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A DCT-based robust video steganographic method using BCH error correcting codes

Conference Paper

A DCT-based robust video steganographic method using BCH error correcting codes

Abstract and Figures

Due to the significant growth of video data over the Internet, it has become a popular choice for data hiding field. The performance of any steganographic algorithm relies on the embedding efficiency, embedding payload, and robustness against attackers. Low hidden ratio, less security, and low quality of stego videos are the major issues of many existing steganographic methods. In this paper, we propose a DCT-based robust video steganographic method using BCH codes. To improve the security of the proposed algorithm, a secret message is first encrypted and encoded by using BCH codes. Then, it is embedded into the discrete cosine transform (DCT) coefficients of video frames. The hidden message is embedded into DCT coefficients of each Y, U, and V planes excluding DC coefficients. The proposed algorithm is tested under two types of videos that contain slow and fast moving objects. The results of the proposed algorithm are compared with three existing methods. The results demonstrate better performance for the proposed algorithm than for the others. The hidden ratio of the proposed algorithm is approximately 27.53%, which is evaluated as a high hiding capacity with a minimal tradeoff of the visual quality. The robustness of the proposed algorithm was tested under different attacks.
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978-1-4577-1343-9/12/$26.00 ©2016 IEEE
A DCT-based Robust Video Steganographic Method
Using BCH Error Correcting Codes
Ramadhan J. Mstafa, IEEE Student Member
Department of Computer Science and Engineering
University of Bridgeport
Bridgeport, CT 06604, USA
rmstafa@my.bridgeport
.
edu
Khaled M. Elleithy, IEEE Senior Member
Department of Computer Science and Engineering
University of Bridgeport
Bridgeport, CT 06604, USA
elleithy@bridgeport.edu
Abstract— Due to the significant growth of video data over
the Internet, it has become a popular choice for data hiding field.
The performance of any steganographic algorithm relies on the
embedding efficiency, embedding payload, and robustness
against attackers. Low hidden ratio, less security, and low quality
of stego videos are the major issues of many existing
steganographic methods. In this paper, we propose a DCT-based
robust video steganographic method using BCH codes. To
improve the security of the proposed algorithm, a secret message
is first encrypted and encoded by using BCH codes. Then, it is
embedded into the discrete cosine transform (DCT) coefficients
of video frames. The hidden message is embedded into DCT
coefficients of each Y, U, and V planes excluding DC coefficients.
The proposed algorithm is tested under two types of videos that
contain slow and fast moving objects. The results of the proposed
algorithm are compared with three existing methods. The results
demonstrate better performance for the proposed algorithm than
for the others. The hidden ratio of the proposed algorithm is
approximately 27.53%, which is evaluated as a high hiding
capacity with a minimal tradeoff of the visual quality. The
robustness of the proposed algorithm was tested under different
attacks.
Keywords
Video Steganography; BCH Codes; DCT;
Embedding Efficiency; Embedding Payload; Robustness
I.
I
NTRODUCTION
Steganography is a process that involves hiding important
information (message) inside other carrier (cover) data to
protect the message from unauthorized users. The mixed data
(stego objects) will be seen by the Human Visual System
(HVS) as one piece of data because the HVS will not be able to
recognize the small change that occurs in the cover data.
Message and cover data could be any type of data format such
as text, audio, image, and video [1]. The development of
steganalysis tools weakens unsecure steganography schemes
and rendering them useless. Hence, researchers have to
develop secure steganography algorithms that are protected
from both attackers and steganalysis detectors. Any successful
steganography system should consider two main important
factors: embedding payload and embedding efficiency [2].
First, the embedding payload is defined as the amount of
secret information that is going to be embedded inside the
cover data. The algorithm has a high embedding payload if it
has a large capacity for the secret message. The embedding
efficiency includes the stego visual quality, security, and
robustness against attackers. Second, both a low modification
rate and good quality of the cover data lead to a high
embedding efficiency [3]. The steganography algorithm that
contains a high embedding efficiency will reduce attacker
suspicion of finding hidden data and will be quite difficult to
detect through steganalysis tools. However, any distortion to
the cover data after the embedding process occurs will increase
the attention of attackers [4]. The embedding efficiency is
directly affected by the security of the steganographic scheme
[5]. In traditional steganographic schemes, embedding payload
and embedding efficiency are opposite. Increasing the capacity
of the secret message will decrease the quality of stego videos
that then weakens the embedding efficiency [6]. Both factors
should be considered. The deciding factors depend on the
steganography algorithm and the user requirements [3]. To
improve steganographic schemes, many of the algorithms use
matrix encoding and block code principles such as Hamming,
BCH, and Reed-Solomon codes [7]. The contributions of this
paper will provide “a state of the art” embedding algorithm in
the frequency domain that uses error correcting codes. In
addition, we proposed a DCT-based robust video
steganographic method using BCH codes. This method
produces a reasonable tradeoff between quality, hiding
capacity, and robustness.
The remainder of this paper is organized as follows:
Section 2 presents some of the related works. Section 3
discusses discrete cosine transform. Section 4 explains some
principles of BCH codes. Section 5 presents the embedding
and extracting phases of the proposed steganography
methodology. Section 6 illustrates and explains the
experimental results. Section 7 contains the conclusions.
II. R
ELATED
W
ORKS
In 2012, Zhang et al. proposed an efficient embedder using
BCH code for steganography. The embedder conceals the
secret message into a block of cover data. The embedding
process is completed by changing various coefficients in the
input block in order to make the syndrome values null. The
efficient embedder improves both storage capacity and
computational time compared with other algorithms.
According to the system complexity, Zhang’s algorithm
improves the system complexity from exponential to linear
[8]. In 2013, Liu et al. proposed a robust steganography
scheme using H.264/AVC compressed video stream without a
distortion drift in the intra-frame. The prevention of the intra-
frame distortion drift can be achieved using the directions of
the intra-frame prediction. Some blocks will be selected as
cover data for embedding the secret information. This process
will depend on the prediction of the intra-frame modes of
neighboring blocks to avert the distortion that propagates from
the adjacent blocks. To improve system efficiency and
robustness, Liu et al. applied BCH code to the message prior to
the embedding process. Then, the encoded message is
embedded into the 4x4 DCT block of quantized coefficients
with only a luminance component of the intra-frame [9]. In
2014, Diop et al. proposed an adaptive steganography
algorithm using a linear error correcting code, referred to as the
low-density parity-check (LDPC) code. The algorithm explains
how to minimize the effect of secret message insertion using
the LDPC code. For that purpose, Diop et al. demonstrated that
the LDPC code is a better encoding algorithm than all other
codes [10].
Previously mentioned algorithms lack the robustness to
withstand hacker attacks. With the embedding payload,
flexibility exists to increase the capacity of a secret message. In
this paper, we propose a DCT-based robust video
steganographic method using BCH error correcting codes.
III. D
ISCRETE
C
OSINE
T
RANSFORM
(DCT)
DCT is a well-known method which is utilized in many
applications such as image and video compression. The DCT
separates the signal into low, middle, and high frequency
regions. The DCT is closely related to the discrete Fourier
transform (DFT). It is a separable linear transformation; that is,
the 2D-DCT is equivalent to a 1D-DCT performed along a
single dimension followed by a 1D-DCT in the other
dimension [11]. For an input video frame, A, of resolution M x
N the DCT frequency coefficients for the transformed frame, B,
and the inverse DCT coefficients of the reconstructed frame
are calculated according to the following equations,
respectively:

=∝






(2+1)
2 (2+1)
2 (1)

=∝





(2+1)
2 (2+1)
2 (2)
Where
=
,=0
,1≤≤−1
And
∝
=
1
,=0
2
,1≤≤−1
A (m, n) is the pixel value in row m and column y of the
frame A, and B (p, q) is the coefficient in row p and column q
of the 2D-DCT matrix. Each of low, middle, and high
frequency coefficients were used as cover data to embed the
encoded secret message [12].
IV. BCH
C
ODES
Bose, Chaudhuri, and Hocquenghem invented the BCH
encoder. It is one of the most powerful random cyclic code
methods, which can be used for detecting and correcting errors
in a block of data. The BCH code is different from the
Hamming code because BCH can correct more than one bit.
The BCH codes inventors decided that the generator
polynomial g(x) will be the polynomial of the lowest degree in
the Galois field GF (2), with ∝,
,∝
,…,∝

as roots on the
condition that is a primitive of GF(2
). When M
(x) is a
minimal polynomial of∝
where(1≤≤2), then the least
common multiple (LCM) of 2t minimal polynomials will be
the generator polynomial g(x). The g(x) function and the
parity-check matrix H of the BCH codes [13, 14] are described
as follows:
=
1∝∝
∝
∝


1(
)(
)
(
)
…(
)

1(
)(
)
(
)
…(
)

.....
.....
.....
1(

)(

)
(

)
…(

)

(3)
()={
(),
(),
(),…,

()}
(4)
()=
()
()
()…

()
(5)
A binary BCH (n, k, t) can correct errors of a maximum t
bits for a codeword ={
,
,
,…,

