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A New Video Steganography Algorithm Based on the Multiple Object Tracking and Hamming Codes

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A New Video Steganography Algorithm Based on the
Multiple Object Tracking and Hamming 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—In the modern world, video steganography has
become a popular option for secret data communication. The
performance of any steganography algorithm is based on the
embedding efficiency, embedding payload, and robustness
against attackers. In this paper, we propose a new video
steganography algorithm based on the multiple object tracking
algorithm and Hamming codes. The proposed algorithm
includes four different stages. First, the secret message is
preprocessed, and Hamming codes (n, k) are applied in order
to produce an encoded message. Second, a motion-based
multiple object tracking algorithm is applied on cover videos in
order to identify the regions of interest of the moving objects.
Third, the process of embedding 3 and 6 bits of the encoded
message into the 1 LSB and 2 LSBs of RGB pixel components
is performed for all motion regions in the video using the
foreground mask. Fourth, the process of extracting the secret
message from the 1 LSB and 2 LSBs for each RGB component
of all moving regions is accomplished. Experimental results of
the proposed video steganography algorithm have
demonstrated a high embedding efficiency and a high
embedding payload.
Keywords- Video steganography; Motion-based multiple
object tracking; Hamming codes; Embedding efficiency;
Eembedding payload
I. INTRODUCTION
Recently, people communicate over the Internet and
share private information. This secret information should be
protected through a secure technique that blocks the data
from intruders and hackers. Steganography is a technique
that protects any secret message from an unintended
recipient’s suspicion within any data form. However, many
steganalytical detectors have been invented that detect a
secret message from an unsecure steganography algorithm.
In order to avoid the secret data from being detected by
steganalytical tools, the steganography algorithm must be
efficient. Every successful steganography algorithm should
contain an embedding efficiency, an embedding payload, and
robustness in order to work against attackers [1, 2].
The steganography algorithm containing a high
embedding efficiency will reduce a hacker’s suspicion of
finding the hidden data, and will be difficult to detect
through steganalysis detectors. In addition, accurate visual
quality of the stego data and a low modification rate of the
cover data will improve the embedding efficiency [3]. The
embedding efficiency includes: perceptual quality,
complexity, and security. The embedding efficiency is
directly affected by the security of the steganographic
algorithm. However, if any obvious distortion of the cover
data after the embedding process occurs, then the result may
be an increase in the attention of the hackers [4]. Another
component of a successful steganography algorithm is a high
embedding payload. The embedding payload is defined as
the amount of secret information that is required to be
embedded inside the cover data. Furthermore, an algorithm
that contains a high embedding payload will have an
extensive capacity to hide a secret message. In traditional
steganographic algorithms, an embedding efficiency and an
embedding payload are opposites. Increasing the capacity of
the secret message will decrease the visual quality of stego
videos resulting in a weakened embedding efficiency. Both
factors should be considered. The deciding factors depend on
the steganography algorithm and the user’s requirements [4,
5]. In order to increase the embedding payload with the low
modification rate of the cover data, many steganography
algorithms have been proposed using alternative methods.
These algorithms use block codes and matrix encoding
principles such as BCH codes, Hamming codes, cyclic
codes, Reed-Solomon codes, and Reed-Muller codes [6].
The contribution of this paper introduces a new video
steganography algorithm based on the multiple object
tracking algorithm and Hamming codes. Our proposed
steganography algorithm offers a reasonable trade-off
between the perceptual quality and an embedding payload.
The remainder of this paper is organized as follows: Section
2 presents some related works. Section 3 explains Hamming
codes. Section 4 reviews motion-based multiple object
tracking algorithm. Section 5 presents the proposed
steganography algorithm. Section 6 illustrates and explains
the experimental results. Section 7 is the conclusion.
II. RELATED WORK
Cheddad et al. proposed a skin tone video steganography
algorithm based on the YCbCr color space. YCbCr color
space is a useful color transformation, which is used in many
techniques such as compression and object detection
methods. The correlation between three color channels
(RGB) is removed, so that the intensity (Y) will be separated
from colors chrominance blue and red (Cb and Cr). After the
human skin regions are detected, the only Cr of these regions
will be utilized for embedding the secret message. [7].
