion vectors

Low embedding capacity

(at most 55

bits per P-frame or

B-frames macroblocks.

Largest amplitudes of motion

vectors are used)

Average PSNR is 36.27 dB Robust against H.264/AVC

compression

Not used Not used

Huang et al. [32] Compressed domain /

DCT coefficients

Low embedding capacity

(32 character per frame/

image of resolution 352 × 288)

Average PSNR is 44 dB Robust against StirMark 3.1

attack (signal processing)

Not used BCH codes

Barni et al. [5] Compressed domain /

DCT coefficients

Low embedding capacity

(at most 30 bits per video

object of 500 Kb/s)

Almostthesameas

compressed video

Robust against MPEG-4

compression

Not used Not used

Shahid et al. [93] Compressed domain /

QDCT coefficients

Average of embedding capacity

ratio is 0.98 % (at most

195 kbps or 20 bits

per macroblock)

Average PSNR is 43.39 dB Robust against H.264/AVC codec Not used Not used

Thiesse et al.

[100–102]

Compressed domain /

QDCT coefficients

The motion vector indices are

embedded

into QDCT coefficients

of luma and chroma

Almostthesameas

compressed video

Robust against H.264/AVC

compression

Not used Not used

Meuel et al. [64] Compressed domain /

QDCT coefficients

An average of 25 Kbits per

frame when the bitrate

is 3828 Kbits/s

Average PSNR is 47.71 dB

when

the bitrate is 3828 Kbits/s

Robust against H.264

compression

RIO (skin detection) Not used

Yilamz et al. [109] Compressed domain /

QDCT coefficients

Intra frame: 8–13 bits of

bit-length for

resynchronization and 4

bits of edge-direction

for damage. Inter frame:

MV row hides in a

corresponding

row of the next frame

Y component: 36.00 dB, U

component:

39.96 dB, V component:

41.24 dB when the bitrate

is 500 Kbits/s

Robust against

H.263+ codec

Not used Not used

Li et al. [44] Compressed domain /

DWT coefficients

An average of 38 Kbits per

frame of resolution

Average PSNR is 35.50 dB

when the first level

of DWT is used

Robust against JPEG/

JPEG2000

compression

RIO (object

detection by

GMM)

Not used

21768 Multimed Tools Appl (2017) 76:21749–21786

Tab le 4 (continued)

Technique Domain / venue

for data hiding

Embedding capacity Video quality Robustness Video

preprocessing

Message

preprocessing

352 × 288 when the first

level of DWT is used

Stanescu et al. [96] Compressed domain /

DCT coefficients

Low embedding capacity

(an average

of 1 bit per 8 × 8 block)

N/A Robust against

MPEG-2 codec

Not used Not used

Li et al. [45] Compressed domain /

QDCT coefficients

Low embedding capacity

(at most 1 bit per 4 ×4

luma block)

Average PSNR is 36 dB

of Intra frame

Robust against

H.264 codec

Not used Not used

Ma et al. [60] Compressed domain /

QDCT coefficients

Average of embedding

capacity ratio

is 0.10 % (at average

798 bits per Intra frame

of re solution 176 × 144)

or (embedding ratio

is 0.08 %)

Average PSNR is 40.74

dB of all Intra frames

Robust against

H.264/AVC codec

Not used Not used

Liu et al. [54] Compressed domain /

QDCT coefficients

Low embedding capacity

(at average 758 bits per

Intra frame of size

176 × 144 or 15,155 bits

in 20 Intra frames)

Average PSNR is 40.73 dB

of all Intra frames

Robust against

H.264/AVC codec

Not used Not used

Liu et al. [55,56] Compressed domain /

QDCT coefficients

Average of embedding

capacity ratio is 0.09 %

(at most 3541 bits are

embedded in 20 Intra

frames of resolution

176 × 144)

Average PSNR is 46.35

dB of all Intra

Robust against

H.264/AVC codec

Not used BCH codes

Ke et al. [38] Compressed domain /

CAVLC

Embedding rate 2.44 %

when quantization

parameter(QP)=28

and video resolution

is 352 × 288 or 1 bit

per 4 × 4 residual block

Average PSNR is 34.54 dB

when QP = 28 and video

resolution is352 × 288

Robust against

H.264 compression

Not used Not used

Liao et al. [46] Compressed domain /

CAVLC

Low embedding capacity

(at most 100 bits

are embedded in 20th

Intra frame of resolution

352 × 288)