} of length n and
a message ={
,
,
,…,

} of length k [15]. An
embedded codeword ={
,
,
,…,

} is calculated as
follows:
=
(6)
At the receiver side, the codeword ={
,
,
,…,

}
is obtained. The transmitted and received codewords can both
be interpreted as polynomials, where ()=
+
+
⋯+


, and()=
+
+⋯+


. The
error E is the difference between C and R, which indicates the
number and location of flipped elements in C. The E and
syndrome Y are calculated as follows:
=(7)
=(−)
=
(8)
In this paper, the BCH codes (7, 4, 1) is used over the
GF(2
), where m=3, k=4, and =2
−1=7.
V. T
HE
P
ROPOSED
S
TEGANOGRAPHY
M
ETHODOLOGY
In this section, we proposed a DCT-based robust video
steganographic method using BCH codes. At the beginning,
the video sequence is separated into frames; each frame is
converted to YCbCr color space. The reason for converting to
YCbCr color space is to remove the correlation between the
red, green, and blue colors. The proposal methodology consists
of data embedding stage and data extracting stage.
Data Embedding Stage
For a security purpose, the hidden message is encrypted
using a secret key, and then BCH (7, 4, 1) codes will be
applied on it producing an encoded message. The whole
encoded message is converted from binary to base-8 digits. On
the other hand, each video sequences is converted into a
number of frames. Each frame separates into the YUV color
space. Then, 2D-DCT is applied individually on each plane.
Subsequently, the process of embedding is achieved by
concealing each base-8 digit of the encoded message into the
DCT frequency coefficients except the DC coefficients of each
of the Y, U, and V planes. Thereafter, the inverse of 2D-DCT
is applied on the three stego components of each frame
producing a stego frame. Finally, the stego video is constructed
from these stego frames. The secret message is concealed into
each of YUV DCT coefficients as follows:

=(

,
);

0
((

),
);

0(9)

=(

,
);

0
((

),
);

0(10)

=(

,
);

0
((

),
);

0(11)
Where Y

,U

,andV

are DCT coefficients of Y, U, and V
respectively, and
is the encoded digits,
=
{000,,111}. The block diagram of the data embedding stage
is illustrated in Fig. 1.
Data Extracting Stage
Data extracting is the process of retrieving the encoded
message from the stego videos. This process is achieved by
isolating the stego videos into frames. Each frame is divided
into Y, U, and V planes. Then, 2D-DCT is applied separately
on each plane. The process of extracting the encoded message
is accomplished by taking
digits from each of Y, U, and V
DCT coefficients, respectively, except DC coefficients. The
outcomes data are decoded by the BCH (7, 4, 1) decoder
followed by the deciphering process to extract the valid
embedded message. The purpose of using ciphering and
encoding methods prior the embedding process is to improve
the security and robustness of the proposed algorithm.
Moreover, the secret key is only shared between sender and
receiver, and used in both the data embedding and extracting
processes. The hidden message can be obtained as follows:
=

;(

0)


;(

0)(12)
=

;(

0)


;(

0)(13)

=

;(

0)


;(

0)(14)
Where Y

,U

,andV

are DCT coefficients of stego YUV
planes, and
is the retrieved secret message. The block
diagram of the data extracting process of the proposed
steganography algorithm is shown in Fig. 2.
Fig. 1 Block Diagram of the Data Embedding Process
VI. E
XPERIMENTAL
R
ESULTS AND
D
ISCUSSION
The experimental environment uses several variables: the
cover data comprise a dataset consisting of six video sequences
Foreman
,
Akiyo
,
Coastguard
,
Container, Bus
,
and Soccer
of
CIF type; also, the format of YUV is 4:2:0. In addition, the
resolution of each video is(352288), and all videos are
equal in length with 150 frames. A large text file is used as a
secret message. The work is implemented using MATLAB to
test the proposed algorithm efficiency.
Visual Quality
Pick signal to noise ratio (PSNR) is an objective quality
measurement used to calculate the difference between the
original and the stego video frames. It can be obtained by
following equations [16]:
=10


 (15)
=∑∑(,)