Overall, the algorithm has a low embedding payload because
it has embedded the secret message i
n
component of the skin region. Khupse et
adaptive video steganography scheme us
i
The steganography scheme has been us
e
interest video frames. Khupse et al. used hu
m
a cover data for embedding the secre
t
morphological dilation and filling operati
o
b
een used as a skin detector. After vi
d
converted to YCbCr color space, the fra
minimum mean square error will be s
e
embedding process. Only the Cb co
m
particular frame will be picked for emb
e
message [8] . This scheme is very li
m
b
ecause only one frame is selected for th
e
process. Zhang et al. proposed an efficien
t
BCH codes for steganography. The embe
secret message into a block of cover data
.
p
rocess is completed by changing various
c
input block in order to make the syndrome
efficient embedder improves both stora
g
computational time compared with o
t
According to the system complexity, Z
h
improves the system complexity from exp
o
[9].
There is flexibility for both embeddi
n
embedding payload in the previously ment
i
This flexibility can be used
b
y our propo
improve the algorithm’s performance even
f
III. HAMMING CODES
In this section, the Hamming codes t
e
explained and discussed through a specifi
c
(15, 11). Hamming codes are defined as
p
owerful binary linear codes. These typ
e
detect and correct errors that occur in the
data during the communication between
p
codeword includes both original and e
x
minimum amount of data redundancy, an
d
the encoded message that uses the
technique. In general, if p is parity bits of
a
number  2 ; then, the length of the c
2
1. The size of the message that needs
defined as 2
1. The number
o
must be added to the message is 
of  / [4, 11].
In this paper, Hamming codes (15, 11)
k=11, and p=4), which can correct the i
single bit error. A message of size 
encoded by adding 
,
,
,
extr
a
become a codeword of 15-
b
it length.
T
p
repared to transmit through a communi
c
the receiver end. The common combinatio
n
and parity data using these type of code
s
parity bits at the position of 2
(i=0, 1, ...,
n
,
,
,
,
,
,
,
,
,
,
,
During the encoding and decoding
generator matrix G and parity-check ma
t
used by Hamming codes (15, 11). At
n
to the only Cr
al. proposed an
i
ng steganoflage.
ed in region of
m
an skin color as
t
message. The
o
n methods have
d
eo frames have
a
me that has the
e
lected for data
m
ponent of this
e
dding the secret
m
ited in capacity
e
data embedding
t
embedder using
d
der conceals the
.
The embedding
c
oefficients in the
values null. The
g
e capacity and
t
her algorithms.
h
ang’s algorithm
o
nential to linea
r
n
g efficiency and
i
oned algorithms.
o
sed algorithm to
f
urther.
e
chnique will be
c
Hamming code
one of the most
e
s of codes can
binary block of
p
arties [10]. The
x
tra data with a
d
is the result of
Hamming code
a
positive integer
c
odeword is
to be encoded is
o
f parity bits that
with the rate
are used (n=15,
d
entification of a
,
,…,
is
a
bits as parity to
T
he codeword is
c
ation channel to
n
of both message
s
is to place the
n
-k) as follows:
,
,

,

processes, the
t
rix H are being
the transmitter
channel, a message M, which in
c
multiplied by the generator matrix
G
b
y having modulo of 2. The c
o
obtained and ready to be sent.



At the receiver channel, the e
n
parity) which is a codeword R of 1
5
checked for errors. Once the r
e
multiplied by the parity-check ma
t
then be applied.
A syndrome vector Z 
,
obtained. If the received message
have all zero bits (0000); otherwise
,
one or more bits of the received
m
In that case, the error correction pr
o



Where 1 0 0 0 1 0
0 1 0 0 1 1
0 0 1 0 0 1
0 0 0 1 0 0
The reason of using parity bits
i
to protect the message during com
m
codes (15, 11), 7 bits of the mess
a
each of parity bit, which is illustrat
e
Figure 1. Venn diagram of the Ha
m
IV. MOTION-BASED MULTIP
L
Due to its various applications,
c
the fastest emerging fields in
detection and tracking of mov
i
computer vision field that has re
c
attention. The tracking of movi
n
divided into two major phases:
objects in an individual video fra
m
these detected objects throughout
a
to construct complete tracks [12].
c
ludes of 11-bit, will be
G
, and then, manipulated
o
deword X of 15-bit is

1
n
coded data (message +
5
-bit will be received and
e
ceived codeword R is
t
rix H, modulo of 2 will
,
,
of 4-bit is
is correct, then Z must
,
during the transmission,
m
essage might be flipped.
o
cess must occur.

2
0 1 1 0 1 0 1 1 1
0 1 0 1 1 1 1 0 0
1 0 1 0 1 1 1 1 0
1 1 0 1 0 1 1 1 1
i
n the Hamming codes is
m
unication. In Hamming
a
ge are used to calculate
e
d in the Fig. 1.
m
ming codes (15, 11).