Average PSNR is 34.37 dB

when video resolution

is 352 × 288

Robust against

H.264/AVC codec

Not used Not used

Multimed Tools Appl (2017) 76:21749–21786 21769

Tab le 4 (continued)

Technique Domain / venue

for data hiding

Embedding capacity Video quality Robustness Video

preprocessing

Message

preprocessing

Lu et al. [58] Compressed domain /

CAVLC

N/A Average PSNR is 37 dB Robust against MPEG-2

codec and geometric

attacks

Not used Not used

Mobasseri et al. [65] Compressed domain /

CAVLC

Low embedding capacity

(an average of 1 bit

per 8 × 8 Intra block)

Almostthesameas

compressed video

Robust against MPEG-2

encoder

Not used Not used

Wang et al. [104] Compressed domain /

CABAC

Average of embedding

capacity ratio is 0.57 %

(1156 bits are embedded

in 50 frames of resolution

176 × 144)

Almostthesameas

compressed

video (average PSNR

is around 37.05 dB)

Robust against H.264/

AVC codec

Not used Not used

21770 Multimed Tools Appl (2017) 76:21749–21786

Where Iand Sare the cover data and steganogram, respectively, and mis a secret message

(m∈Fm

2Þ. Additionally, Cheddad et al. [10] presented a skin tone data concealing method which

depends on the YCbCr color space. YCbCr is utilized in different methods such as object detection

and compression techniques. In YCbCr, the correlation between RGB colors is isolated by

separating the luminance (Y) from the chrominance blue (Cb) and the chrominance red (Cr).

Subsequently, the human skin areas are recognized, the Cr of these areas are used for concealing

the hidden information. Overall, the method has a limited embedding capacity because the hidden

message is embedded only in the Cr plane of the skin region. Similarly, Sadek et al. [91]proposeda

robust video steganography method based on the skin region of interest. The secret message is

concealed into the wavelet coefficients of skin regions for each blue and red components. This

method is robust against MPEG compression. However, the results of comparison demonstrated that

Cheddad et al.‘s method outperformed Sadek et al.‘s algorithm in both imperceptibility and

embedding capacity. Khupse et al. [43] presented an adaptive information hiding scheme using

steganoflage, which is based on the ROI in a frame instead of utilizing an entire frame. This method

utilized human skin tone as a carrier object for concealing the hidden data. The filling operation and

morphological dilation techniques have been applied as a skin detection. Then, the YCbCr frame that

Tab le 5 Advantages and disadvantages of each venue for data concealing in compressed domain

Venues for data hiding Characteristics (According to compressed

video steganography techniques)

Limitations

Intra frame prediction The computational complexity is moderate The embedding capacity is low

and the impact on the video

quality is high

Inter frame prediction The influence on the video quality and

the computational complexity are low

The embedding capacity is limited

Motion vectors Both embedding payload and

computational complexity are moderate

The impact on the video quality

is high

DCT / QDCT / DWT

coefficients

Achieve a high embedding payload as

well as a low computational complexity

The influence on the video quality

is high

CAVLC / CABAC entropy

coding

Achieve a high embedding payload as

well as a low computational complexity

The quality of the steganogram is

severely distorted

Fig. 12 ECC and steganographic method of [114]

Multimed Tools Appl (2017) 76:21749–21786 21771

has the lower mean square error (MSE) is chosen for the hiding stage. Only the Cb part of that

certain video frame is selected for concealing the hidden information. Khupse et al.‘smethodis

restricted in embedding payload due to considering only a single frame for data hiding stage.

