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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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