L
E OBJECT TRACKING
c
omputer vision is one of
computer science. The
i
ng objects within the
c
ently gained significant
n
g objects is commonly
1) detection of moving
m
e, and 2) association of
a
ll video frames in order
In the first phase, the background subtraction method,
based on the Gaussian Mixture Model (GMM), is used to
detect the moving objects. GMM is a probability of density
function equal to a weighted sum of component Gaussian
densities. The background subtraction method computes the
differences between consecutive frames that generate the
foreground mask. Then, the noises will be eliminated from
the foreground mask by using morphological operations. As
a result, the corresponding moving objects are detected from
groups of connected pixels.
The second phase is called data association. It is based
on the motion of the detected object. A Kalman filter is
utilized to estimate the motion of each trajectory. In each
video frame, the location of each trajectory is predicted by
the Kalman filter. In addition, the Kalman filter is used to
determine the probability of a specific detection that belongs
to each trajectory [12].
V. THE PROPOSED STEGANOGRAPHY ALGORITHM
In this section, we present a new video steganography
algorithm based on the multiple object tracking algorithm
and Hamming codes (15, 11). Our proposed steganography
is divided into the following four stages:
A. Secret Message Preprocessing Stage
In this work, a large size text file is used as a secret
message, and it is preprocessed before the embedding stage.
Here, the whole characters in the text file are converted into
ASCII codes in order to generate an array of binary bits.
Then, for security purposes, the binary array is encrypted by
using a key (Key1) that represents the size of the secret
message. This process will encode the message and protect it
from attackers. Since the binary linear block of Hamming
codes (15, 11) are used, the encrypted array is divided into
11-bit blocks. Then, every block is encoded by the Hamming
codes (15, 11) that will produce 15-bit blocks. The size of
the message is extended by adding four parity bits into each
block. Another key (Key2) is utilized to generate randomized
15-bit numbers, and each number is XORed with the 15-bit
encoded block. The security of the proposed algorithm will
be improved by using two keys, Hamming codes, and XOR
operation.
B. Motion-Based Multiple Object Tracking Stage
The motion-based multiple object tracking algorithm has
been previously explained in Section 4. The process of
identifying the moving objects in the video frames must be
performed when motion object regions are used as cover
data. This process is achieved by detecting each moving
object within an individual frame, and then associating these
detections throughout all of the video frames. The
background subtraction method is applied to detect the
moving objects. Then, the Kalman filter is used to predict
estimation trajectory of each moving object.
C. Data Embedding Stage
In each video frame, the cover data of the proposed
algorithm is the motion objects that are considered as regions
of interest. The motion regions are identified through the
video frames after they are detected and tracked. The region
of interest changes in every frame based on the size and the
number of the moving objects. The motion-based multiple
object tracking algorithm is applied in order to predict
trajectories of all moving objects. In each video frame, the
background subtraction method is administered to generate a
foreground mask which will determine the regions of the
moving objects. Then, the R, G, and B components of each
motion object’s pixels are used for embedding purposes. In
the proposed algorithm, the 1 LSB and 2 LSBs are utilized in
order to embed 3 and 6 bits of the secret message in each
motion pixel. Moreover, in order to transmit keys to the
receiver party, both keys are embedded into the non-motion
region of the first video frame. Upon completion, the stego
frames will be reconstructed in order to produce the stego
video format that transmits via the communication medium
to the receiver party. Fig. 2 shows the block diagram of the
data embedding stage.
D. Data Extraction Stage
The process of the data extracting stage is illustrated in
Fig. 3. In order to retrieve a secret message correctly, the
stego video is divided into frames through the receiver, and
then two keys are extracted from the non-motion region of
the first frame. To predict trajectories of motion objects, the
motion-based multiple object tracking algorithm is applied
again by the receiver. Moreover, in each video frame, a
foreground mask that is similar to the embedding stage’s
mask is produced by using the background subtraction
method. Then, the process of extracting the hidden message
is conducted by taking out 3 and 6 bits from the 1 LSB and 2
LSBs of RGB color components in each motion object’s
pixels of all the video frames. The extracted bits from all the
video frames are stored in a binary array. The binary array is
divided into 15-bit blocks. Each block will be XORed with
the 15-bit number randomly generated by Key2. The results
of the 15-bit blocks are decoded by using the Hamming (15,
11) decoder to produce 11-bit blocks. Since the sender has
encrypted the secret message, the obtained array is decrypted
by using Key1. The final array is divided into an 8-bit code
(ASCII) in order to generate the right characters of the
original message.