Alavianmehr et al. [3] presented a robust uncompressed video steganography by utilizing the

histogram distribution constrained (HDC). In this method, the Ycomponent of every frame is

segmented into non-overlapping blocks (C)ofsizem×n. Then, the secret message is concealed

into these blocks based on the shifting process. The selected blocks are changed only when the

secret message bits are B1^. The modified frame Sof the k

th

block is calculated as follows:

^

Ski;jðÞ¼

Ski;jðÞþγ;if α∈0;T½and mod i;2ðÞ¼mod j;2ðÞ

Ski;j

ðÞ

−γ;if α∈−T;0

½

and mod i;2

ðÞ

≠mod j;2

ðÞ

Ski;jðÞ;otherwise

8

<

:

ð23Þ

γ¼GþT

ðÞ

2

mnð24Þ

αk¼X

m

i¼1X

n

j¼1

Cki;jðÞNi;jðÞ ð25Þ

Ni;jðÞ¼ 1;if mod i;2ðÞ¼mod j;2ðÞ

−1;if mod i;2

ðÞ

≠mod j;2

ðÞ

ð26Þ

Where γ,α,andNare the shift quantity, the arithmetic difference, and the computed matrix for

each block, respectively. Also, Tand Gare two predefined thresholds used in this method.

Alavianmehr et al.‘s method withstands against compression attack. However, it utilizes only Y

plane for data embedding process. Similarly, Moon et al. [66] presented a secure data hiding

method using a computer forensic process. The hidden message is authenticated and ciphered

by a secret key, and then it conceals into the 4 LSB of each pixel of the video frames. In order

to transfer the authentication key to the receiver, it will conceal into one of the frame

recognized by the sender and recipient. The goal of utilizing the computer forensic process

is the validity of the obtained stego videos. The method presented by Moon et al. is not robust

against video processing operations due to utilization of spatial domain. In addition, Kelash

et al. [39] presented a histogram variation-based video steganography method. Frames whose

histogram variation averages exceed the histogram constant value (HCV) are chosen for the

data concealing procedure utilizing the specified threshold. Then, these frames are segmented

into blocks in order to compute the variation of the successive pixels. The hidden message is

concealed into 3 LSB of each selected pixel. According to the hiding capacity, Kelash et al.‘s

method is restricted because it is only based on the HCV value. Comparatively, Paul et al. [80]

presented a new steganographic technique to conceal the hidden message inside the video

stream. Once the abrupt scene fluctuation frames are revealed, these frames are selected to host

the hidden message. The histogram variation is utilized to find each frame whether it is a

sudden scene variation or not. A 3–3-2 LSBs of each pixel are contributed into a data

concealing process in order to hide the covert information. The randomization location of

the pixels improved the security of this method. However, there is a limitation of the number of

21772 Multimed Tools Appl (2017) 76:21749–21786

sudden scene changes frames. On the other hand, Bhole et al. [6] presented a randomization

byte data hiding method. The first video frame is utilized to conceal the control data of other

frames which is called an index frame. The remaining frames are used for data concealing

procedure utilizing the index frame information. Bhole et al., also used the LSB method. Bhole

et al.‘s algorithm lacks of the robustness due to utilization of spatial domain. In a different

work, Hanafy et al. [25] presented a secure communication method based on video steganog-

raphy. This scheme is applied to the spatial domain by using raw videos as cover data and

every text, audio, image, and video as a secret message. In this instance, the message is

segmented into non-overlapping blocks, and then these blocks are randomly concealed into the

frames based on the secret key. The randomization of the secret message is dynamically

changed in each video frame in order to control identifying the message’s location by attackers.

The data embedding process is accomplished by using 2 LSBs of each color channel (RGB),

which hides 6 bits of secret message in each pixel frame. In this scheme, the secret message is

protected using a secret key. However, the scheme utilizes the spatial domain in order to

embed the covert information. Here, this method is not robust against video compression

processes and noises. Similarly, Lou et al. [57] proposed LSB steganography scheme using the

reversible histogram transformation function. Here, the covert information is hidden into the

LSB pixels of the cover data. This algorithm is robust against two well-known statistical

steganalysis schemes including x

2

-detection and regular-singular attacks. The average of the

embedding rate of Lou et al.‘s method is similar to the LSB technique. In addition, Tadiparthi

et al. [99] proposed a steganographic method that utilizes animations as cover data. This

method conceals the secret message into the animation frames. Tadiparthi et al.‘salgorithm

achieves better results when comparing with the two existing algorithms. However, the secret

message distribution cannot be modified because the secret key relies on the probability

distribution of the secret message. In addition, this method requires longer time to implement,

and thus makes it more complex than others. Eltahir et al. [18] proposed a high rate data

concealing algorithm. In each frame, a 3–3-2 approach is used based upon the LSB of three

color channels (RGB). A 3–3-2 method refers to 3-bits of Red, 3-bits of Green, and 2-bits of