−(,)
∗ (16)
Fig. 2 Block Diagram of the Data Extracting Process
Where O and S denote the original and stego YUV frame
components, respectively, and m and n are the video
resolutions. In Fig. 3, the PSNR of the Y-components are
calculated for all six videos. Overall, the
Akiyo
video has the
best luminance quality. Fig. 4 shows the PSNR of the U-
component for all six videos. The PSNR-U of the
Coastguard
video has the highest dBs among the group. Fig. 5 shows the
PSNR of the V-component for all experiments. The PSNR-V
for the
Coastguard
video has a better quality among all videos.
In Fig. 6, the PSNR comparison for 150 frames of each video
is shown. The comparison shows that the result of the objective
quality for each of the
Foreman
,
Akiyo
,
Coastguard
,
Container, Bus
,
and Soccer
videos ranged between 38.95-
42.73 dBs. Overall, the results of the PSNR for the
Foreman
,
Akiyo
,
Container
, and
Bus
videos are more stable, while in the
Coastguard
and
Soccer
videos the quality is frequently
changing. The changes occur because these videos contain
faster motion objects that lead to unstable visual quality. Table
I shows the averages of the PSNR for each Y, U, and V
component for all video sequences. Moreover, the visual
quality of each video is measured separately by averaging each
of the 150 frames per video. The averages are various and
depend on both the type of videos and the speed of the motion
object.
Fig. 3 PSNR Comparisons for the Y-Components of All six Videos
Fig. 4 PSNR Comparisons for the U-Component of All six Videos
Fig. 5 PSNR Comparisons for the V-Component of All six Videos
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115
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127
133
139
145
PSNR (dB)
Frame Number
Foreman Akiyo Coastguard Container Bus Soccer
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50
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145
PSNR (dB)
Frame Number
Foreman Akiyo Coastguard Container Bus Soccer
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50
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115
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139
145
PSNR (dB)
Frame Number
Foreman Akiyo Coastguard Container Bus Soccer
Fig. 6 PSNR Comparisons for 150 Frames of All six Videos
TABLE I. T
HE
A
VERAGE
PSNR
FOR
E
ACH
Y,
U,
AND
V
C
OMPONENT
FOR
A
LL
S
IX
V
IDEOS
Sequences PSNRY PSNRU PSNRV PSNR
Foreman
39.82 40.63 41.21 40.55
Akiyo
43.33 35.14 42.16 40.21
Coastguard
39.45 43.69 44.93 42.69
Container
37.91 40.71 39.28 39.30
Bus
35.96 39.78 41.10 38.95
Soccer
42.85 41.40 43.94 42.73
Average 39.88 40.22 42.10 40.73
Embedding Payload
The average of the obtained hidden ratio of the proposed
algorithm is 27.53%. A reasonable tradeoff is noticed between
the amount of the embedded message in each video (5.99
Mbytes) and the average quality of six experiments (40.73 dB).
The hidden ratio (HR) can be calculated as in equation 17.
A number of experiments were conducted to compare the
performance of the proposed with three existing methods.
Table II illustrates the comparison of our proposed method
with the three existing methods in the literature, according to
the PSNR and the amount of secret data. Consequently, our
proposed algorithm outperformed three existing methods.
Table III shows the amount of secret message of proposed
algorithm in each Y, U, and V planes.
TABLE II. P
ERFORMANCE
C
OMPARISON OF THE
P
ROPOSED
A
LGORITHM
WITH
O
THER
A
CCORDING TO BOTH
PSNR
AND
H
IDDEN
R
ATIO
Criteria Hu et
al. [17]
Patel et
al. [18]
Alavianmehr
et al. [19]
Proposed
Algorithm
Hidden
Ratio 18.75% 12.5% 1.34% 27.53%
PSNR (dB) 29.03 31.23 36.97 40.73
TABLE III. E
MBEDDING
C
APACITY OF THE
P
ROPOSED
A
LGORITHM
Video Resolution YUV Proposed Algorithm
(Bits/Frame)
176 x 144
Y 55836
U 13959
V 13959
352 x 288
Y 223344
U 55836
V 55836
=




 100%

(17)
Robustness
To measure the robustness of the proposed algorithm, the
Similarity (Sim) metric has been utilized. This metric is used to
test whether the extracted secret message has been corrupted
during communication [20]. The Sim (0≤≤1)can be
calculated as in the following equation [21]:
= ∑∑(,)


(,)
∑∑(,)


∑∑
(,)