VI. EXPERIMENTAL RESULTS AND ANALYSIS
Three S2L1 video sequences of different views (View1,
View3, and View4) were used from the well-known
PETS2009 dataset [13]. The implemented videos contain
moving objects which are taken by different stationary
cameras. Experimental results are obtained by using the
R2013a version of the MATLAB software program. The
videos contain a 768x576 pixel resolution at 30 frames per
second, and a data rate of 12684 kbps. Each cover video
sequence contains 795 frames. In all the video frames, the
secret message appears as a large text file split in accordance
with the size and number of the moving objects.
Figure 2. Block diagram of the data embedding stage of the proposed algorithm.
Figure 3. Block diagram of the data extraction stage of the proposed algorithm.
A. Visual Quality
The visual quality of the proposed algo
r
by applying the Peak Signal to Noise Rati
o
PSNR is a non-perceptual objective metric
t
difference between the original and the dist
o
calculated as follows:
 10 


 
And Mean Square Error (MSE) is calcul
a
∑∑,,





C and S refer to the cover frame
a
respectively. In addition, m and n are
d
resolutions, and h indicates the R, G, and
B
(k=1, 2, and 3).
Fig. 4 shows the PSNR comparison
o
(View1) when using 1 LSB and 2 LSBs
object’s RGB pixels. Here, the PSNR value
for 1 LSB and 40.45 dB for 2 LSBs.
Fig. 5 illustrates the PSNR comparis
o
experiment when using 1 LSB and 2 LSB
s
pixel in the video frames. The PSNR value
s
43.88 dBs for 1 LSB and 2 LSBs, respectiv
e
Fig. 6 shows the PSNR comparison of
when using 1 LSB and 2 LSBs of each mot
i
pixels. Here, the PSNR values equal 51.35
d
44.16 dB for 2 LSBs.
The third experiment (View4) has bet
t
among other experiments because it has
f
moving objects than others. This means tha
t
embed less size of the secret data tha
n
experiments. Overall, the stego videos’ vi
s
close to the original videos’ visual qualitie
s
values of PNSRs for our proposed algorith
m
Figure 4. The PSNR comparison of the View1
r
ithm is measured
o
(PSNR) metric.
t
hat measures the
o
rted videos. It is
3
a
ted as follows:
,,
4
a
nd stego frame,
d
efined as video
B
color channels
o
f the first video
of each motion
s equal 47.73 dB
o
n of the View3
s
of each motion
s
equal 50.93 and
e
ly.
the View4 video
i
on object’s RGB
d
B for 1 LSB and
t
er visual quality
f
ewe
r
regions of
t
View4 video can
n
the other two
s
ual qualities are
s
due to the high
m
.
experiment.
Figure 5. The PSNR comparison
Figure 6. The PSNR comparison of
B. Embedding Payload
According to the reference [14]
has a high embedding payload. Her
e
hiding ratios for three experiments
i
hidden secret message in each V
i
videos using 1 LSB is 31.38, 14.
6
respectively. Moreover, when usin
g
the secret message in each Vie
experiments will be 62.77, 29.2
5
respectively. The hiding ratio can b
e
     
 
Fig. 7, 8, and 9 illustrate the d
a
the proposed steganography alg
o
View3, and View4 experiments.
T
shown the comparison of the em
b
video when 1 LSB and 2 LSBs of t
h
of the View3 video.
the View4 experiment.
, our proposed algorithm
e
,
t
he average of obtained
i
s 3.37%. The size of the
i
ew1, View3, and View4
6
2, and 12.95 Megabits,
g
2 LSBs, the amount of
w
1, View3, and View4
5
, and 25.92 Megabits,
e
calculated as follows:
  100% 5
a
ta embedding payload of
o
rithm for each View1,
T
hese three figures have
b
edding capacity of each
h
e moving objects’ pixels
are utilized. The 2 LSBs were impleme
n
double the amount of the secret message in
e
Figure 7. The embedding payload comparison of the
Figure 8. The embedding payload comparison of
t
Figure 9. The embedding payload comparison of the
n
ted in order to
e
ach experimen
t
.
View1 experiment.
t
he View3 video.
View4 experiment.
VII. CONCLUSI
O
This paper introduces a ne
w
algorithm based on the multiple
o
and Hamming codes. The four alg
o
the secret message preprocessing s
t
multiple object tracking algorit
h
embedding process stage; and 4)
t
The performance of the propos
e
through a series of experiments.
O
demonstrated that the proposed
embedding efficiency based on
h
PSNRs. Furthermore, the pro
p
p
rotection methods on the secre
t
embedding process creating
Moreover, the proposed algorith
m
payload due to the sizable amo
u
message. In our future work, we
algorithm in the frequency domai
n
robustness of the algorithm agains
t
video processing attacks and artific
i
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t
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tions on, vol. 15, pp. 2431-2440,
... The results of the proposed algorithm show that the scheme achieved high embedding capacity with high imperceptibility. After that Mstafa et al. [15] proposed a scheme based on multiple object tracking where data was hidden in RGB components by using the LSB technique. However, the proposed schemes utilized spatial domain technique for embedding leaving some space for robustness enhancement. ...