Blue in each pixel that are used to hide the covert data as shown in the Fig. 13.Later,Dasgupta

et al. [15]optimizedthe[18] method based on the genetic algorithm in order to enhance both

the security of the covert information and the visual quality of the steganogram. The reason for

this improvement is to develop an objective function that is based on the weights of different

parameters such as MSE and HVS. However, [15,18] algorithms are not robust against signal

processing, noises, and video compression due to the fact that they operate in the pixel domain.

Hu et al. [30] presented a novel data concealing using a non-uniform rectangular partition

method. The non-uniform rectangular partition procedure has three main factors. First, a

suitable initial partition must be chosen to improve the results of the partition. Therefore, a

reconstructed frame can be obtained with a minimum number of partitions and codes. Second,

in order to make an approximation of the pixel gray values in each specific sub-image

(rectangle), the bivariate polynomial has been utilized. Then, by applying the optimal quadratic

approximation to these gray values, the undetermined coefficients of the bivariate polynomial

can be specified. The partition processing will continue to divide the sub-image into four

smaller parts, especially if the original sub-image cannot be extracted by the determined

bivariate polynomial using the required control error. Also, the process of approximation is

repeated again until the number of pixels in the sub-image is greater than or equal to the

undetermined coefficients of the bivariate polynomial. The original image can approximately

be reconstructed according to the codes that have been obtained from the partitioning process.

Multimed Tools Appl (2017) 76:21749–21786 21773

Third, the last factor of the non-uniform rectangular partition process is the control error. The

control error is determined at the end of the partitioning process. It decides whether or not to

continue dividing sub-images. The non-uniform rectangular partition is applied on each frame

of secret video in order to obtain partitioned codes that will be concealed into the cover frames.

This steganographic algorithm is based on a concept called BTangram^that is similar to a

puzzle game. The algorithm has two main advantages: 1) The adaptability of non-uniform

rectangular partition and 2) the cover frame carries and records the partition codes information

[30]. If the secret frame is Aand the carrier frame is B, then the process of embedding in this

method can be accomplished by the following points:

1- A suitable initial partition area is selected. Also, a control error E= 4 (ranged from 2 to 6)

and a bivariate polynomial equation f(x,y)=ax +by +cxy +dare specified. By applying

the non-uniform rectangular partition algorithm, the frame Apartition grids are obtained;

and

2- Partition grids of frame Aare placed on frame B, and then h1¼z1−ẑ1,h2¼z2−ẑ2,

h3¼z3−ẑ3,andh4¼z4−ẑ4are calculated. Where z

1

,z

2

,z

3

,z

4

and ẑ1;ẑ2;ẑ3;ẑ4are the

gray values of each rectangular sub-area vertexes for Aand Bframes, respectively; and

3- Embedding all partition codes and their differences {h}intoeach4LSBframeBgray values.

Hu et al.‘s algorithm increases the capacity of the hidden data. However, it is not robust

against the video compression and temporal noises due to due to utilization of spatial domain.

Moreover, the computational time is high due to algorithm’s complexity. In a different work,

Kawaguchi et al. [37] proposed principles and applications of BPCS steganography. In this

method, the video frame is first converted into 8 bit-planes, and then each bit-plane is divided

into informative (simple) and noise-like (complex) regions. The BPCS technique differs from

the LSB technique in the number of bit-planes that are utilized for embedding secret message.

