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Abstract—This paper deals with data hiding in motion vectors
of compressed video using Least Significant Bit algorithm. Data
hiding is different technique than cryptography. Data hiding
embed the data in to digital media for the identification,
annotation, and copyright. Data hiding is secure adding of
information content to a data product, using mathematical
techniques. These mathematical techniques work at the primitive
level of digital data products, with no perceptual degradation to
data product integrity. We target the motion vectors to encode
and reconstruct the forward predictive (P)-frame. The choice of
these motion vectors are based on their associated macroblock
prediction error, which is different from the approaches based on
the motion vector attributes. The secret message is embedded in
the least significant bit of the candidate motion vectors. Data
hiding in motion vectors relies on changing the motion vectors
based on their attributes such as their magnitude, phase angle,
etc. The method is implemented & tested for hiding data in
sequences of groups of pictures & the results are evaluated.
Experimental result shows that the proposed method is efficient
for data hiding in compressed video by using LSB algorithm
INTRODUCTION
Data hiding is process of secretly embedding information
inside a data source without changing its perceptual quality.
Steganography is the art of concealing a message, image or file
within another message, image or file. The main goal of
steganography is to hide a message m in some audio or video
(cover) data d, to obtain new data dʰ, practically
indistinguishable from d, by people, in such a way that an
eavesdropper cannot detect the presence of m in dʰ.
Steganography methods usually do not need to provide strong
security against removing or modification of the hidden
message. The block diagram of the data hiding technique is
shown in Fig. 1.
We target the internal dynamics of video compression,
specifically the motion estimation stage. We have chosen this
stage because its contents are processed internally during the
video encoding/decoding which makes it hard to be detected by
image steganalysis methods and is lossless coded. When the
frame rate is sufficiently high, there is a great amount of
similarity between successive frames. It is more efficient to
code the difference between frames than the frames themselves. Motion estimation is aimed to reduce such strong
temporal redundancy present in video sequence. So far, the
most successful technique for motion estimation is perhaps the
block matching algorithm (BMA) which has been adopted by
various video coding standards, such as H.261, H.263, MPEG-
1, MPEG-2 and MPEG-4. The block matching is constrained to
search within the selected sector for a magnitude to be larger
than the predefined threshold.
In the literature, most work applied on data hiding in
motion vectors relies on changing the motion vectors based on
their magnitude, phase angle, etc. In [3] and [4], the data bits of
the message are hidden in some of the motion vectors whose
magnitude is above a predefined threshold, and are called
candidate motion vectors (CMVs). A single bit is hidden in the
least significant bit of the larger component of each CMV. The
authors in [5] and [6] embed the data in video using the phase
angle between two consecutive CMV. These CMV are selected
based on the magnitude of the motion vectors as in [3].
The rest of the paper is organized as follows: in section II
we overview the terms of video compression and
decompression. In section III we have given problem
definition. New three step-search method is given in section
IV. The proposed method used here is given in section V
followed by the results and analysis in section VI. Finally the
conclusions are given in section VII.
II. BACKGROUND AND NOTATIONS
In this section, we overview lossless video compression to
define our notation and evaluation metrics. The I-frame is used as a reference frame for encoding a group of forward predicted
(P)-or bidirectionally predicted (B)-frames. In the commonly
used Motion Picture Export Group (MPEG-2) standard [7], the
video is group of pictures (GOPs) whose frames can be
encoded in the sequence: [I,B,B,P,B,B,P,B,B]. The temporal
redundancy between frames is exploited using block based
motion estimation that is applied on macroblocks B in P or B
and searched in target frames.
Generally the motion field in video compression is assumed
to be translational with horizontal component dᵡ and vertical
component dʸ and denoted in vector form by d(x) for the spatial
variables x=(x, y). Motion vectors can be obtained using
methods such as new three step search, three step search etc.;
this is based on the required compression ratio & the
reconstruction quality. Since d does not represent the true
motion in the video then the compensated frame Ṗ using (x +
d(x)) must be associated with a prediction error E(x) = (P - Ṗ)
(x) in order to be able to reconstruct P = Ṗ +E with minimum
distortion at the decoder in case of a P-frame. The motion
vectors d are lossless coded and thus become an attractive
place to hide a message that can be blindly extracted by a
special decoder.
The decoder receives the encoded pair (d, Ẽ), applies
motion compensation to form Ṗ or Ḃ and decompresses Ẽ to
obtain a reconstructed Eᵣ. Since E and Eᵣ are different due to
quantization effect, then decoder unable to reconstruct P
identically but it alternatively reconstructs Pᵣ = Ṗ + Eᵣ. The
reconstruction quality is measured by the mean squared error
(MSE) P - Pᵣ, represented as peak signal-to-noise ratio (PSNR)
denoted by R.
III. PROBLEM DEFINITION
Data hiding in motion vectors at the encoder replaces the
regular pair (d, Ẽ), to become (dʰ, Ẽʰ), where h denotes hiding.
We define data hiding in motion vectors of compressed video
in the context of super-channel [8]; the secret message m is
hidden in the host video signal x = (d, E) to produce the
composite signal s = (dʰ, Eʰ). The composite signal is subject to
video lossless compression to become y = (dʰ, Ẽʰ). The
message m can be identically extracted from y. This robustness
constrain should have low distortion effect on the reconstructed
video as well as low effect on the data size (bit rate). The cost
function mean squared error is given by equation (i)
1 1 2
2
0 0
1
N N
ij ij
i j
MSE C R
N
(i)
Where N is the size of the micro block, Cij and Rij are the
pixels being compared in current macro block and reference
macro block, respectively. Peak-Signal-to-Noise-Ratio (PSNR)
given by equation (ii) characterizes the motion compensated
image that is created by using motion vectors and macro blocks
from the reference frame.
2
(Peak to peak value of the original data)
PSNR = 10Log10 MSE
(ii)
The selection of the CMV is the key difference between
different methods. For instance [3] and [4] choose the CMV
based on their magnitude. On the other hand [5] and [6] choose
CMV based on their magnitude and the phase between
consecutive motion vectors. In our proposed method we rely
directly on the associated macroblock prediction error, such
that we choose our CMV associated with macroblocks of high
prediction error. If we temper with these CMVs, then we will
not have poor effect on the video reconstruction quality.
IV. NEW THREE STEP SEARCH
New three step search used a halfway-stop technique to
speed up the (quasi-) stationary blocks matching. The NTSS
performs better than TSS in terms of mean square error and the
number of search points. For the search window w = 7, in the
first step the NTSS algorithm utilizes a 3 × 3 grid search
pattern at the center in addition to the larger 9 × 9 grid in the
TSS. If the minimum block distortion measure (BDM) point is
one of the eight points on the 3 × 3 grid, only additional five or
three points will be checked, otherwise the search window
center will be moved to the winning point and the following
procedure is the same as in TSS. The details are as follows:
Step 1) Totally 8 + 9 = 17 points are checked including the
central nine points on the 3 × 3 grid and the eight neighboring
points on the 9 × 9 grid. If the minimum BDM point is the
search window center, the search will be terminated; otherwise
go to step 2.
Step 2) If one of the central eight neighboring points on the
3 × 3 grid is found to be the minimum in the first step, go to
step 3; otherwise go to step 4.
Step 3) Move the small 3 × 3 search window so that the
window center is the winning point found in step 1. Search
additional five or three points according to the location of the
previous winning point, then the search will stop.
Step 4) Reduce the large 9 × 9 search window size by half
and move the center to the minimum BDM point in step 1,
follow the searching process of step 2 and step 3 in TSS.