... The results of the proposed scheme's evaluation have also been compared with the existing work Ramalingam et al. [8], Mstafa et al. [15], Rezagholipour et al. [33] and Mstafa et al. [17], Nyo et al. [10] and Thahab [7]; by embedding the same secret image for all the methods and implementation was done using the considered videos. The comparative results are shown in Fig. 7 and the results are compared in terms of PSNR with the existing work. ...
... Additionally, the elapsed time of the proposed scheme has also been compared with the considered existing techniques and the average elapsed time is plotted in Fig. 8. It is evident form the graph that average elapsed time of the proposed embedding scheme is less than Ramalingam et al. [8], Mstafa et al. [15], Rezagholipour et al. [33] and Mstafa et al. [17] and it is comparable to the work of Nyo et al. [10] and Thahab [7] with the negligible margin of 0.5916 and 0.08903 s respectively. However, the time latency is less important as the advancement in computation power reveal the trail of precision. ...
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The proposed scheme utilized H.264/AVC video format for steganography which is the most common video standard at present. The scheme employed Discrete Wavelet Transform (DWT) on the Region of Interest (ROI) based on multiple moving objects tracking. After tracking multiple objects, each object is embedded with different secret images to improve the capacity. Multiple object tracking helps in achieving robustness and security; in addition, secret data is encrypted before embedding to provide a high level of security. The proposed scheme is tested on numerous video sequences by evaluating both subjective and objective metrics. Different metrics employed for objective evaluation involves Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity (SSIM) Index, Normalized Cross Correlation (NCC) and Bit Error Rate (BER). However, subjective evaluation is carried out by visual inspection. Additionally, the proposed scheme has been tested against noise, compression, frame rate change and scaling attacks to ensure robustness. Further, the security performance has been analysed by testing against three existing steganalysis techniques and histogram analysis. The main aim of the paper is to focus on robustness and security without compromising hiding capacity and imperceptibility. The reckoning result proves not only high robustness and security but also improves imperceptibility and capacity.
... As a result, it is occasionally advised that both techniques be combined. In such a situation, even if the attacker had concerns about the existence of the communication and succeeded to overcome the steganography technique, the perpetrator would still have to crack the encrypted message to acquire the hidden message [5][6][7][8]. ...
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The rapid transmission of multimedia information has been achieved mainly by recent advancements in the Internet's speed and information technology. In spite of this, advancements in technology have resulted in breaches of privacy and data security. When it comes to protecting private information in today's Internet era, digital steganography is vital. Many academics are interested in digital video because it has a great capability for concealing important data. There have been a vast number of video steganography solutions developed lately to guard against the theft of confidential data. The visual imperceptibility, robustness, and embedding capacity of these approaches are all challenges that must be addressed. In this paper, a novel solution to reversible video steganography based on Discrete Wavelet Transform (DWT) and Quick Response (QR) codes is proposed to address these concerns. In order to increase the security level of the suggested method, an enhanced ElGamal cryptosystem has also been proposed. Prior to the embedding stage, the suggested method uses the modified ElGamal algorithm to encrypt secret QR codes. Concurrently, it applies two-dimensional DWT on the Y-component of each video frame resulting in Approximation (LL), Horizontal (LH), Vertical (HL), and Diagonal (HH) sub-bands. Then, the encrypted Low (L), Medium (M), Quantile (Q), and High (H) QR codes are embedded into the HL sub-band, HH sub-band, U-component, and V-component of video frames, respectively, using the Least Significant Bit (LSB) technique. As a consequence of extensive testing of the approach, it was shown to be very secure and highly invisible, as well as highly resistant to attacks from Salt & Pepper, Gaussian, Poisson, and Speckle noises, which has an average Structural Similarity Index (SSIM) of more than 0.91. Aside from visual imperceptibility, the suggested method exceeds current methods in terms of Peak Signal-to-Noise Ratio (PSNR) average of 52.143 dB, and embedding capacity 1 bpp.
... This authentication method can protect video content from spatio-temporal tampering, and recover the modified regions and moving objects. In [240], a video steganography algorithm uses motion-based multiple object tracking to identify ROIs. A Gaussian mixture model is combined with morphological operations to subtract the background and identify object regions. ...