The BPCS technique uses all bit-planes (0–7) for data hiding while the LSB technique only

uses a bit-plane 0 for the embedding process. Figure 14 clarifies how one of the video frames

converts to 8 bit-planes by applying the BPCS technique. In this method, the covert informa-

tion is concealed into the complex regions to achieve a high embedding payload. Moreover,

Fig. 13 The hiding capacity in each RGB pixel [18]

21774 Multimed Tools Appl (2017) 76:21749–21786

modifying the noise-like areas in each bit-plane for data hiding purposes has a minimal

influence to the human visual system. The complexity (α) level is measured in each region

whether informative or complex, and αcan be defined as follows:

α¼k

2mm−1ðÞ

;0≤α≤1ðÞ ð27Þ

Where kequals the total length of the black-and-white border in the selected region,

and 2m(m−1) is the highest possibility of the border length gained from the

selected region. An m×mrepresents the size of the selected region. Figure 15

illustrates the complexity degree of the BPCS regions according to the Kawaguchi

et al. method.

Sun [98] proposed a new information hiding method based on the improved BPCS steganog-

raphy. The regular BPCS method computes the complexity of the selected region based on the

total length of the black-and-white border. This technique introduces a new method that identifies

the noise-like regions which is useful, especially, in periodical patterns. Each canonical gray

coding (CGC), run-length irregularity, and border noisiness are utilized to measure the complexity

level of the selected regions. Based on the complexity degree, the secret data is concealed into the

noise-like areas. In order to expand the capacity of the covert information, the informative regions

are converted into the complex regions using the conjugation operation. If nis the length of pixels

and h[i] is the repetition of run-lengths in each black-or-white of ipixels, then run-length

irregularity of the binary pixels (H

s

)inSun’s algorithm can be calculated as follows:

Hs¼−X

n

i¼1

hi½log2Pið28Þ

Pi¼hi½

Xn

j¼1hj½

ð29Þ

Fig. 14 The process of converting one of the Foreman video frames into 8 bit-planes using the BPCS technique

Multimed Tools Appl (2017) 76:21749–21786 21775

In conclusion, the steganographic methods that operate in spatial domain are simple and

obtain a high payload of secret messages. However, these techniques are not robust against

signal processing, noises, and compression. Moreover, most of the above-mentioned methods

do not take advantage of the cover data and the secret message preprocessing stages which can

enhance the robustness and security of the steganographic algorithms.

3.2.2 Video steganography techniques in transform domain

In video steganography methods that operate in transform domain, each video frame

is individually transformed into frequency domain using DCT, DWT, and discrete

Fourier transform (DFT) and the secret message is embedded utilizing the low,

middle, or high frequencies of the transformed coefficients. Patel et al. [79]pre-

sented a new data hiding method using the lazy wavelet transform (LWT) technique,

where each video frame is divided into four sub-bands, separating the odd and even

coefficients. The secret information is then embedded into the RGB LWT coeffi-

cients. For accurate extraction of embedded data, the length of hidden data is

concealed into the audio coefficients. The amount of hidden information is high,

but this type of wavelet is not a real mathematical wavelet operation. Consequently,

Patel et al.‘s method will not protect the hidden information from attackers due to it

operates in the spatial domain. On the other hand, Spaulding et al. [95]presented

the BPCS steganography method using an embedded zerotree wavelet (EZW) lossy

compression. In this method, the DWT’s coefficients are representing the original

frame’s pixels. Therefore, the BPCS steganography can be applied to DWT coeffi-

cient sub-bands which contain different features. The features of DWT sub-bands

include correspondence, complexity, and resiliency against attacks. Each DWT sub-

band is divided into pit-planes, and then the quantized coefficients are used for

hiding the covert data. This method achieves a high embedding capacity around a

quarter of the size of the compressed frame. Fig. 16 illustrates the data embedding

process of Spaulding et al.‘s method.

Fig. 15 BPCS complexity degree of different regions: left informative region and right noise-like region

21776 Multimed Tools Appl (2017) 76:21749–21786

Similarly, Noda et al. [77] presented a video steganography technique utilizing the

BPCS and wavelet compressed video. The 3D set partitioning in hierarchical trees

(SPIHT) and motion-JPEG2000 are the two coding techniques that use the DWT

domain. First, each bit-plane of the video frame and the secret message is segmented

into 8*8 blocks. Then, the noise-like, bit-plane blocks are selected using the threshold

of the noise-like complexity measurement. The two wavelet compression techniques

are applied on the selected blocks by using the BPCS method, hiding the secret data

into the quantized DWT coefficients. The experimental results of Noda et al.‘s

algorithm demonstrated that the 3D SPIHT coding method has a higher embedding

payload than the Motion-JPEG2000 coding method when using BPCS steganography.