(18)
where and
are the embedded and extracted secret
messages, respectively, and a and b are the dimensions of the
secret message array. The algorithm is tested under different
types of attacks (Gaussian noise with the zero mean and
variance=0.01 and 0.001, Salt & pepper noise with the
density=0.01 and 0.001, and median filtering). To achieve the
robustness of the algorithm, the higher Sim must be obtained.
Table IV illustrates the robustness of the proposed algorithm
under attacks while it retrieves the hidden data with a high Sim.
TABLE IV. S
IM
V
ALUES OF THE
P
ROPOSED
A
LGORITHM
U
NDER
A
TTACKS
No
attacks
(Salt & Pepper)
density=
(Gaussian
white)
variance= Median
filtering
Sequences 0.01 0.001 0.01 0.001
Foreman
1 0.960 0.953 0.914 0.892 0.970
Akiyo
1 0.950 0.957 0.918 0.903 0.981
Coastguard
1 0.940 0.947 0.908 0.893 0.981
Container
1 0.970 0.977 0.938 0.923 0.993
Bus
1 0.960 0.967 0.928 0.913 0.982
Soccer
1 0.918 0.926 0.897 0.868 0.954
VII. C
ONCLUSION
In this paper, a DCT-based robust video steganographic
method using BCH error correcting codes has been proposed.
The steganography algorithm converts the video into frames;
then, it divides each frame into Y, U, and V components. Prior
to the embedding process, the secret message is encrypted and
encoded using BCH codes. The 2D-DCT has been applied to
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38
40
42
44
46
48
1
7
13
19
25
31
37
43
49
55
61
67
73
79
85
91
97
103
109
115
121
127
133
139
145
PSNR (dB)
Frame Number
Foreman Akiyo Coastguard Container Bus Soccer
each YUV components. DCT coefficients, excluding DC
coefficients, are selected for embedding the secret data.
The proposed algorithm has a high embedding payload.
The amount of the secret data in each video is approximately
5.99 Mbytes and the HR is 27.53%. The visual quality of the
stego videos is also high: the PSNR ranged between 38.95-
42.73 dBs with an Sim=1. Moreover, the experimental results
showed that the proposed algorithm is robust against several
attacks. In addition, the security of the our method is improved
by ciphering and encoding processes prior to the embedding
process. The result of comparison shows that the proposed
algorithm outperformed three existing algorithms. For future
work, we would like to improve the embedding payload of the
proposed algorithm with the respect of the video quality by
using other techniques that operate in frequency domain. Also,
we would like to conduct efficient linear block codes to
enhance the security of the algorithm.
R
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Ramadhan J. Mstafa
Ramadhan 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 IEEE Student Member. His research areas of
interest include image processing, mobile communication, security, and
steganography.
Khaled M. Elleithy
Dr. Elleithy is the Associate Vice President of
Graduate Studies and Research at the University of
Bridgeport. He is a professor of Computer Science
and Engineering. He has research interests are 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 is the General Chair of the 2005-2013 International
Joint Conferences on Computer, Information, and Systems Sciences, and
Engineering virtual conferences.
... R J Mstafa et al. [30] proposed an approach in which secret text message is encrypted using BCH codes [31] and this encrypted message is concealed into DCT coefficients of every Y, U & V planes of video frames. The author also use two different types of videos consists of fast & slow-moving object for hiding the message. ...
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Conference Paper
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people are becoming more worried about information being hacked by attackers. Recently, many algorithms of steganography and data hiding have been proposed. Steganography is a process of embedding the secret information inside the host medium (text, audio, image and video). Concurrently, many of the powerful steganographic analysis software programs have been provided to unauthorized users to retrieve the valuable secret information that was embedded in the carrier files. Some steganography algorithms can be easily detected by steganalytical detectors because of the lack of security and embedding efficiency. In this paper, we propose a secure video steganography algorithm based on the principle of linear block code. Nine uncompressed video sequences are used as cover data and a binary image logo as a secret message. The pixels' positions of both cover videos and a secret message are randomly reordered by using a private key to improve the system's security. Then the secret message is encoded by applying Hamming code (7, 4) before the embedding process to make the message even more secure. The result of the encoded message will be added to random generated values by using XOR function. After these steps that make the message secure enough, it will be ready to be embedded into the cover video frames. In addition, the embedding area in each frame is randomly selected and it will be different from other frames to improve the steganography scheme's robustness. Furthermore, the algorithm has high embedding efficiency as demonstrated by the experimental results that we have obtained. Regarding the system's quality, the Pick Signal to Noise Ratio (PSNR) of stego videos are above 51 dB, which is close to the original video quality. The embedding payload is also acceptable, where in each video frame we can embed 16 Kbits and it can go up to 90 Kbits without noticeable degrading of the stego video's quality.
Conference Paper
Steganography is the art of secret communication. Since the advent of modern steganography, in the 2000s, many approaches based on the error correcting codes (Hamming, BCH, RS, STC ...) have been proposed to reduce the number of changes of the cover medium while inserting the maximum bits. The works of LDiop and al [1], inspired by those of T. Filler [2] have shown that the LDPC codes are good candidates in minimizing the impact of insertion. This work is a continuation of the use of LDPC codes in steganography. We propose in this paper a steganography scheme based on these codes inspired by the adaptive approach to the calculation of the map detectability. We evaluated the performance of our method by applying an algorithm for steganalysis.