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Data hiding or information hiding is a prominent class of information security that aims at reliably conveying secret data embedded in a cover object over a covert channel. Digital media such as audio, image, video, and three-dimensional (3D) media can act as cover objects to carry such secret data. Digital media security has acquired tremendous significance in recent years and will be even more important with the development and delivery of new digital media over digital communication networks. In particular, least significant bit (LSB) data hiding is easy to implement and to combine with other hiding techniques, offers high embedding capacity for data, can resist steganalysis and several types of attacks, and is well suited for real-time applications. This article provides a comprehensive survey on LSB data hiding in digital media. The fundamental concepts and terminologies used in data hiding are reviewed along with a general data hiding model. The five attributes of data hiding, i.e., capacity, imperceptibility, robustness, detectability, and security, and the related performance metrics used in this survey to compare the characteristics of the different LSB data hiding methods are discussed. Given the classification of data hiding methods with respect to audio, image, video, and 3D media, a comprehensive survey of LSB data hiding for each of these four digital media is provided. In particular, landmark studies, state-of-the-art approaches, and applications of LSB data hiding are described for each of the four digital media. Their performance is compared with respect to the data hiding attributes which illustrates benefits and drawbacks of the reviewed LSB data hiding methods. The article concludes with summarizing main findings and suggesting directions for future research. This survey will be helpful for researchers and practitioners to keep abreast about the potential of LSB data hiding in digital media and to develop novel applications based on suitable performance trade-offs between data hiding attributes.
... The paper [14] presents a video Steganography algorithm to hide the secret data inside a video by using Hamming codes (15,11) to encode the data before embedding and multiple objects tracking algorithm to select the embedding position in the video. The algorithm proves an excellent security level, but embedding capacity in this method is low. ...
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In the previous decade, the mixing between chaotic supposition and cryptography frames considers a significant field of data security. Chaos-based image encryption is given a lot of attention in the exploration of data security moreover a great deal of picture encrypting calculations have been proposed concerning chaotic maps. Because of some inveterate highlights of media like information limit and high information excess, the encryption of images not quite the same as that of texts; accordingly it is hard to deal with them by conventional encryption strategies. This paper presents a short review of robust digital watermarking systems that used chaotic algorithms such as Logistic, Tent, Baker, Hyper, Fibonacci, and Arnold maps for encryption of the data presented in several years.
... The second metric, imperceptibility, is evaluated by measuring the visual quality of the stego-videos. Usually, to measure this metric, the Peak Signal-to-Noise Ratio (PSNR) is used, which is measured in decibels (dB) and calculated as in Eq. (10). PSNR values falling below 30 dB indicate that the human eye can notice the distortion. ...
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Recent developments in the speed of the Internet and information technology have made the rapid exchange of multimedia information possible. However, these developments in technology lead to violations of information security and private information. Digital steganography provides the ability to protect private information that has become essential in the current Internet age. Among all digital media, digital video has become of interest to many researchers due to its high capacity for hiding sensitive data. Numerous video steganography methods have recently been proposed to prevent secret data from being stolen. Nevertheless, these methods have multiple issues related to visual imperceptibly, robustness, and embedding capacity. To tackle these issues, this paper proposes a new approach to video steganography based on the corner point principle and LSBs algorithm. The proposed method first uses Shi-Tomasi algorithm to detect regions of corner points within the cover video frames. Then, it uses 4-LSBs algorithm to hide confidential data inside the identified corner points. Besides, before the embedding process, the proposed method encrypts confidential data using Arnold's cat map method to boost the security level. Experimental results revealed that the proposed method is highly secure and highly invisible, in addition to its satisfactory robustness against Salt & Pepper noise, Speckle noise, and Gaussian noise attacks, which has an average Structural Similarity Index (SSIM) of more than 0.81. Moreover, the results showed that the proposed method outperforms state-of-the-art methods in terms of visual imperceptibility, which offers excellent peak signal-to-noise ratio (PSNR) of average 60.7 dB, maintaining excellent embedding capacity. INDEX TERMS Arnold's Cat map, corner detector, embedding capacity, imperceptibility, robustness, security, video steganography.
Chapter
Securing information has been very anxious regarding the advancement of data science, technology as well as research fields. To resolve the security issues, information hiding algorithms have developed based on deep learning technique. Steganography refers to the process of hiding information within specific files whereas Steganalysis refers the process of recognition of hidden information within those files. In this chapter, we have reviewed several existing work done by various researchers on steganography videos and images using both machine learning and deep learning algorithms. In addition, we focused on Convolutional Neural Network framework, block diagrams for performing Steganalysis process especially hidden information that referred by several researchers. It has five sections namely image steganography, video steganography, automatic detection, integration of audio and video steganography, and finally state of art using deep learning algorithms have illustrated. This proposed review creates an entire idea on steganography videos and image data hiding based on both ML and DL technique.