However, the suggested algorithm of Noda et al. is not guaranteed that all types of

cover videos contain enough noise-like bit-plane regions. Moreover, this method is

only applied to the wavelet-based compression domain. Ordinarily, the steganographic

techniques based on the transform domains improve the robustness against signal

processing, noises, and compression. However, these techniques are more complex

than the spatial domain methods.

Table 6summarizes video steganography methods that utilize raw domain for data hiding,

highlighting each of embedding capacity, video quality, robustness against attacks, video

preprocessing, and secret messages preprocessing.

4 Performance assessment metrics

The main purpose of steganography techniques is to conceal the secret information

inside the cover video data, thus the quality of the cover data will be changed ranging

from a slight modification to a severe distortion. In order to evaluate whether the

distortion level is acceptable or not, statistically, different metrics have been utilized

Fig. 16 A block diagram representing the data concealing phase of the method [95]

Multimed Tools Appl (2017) 76:21749–21786 21777

Tab le 6 Venues, embedding capacity, video quality, robustness, video and message preprocessing of the discussed video steganography methods that operate in raw domain

Technique Domain / venue for

data hiding

Embedding capacity Video quality Robustness Video preprocessing Message preprocessing

Zhang et al. [114] Raw / Spatial domain At most the embedding

capacity is m × t bits per

n=2

m

−1 bits block,

where m > 2 and t =

2or 3

N/A Not robust against signal

processing operations

Not used BCH

Cheddad et al. [10] Raw / Spatial domain Average of embedding

capacity ratio is 0.08 %

Average PSNR is

61.22 dB

Not robust enough against

signal processing and

compression

Skin region detection Not used

Cheddad et al. [12] Raw / Spatial domain Average of embedding

capacity ratio is 1.03 %

Average PSNR is

59.63 dB

Not robust enough against

signal processing and

compression

Skin region detection Not used

Sadek et al. [91] Raw / Spatial domain Average of embedding

capacity ratio is 0.23 %

Average PSNR is

54.64 dB

Robust against MPEG-4

codec

Skin region detection Not used

Khupse et al. [43] Raw / Spatial domain Low embedding capacity

only frame is used (2120

bits per video)

Almost the same as

original video

Not robust against signal

processing, noises, and

compression

Skin region detection Not used

Alavianmehr et al. [3] Raw / Spatial domain Average of embedding

capacity ratio is 1.34 %

(4096 bits per video)

Average PSNR is

36.97 dB

Robust against H.264/AVC

codec

Not used Not used

Moon et al. [66] Raw / Spatial domain Average of embedding

capacity ratio is 12.5 %

N/A Not robust against signal

processing, noises, and

compression

Not used Encryption

Kelash et al. [39] Raw / Spatial domain Average of embedding

capacity ratio is 1.1 %

Average PSNR is

48.84 dB

Not robust against signal

processing, noises, and

compression

Not used Not used

Paul et al. [80] Raw / Spatial domain Average of embedding

capacity 8 bpp only in

sudden scene change

frames

Almost the same as

original video

(frames that are

sudden scenes)

Not robust against signal

processing and noises

Scene change detector Not used

Bhole et al. [6] Raw / Spatial domain Average of embedding

capacity ratio is 0.2 %

N/A Not robust against signal

processing, noises, and

compression

Not used Not used

21778 Multimed Tools Appl (2017) 76:21749–21786

Tab le 6 (continued)

Technique Domain / venue for

data hiding

Embedding capacity Video quality Robustness Video preprocessing Message preprocessing

Hanafy et al. [25] Raw / Spatial domain Average of embedding

capacity 0.65 bpp

Average PSNR is

51.35 dB

Not robust against noises

and compression

Randomization Randomization

Lou et al. [57] Raw / Spatial domain Average of embedding

capacity ratio is 12 %

Average PSNR is

50.51 dB

Robust against x

2

-detection

and regular-singular at-

tacks

Not used Not used

Tadiparthi et al. [99] Raw / Spatial domain Average of embedding

capacity ratio is 2 %

N/A Not robust against signal

processing, noises, and

compression

Not used Encryption

Eltahir et al. [18] Raw / Spatial domain Average of embedding

capacity 8 bpp

N/A Not robust against signal

processing, noises, and

compression

Not used Not used

Dasgupta et al. [15] Raw / Spatial domain Average of embedding

capacity 8 bpp

Average PSNR is

38.45 dB

Not robust against signal

processing, noises, and

compression

Not used Not used

Hu et al. [30] Raw / Spatial domain Average of embedding

capacity 1.5 bpp

Average PSNR is

29.03 dB

Not robust against signal

processing, noises, and

compression

Not used Non-uniform Rectangular

Partition

Kawaguchi et al. [37] Raw / Spatial domain At most the embedding

capacity ratio is 41 %

when the threshold is 25

N/A Not robust against signal

processing and noises

BPCS Not used

Sun [98] Raw / Spatial domain At most the embedding

capacity ratio is 45 %

Average PSNR is

44.28 dB

Not robust against signal

processing, noises, and

compression

BPCS Not used

Patel et al. [79] Raw / Transform do-

main

Average of embedding

capacity ratio is 12.5 %

Average PSNR is

31.23 dB

Not robust against signal

processing, noises, and

compression

Not used Rijndael 256 encryption

Spaulding et al. [95] Raw / Transform do-

main

Average of embedding

capacity ratio is 25 %

Average PSNR is

33 dB

Robust lossy compression BPCS Not used

Noda et al. [77] Raw / Transform do-

main

Average of embedding

capacity ratios are 18 %

for 1 bit-plane and 28 %

for 2 bit-planes

Average PSNR of 2

bit-planes are

42.55 dB

Robust against 3D–SPIHT

and Motion-JPEG2000

compression

BPCS Not used

Multimed Tools Appl (2017) 76:21749–21786 21779

[2]. PSNR is a common metric utilized to calculate the difference between the carrier

and stego data. The PSNR can be calculated as follows [92]:

MSE ¼Xm

i¼1Xn

j¼1Xh

k¼1Ci;j;kðÞ−Si;j;kðÞ½

2

mnhð30Þ

PSNR ¼10*Log10

MAX C2

MSE

dBðÞ ð31Þ

Cand Srepresent the carrier and stego frames. Both mand nindicate the frame resolutions, and h

represents the RGB colors (k = 1, 2, and 3). PSNR-HVS (PSNRH)andPSNR-HVS-M (PSNRM)

objective measurements are utilized to enhance the quality of the steganograms. The PSNRM is an

upgraded form of the PSNRH.EachofPSNRH and PSNRM relies on the DCT coefficients of the

transform domain [17]. PSNRH and PSNRM can be calculated using Eq. 32 and Eq. 33 [82]:

PSNRH ¼10*Log10

MAX C2

MSEhvs

dBðÞ ð32Þ

PSNRM¼10*Log10

MAXC2

MSEhvs−m

dBÞðð33Þ

MSE

hvs

and MSE

hvs_m

utilize the factor matrix and the 8 × 8 DCT coefficients of the carrier and

stego frame blocks [17]. On the other hand, the performance of steganographic method in terms of

embedding capacity is a major factor that any method tried to increase it with the respect of the

visual quality. According to [103], any steganographic method has a high hiding capacity if the

hidden ratio exceeds 0.5 %. The embedding ratio is calculated in the following formula:

Embedding ratio ¼Size of embedded message

Cover video size 100%ð34Þ

To further evaluate the performance of any steganographic algorithm in terms of robustness,

two objective metrics including bit error rate (BER) and similarity are used. These metrics are

applied to determine whether the secret messages are retrieved from the stego videos success-

fully by comparing the concealed and extracted covert data. The BER and similarity are

computed in the following formulas [27]:

BER ¼Xa

i¼1Xb

j¼1Mi;jðÞ⊕^

Mi;jðÞ

ab100% ð35Þ

Similarity ¼Xa

i¼1Xb

j¼1Mi;jðÞ

^

Mi;jðÞ

ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

Xa

i¼1Xb

j¼1Mi;jðÞ

2ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

Xa

i¼1Xb

j¼1^

Mi;jðÞ

2

r

sð36Þ

Where M and ^

M are the concealed and extracted hidden data, and, Ba^and Bb^are the size of

the hidden data.