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In the last few decades, information security has gained huge importance owing to the massive growth in digital communication; hence, driving steganography to the forefront for secure communication. Steganography is a practice of concealing information or message in covert communication which involves hiding the information in any multimedia file such as text, image, or video. Many contributions have been made in the domain of image steganography; however, due to the low embedding capacity and robustness of images; videos are gaining more attention of academic researchers. This paper aims to provide a qualitative as well as quantitative analysis of various video steganography techniques by highlighting their properties, challenges, pros, and cons. Moreover, different quality metrics for the evaluation of distinct steganography techniques have also been discussed. The paper also provides an overview of steganalysis attacks which are commonly employed to test the security of the steganography techniques. The experimental analysis of some of the prominent techniques using different quality metrics has also been done. This paper also presented a critical analysis driven from the literature and the experimental results. The primary objective of this paper is to help the beginners to understand the basic concepts of this research domain to initiate their research in this field. Further, the paper highlighted the real-life applications of video steganography and also suggested some future directions which require the attention of the research community.
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Abstract— In this internet era, there is tremendous growth in the amount of digital data that exchange and disseminate across various internet platforms, especially video. Maintaining copyright for this data is one of the essential things that made people worry. So, there is continuously question around the protection techniques of the data. Video steganography is the human visual system cannot recognise one of the methods that used to hide data inside the digital video file in away. Video watermarking is another technique used to insert additional data inside the digital video to protects its intellectual right against unauthorised manipulation. In this paper, it will use the principle of both steganography and watermarking technique to maintain the video copyright by hiding publisher logo image inside the digital video file by embedding the logo pixel data in LSB of the video frames. The most famous known of the video steganography, watermarking techniques which used in the presented-day has become traditional methods to the attackers. Therefore, there is a necessity to a new smart approach to maintaining the copyright of the digital video. In this paper, an optimised technique is proposed by utilising the advantages of Turbo code to encryption the logo image bits and least significant bit (LSB) approach to embedding the encoded logo pixels inside the frames of the cover video. This process made the system have powerful and high security against the different hacker attacks. The system proved a high quality in result Stegovideo by relative rate reach to 98% from the quality of the original video and PSNR equal about (57 dB) with high robustness against noise like salt and pepper and Gaussian noise.
Chapter
In this digital era, with the advent of technology like 4G network and VOIP, video calling is the most cost effective and cutting-edge communication technique. Simultaneously, video sharing through social networking sites is very popular, as it can reach a wider public domain in seconds. This enormous use of video motivates the fact that as digital medium video can be effectively utilized for secret sharing. Using video steganography, any kind of secret data like text, image, audio, even a short video can be hidden inside another video object, which can be securely transmitted to the recipient over the internet. In this chapter, an effort has been made to relate various techniques of video steganography under a single header to identify future scope of research. Also, all possible quality metrics for videos and for measuring robustness have been studied, and different steganalysis attacks on video have been analyzed. The broad mission of this chapter is to be a quick reference to future researchers of video steganography.
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In this paper, in order to improve the security and efficiency of the steganography algorithm, we propose an efficient video steganography algorithm based on the binary BCH codes. First the pixels’ positions of the video frames’ components are randomly permuted by using a private key. Moreover, the bits’ positions of the secret message are also permuted using the same private key. Then, the secret message is encoded by applying BCH codes (n, k, t), and XORed with random numbers before the embedding process in order to protect the message from being read. The selected embedding area in each Y, U, and V frame components is randomly chosen, and will differ from frame to frame. The embedding process is achieved by hiding each of the encoded blocks into the 3-2-2 least significant bit (LSB) of the selected YUV pixels. Experimental results have demonstrated that the proposed algorithm have a high embedding efficiency, high embedding payload, and resistant against hackers.
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Recently, video steganography has become a popular option for a secret data communication. The performance of any steganography algorithm is based on the embedding efficiency, embedding payload, and robustness against attackers. In this paper, we propose a novel video steganography algorithm in the wavelet domain based on the KLT tracking algorithm and BCH codes. The proposed algorithm includes four different phases. First, the secret message is preprocessed, and BCH codes (n, k, t) are applied in order to produce an encoded message. Second, face detection and face tracking algorithms are applied on the cover videos in order to identify the facial regions of interest. Third, the process of embedding the encoded message into the high and middle frequency wavelet coefficients of all facial regions is performed. Forth, the process of extracting the secret message from the high and middle frequency wavelet coefficients for each RGB components of all facial regions is accomplished. Experimental results of the proposed video steganography algorithm have demonstrated a high embedding efficiency and a high embedding payload.