21780 Multimed Tools Appl (2017) 76:21749–21786

5 Conclusion and recommendations

In this paper, we have presented a comprehensive review and analysis of video steganography

methods in both compressed and raw domains. In addition, the main confusion between steganog-

raphy, cryptography, and watermarking techniques was eradicated. First, compressed video stega-

nography techniques were classified based on the video encoding stages as venues for data

embedding. Venues for concealing secret messages in compressed domain include: 1) intra frame

prediction, 2) inter frame prediction, 3) motion vectors, 4) DCT and QDCT coefficients, and 5)

CAVLC and CABAC entropy coding. Second, the existing raw video steganography methods were

categorized according to their domain of operation including 1) spatial domain methods and 2)

transform domain methods. Then, techniques of each domain were discussed and their performance

assessments, imperceptibility, embedding capacity, robustness against attacks, video preprocessing,

and secret messages preprocessing were highlighted. Furthermore, the characteristics and draw-

backs of each steganographic method were mentioned. The following recommendations and future

research trends are suggested to come up with an appropriate method for data hiding:

1- Proposing a video steganography method that maintains a trade-off between video quality,

hiding capacity, and robustness against attacks, this makes it more appropriate for real-

time security methods.

2- Suggesting a steganographic technique that combines steganography with other system

protection methods such as cryptography and error correcting codes. Thus, encrypting and

encoding the hidden massage prior to the embedding process will provide an additional

security level to the secret message and make it more robust against attackers during the

transmission.

3- Providing a video steganography algorithm that focuses on a portion of the video as

carrier for data hiding instead of using entire video. Such a method will lead to enhance

the quality of steganograms and the resistance against attacks. For instance, concealing the

secret message into the region of interest includes human faces, human bodies, cars, or

any other motion objects. Furthermore, it will be challenging for unauthorized users and

intruders to define the position of hidden data in each video frames as the hidden data is

concealed into the ROI which modifies from frame to frame, hence maintaining the

security of hidden message.

4- Introducing a video steganography method that utilizes transformation coefficients of the

ROI rather than using actual pixel domain. Since transform domain techniques are more

robust against signal processing operations and compression process.

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Multimed Tools Appl (2017) 76:21749–21786 21785

Ramadhan J. Mstafa is originally from Duhok, Kurdistan Region, Iraq. He is pursuing his PhD degree in

Computer Science and Engineering at the University of Bridgeport, Bridgeport, Connecticut, USA. He received

his Bachelor’s degree in Computer Science from the University of Salahaddin, Erbil, Iraq. Mr. Mstafa received

his Master’s degree in Computer Science from University of Duhok, Duhok, Iraq. He is an IEEE and ACM

Student Member. His research areas of interest include image processing, mobile communication, security,

watermarking, and steganography.

Dr. Khaled M. Elleithy is the Associate Vice President for Graduate Studies and Research at the University of

Bridgeport. He is a professor of Computer Science and Engineering. He has research interests in the areas of

wireless sensor networks, mobile communications, network security, quantum computing, and formal approaches

for design and verification. He has published more than three hundred research papers in international journals

and conferences in his areas of expertise. Dr. Elleithy has more than 25 years of teaching experience. His teaching

evaluations are distinguished in all the universities he joined. He supervised hundreds of senior projects, MS

theses and Ph.D. dissertations. He supervised several Ph.D. students. He developed and introduced many new

undergraduate/graduate courses. He also developed new teaching / research laboratories in his area of expertise.

Dr. Elleithy is the editor or co-editor for 12 books by Springer. He is a member of technical program committees

of many international conferences as recognition of his research qualifications. He served as a guest editor for

several international journals. He was the chairman for the International Conference on Industrial Electronics,

Technology & Automation, IETA 2001, 19-21 December 2001, Cairo –Egypt. Also, he was the General Chair of

the 2005-2014 International Joint Conferences on Computer, Information, and Systems Sciences, and Engineer-

ing virtual conferences.

21786 Multimed Tools Appl (2017) 76:21749–21786

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