Conference Paper
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Video steganography has become a popular topic due to the significant growth of video data over the Internet. The performance of any steganography algorithm depends on two factors: embedding efficiency and embedding payload. In this paper, a high embedding payload of video steganography algorithm has been proposed based on the BCH coding. To improve the security of the algorithm, a secret message is first encoded by BCH(n,k,t) coding. Then, it is embedded into the discrete wavelet transform (DWT) coefficients of video frames. As the DWT middle and high frequency regions are considered to be less sensitive data, the secret message is embedded only into the middle and high frequency DWT coefficients. The proposed algorithm is tested under two types of videos that contain slow and fast motion objects. The results of the proposed algorithm are compared to both the Least Significant Bit (LSB) and [1] algorithms. The results demonstrate better performance for the proposed algorithm than for the others. The hiding ratio of the proposed algorithm is approximately 28%, which is evaluated as a high embedding payload with a minimal tradeoff of visual quality. The robustness of the proposed algorithm was tested under various attacks. The results were consistent.
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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.
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Innovation of technology and having fast Internet make information to distribute over the world easily and economically. This is made people to worry about their privacy and works. Steganography is a technique that prevents unauthorized users to have access to the important data. The steganography and digital watermarking provide methods that users can hide and mix their information within other information that make them difficult to recognize by attackers. In this paper, we review some techniques of steganography and digital watermarking in both spatial and frequency domains. Also we explain types of host documents and we focused on types of images.
Conference Paper
Steganography is define as the process of hiding information in a multimedia carrier. Its ultimate objectives are undetectability and robustness of the hidden data. It is recognized as adaptive steganography as the data is embed in to the specific Region of Interest (ROI) of the cover image for the purpose of safety of the inserted data. The assurance of both imperceptibility and robustness requirements are the main objectives of developing an image-hiding technique. The focus is on embedding the data in the skin region of a video frame. Thus focus is on the skin detection algorithm to extract the skin region. This is act as the region of interest for embedding the secret message. The video frames are then converted to YCbCr colour space to perform embedding. The frame having least MSE is selected to embed a secret data. The secret data is then inserted into the Cb component of YCbCr of a frame having least MSE. After embedding secret data Steganoflage video is created by transforming the data into RGB colour space.
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This paper presents an improved data hiding technique based on BCH (n,k,t ) coding. The proposed embedder hides data into a block of input data by modifying some coefficients in the block in order to null the syndrome. The proposed embedder can hide data with less computational time and less storage capacity compared to the existing methods. The complexity of the proposed method is linear while that of other methods are exponential for any block size n. Thus, it is easy to extend this method to a large n. The BCH syndrome coding for steganography is now viable ascribed to the reduced complexity and its simplicity of the proposed embedder.
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The meaning of data (information) hiding is to embed the secret information into a cover host, such as an image. Usually, the naked eye of the people cannot perceive any change when the image is modified slightly. The evaluation of data hiding schemes should be measured by the distortion (or called Mean Square Error; MSE) and the embedding rate (the average number of bits embedded in a cover pixel). In this paper, we propose two improved data hiding schemes. One is to improve the EMD (Exploiting Modification Direction)-based data hiding algorithm to have higher stego-image quality. The other is to improve the Matrix encoding-based data hiding algorithm by using the idea of Hamming+1 to further improve the stego-image quality. Both proposed improved schemes are verified to be correct through the theoretical analysis and the experiment.
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In matrix embedding (ME)-based steganography, the host coefficients are minimally perturbed such that the transmitted bits fall in a coset of a linear code, with the syndrome conveying the hidden bits. The corresponding embedding distortion and vulnerability to steganalysis are significantly less than that of conventional quantization index modulation (QIM)-based hiding. However, ME is less robust to attacks, with a single host bit error leading to multiple decoding errors for the hidden bits. In this paper, we employ the ME-RA scheme, a combination of ME-based hiding with powerful repeat accumulate (RA) codes for error correction, to address this problem. A key contribution of this paper is to compute log likelihood ratios for RA decoding, taking into account the many-to-one mapping between the host coefficients and an encoded bit, for ME. To reduce detectability, we hide in randomized blocks, as in the recently proposed Yet Another Steganographic Scheme (YASS), replacing the QIM-based embedding in YASS by the proposed ME-RA scheme. We also show that the embedding performance can be improved by employing punctured RA codes. Through experiments based on a couple of thousand images, we show that for the same embedded data rate and a moderate attack level, the proposed ME-based method results in a lower detection rate than that obtained for QIM-based YASS.