19-02-2013, 04:51 PM
Data Hiding in Motion Vectors of Compressed Video Based on Their Associated Prediction Error
[attachment=51379]
Abstract
This paper deals with data hiding in compressed
video. Unlike data hiding in images and raw video which operates
on the images themselves in the spatial or transformed domain
which are vulnerable to steganalysis, we target the motion vectors
used to encode and reconstruct both the forward predictive
(P)-frame and bidirectional (B)-frames in compressed video. The
choice of candidate subset of these motion vectors are based on
their associated macroblock prediction error, which is different
from the approaches based on the motion vector attributes such as
the magnitude and phase angle, etc. A greedy adaptive threshold
is searched for every frame to achieve robustness while maintaining
a low prediction error level. The secret message bitstream
is embedded in the least significant bit of both components of
the candidate motion vectors. The method is implemented and
tested for hiding data in natural sequences of multiple groups of
pictures and the results are evaluated. The evaluation is based on
two criteria: minimum distortion to the reconstructed video and
minimum overhead on the compressed video size. Based on the
aforementioned criteria, the proposed method is found to perform
well and is compared to a motion vector attribute-based method
from the literature.
INTRODUCTION
DATA hiding [1] and watermarking in digital images
and raw video have wide literature. This paper targets
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, thus it is not prone
to quantization distortions. In the literature, most work applied
on data hiding in motion vectors relies on changing the motion
vectors based on their attributes such as their magnitude, phase
angle, etc. In [2] and [3], 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. In [4], the data is encoded as a
region where the motion estimation is only allowed to generate
motion vectors in that specified region.
BACKGROUND AND NOTATIONS
In this section, we overview lossy video compression to define
our notation and evaluation metrics. At the encoder, the intrapredicted
(I)-frame is encoded using regular image compression
techniques similar to JPEG but with different quantization
table and step; hence the decoder can reconstruct it independently.
The I-frame is used as a reference frame for encoding a
group of forward motion-compensated prediction (P)- or bidirectionally
predicted (B)-frames. In the commonly used Motion
Picture Expert Group (MPEG-2) standard [8], the video is ordered
into groups 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 of size
in or and searched in target frame(s).
RESULTS
We implemented the hiding and extraction Algorithms 1, 2,
and 3 and integrated them to the MPEG-2 encoder and decoder
operation. The parameters of our experiments, presented in this
section, are: macroblock size , motion vector representation
bits . We used both the fast three-steps and exhaustive
search motion estimation algorithms with half pixel
accuracy. Each test video sequence is organized into consecutive
GOP organized as [I,B,B,P,B,B,P,B,B]. The compression
to the I-frame and the prediction error of the P- and B-frames
are implemented using JPEG compression with a quality factor
75, 70, and 30, respectively. We tested our algorithms on six
standard test sequences: car-phone, foreman, coastguard, football,
flower-garden, and mobile sequence which are all shown
in Fig. 2.
CONCLUSION
We proposed a new data-hiding method in the motion vectors
of MPEG-2 compressed video. Unlike most data-hiding
methods in the motion vectors that rely their selection on attributes
of the motion vectors, we chose a different approach
that selects those motion vectors whose associated macroblocks
prediction error is high (low PSNR) to be the candidates for
hiding a bit in each of their horizontal and vertical components.
A greedy search for the suitable value of the threshold to be
used for choosing the macroblocks corresponding to the CMV
is done such that the candidates will be identically identified
by the decoder even after these macroblocks have been lossy
compressed.
[attachment=51379]
Abstract
This paper deals with data hiding in compressed
video. Unlike data hiding in images and raw video which operates
on the images themselves in the spatial or transformed domain
which are vulnerable to steganalysis, we target the motion vectors
used to encode and reconstruct both the forward predictive
(P)-frame and bidirectional (B)-frames in compressed video. The
choice of candidate subset of these motion vectors are based on
their associated macroblock prediction error, which is different
from the approaches based on the motion vector attributes such as
the magnitude and phase angle, etc. A greedy adaptive threshold
is searched for every frame to achieve robustness while maintaining
a low prediction error level. The secret message bitstream
is embedded in the least significant bit of both components of
the candidate motion vectors. The method is implemented and
tested for hiding data in natural sequences of multiple groups of
pictures and the results are evaluated. The evaluation is based on
two criteria: minimum distortion to the reconstructed video and
minimum overhead on the compressed video size. Based on the
aforementioned criteria, the proposed method is found to perform
well and is compared to a motion vector attribute-based method
from the literature.
INTRODUCTION
DATA hiding [1] and watermarking in digital images
and raw video have wide literature. This paper targets
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, thus it is not prone
to quantization distortions. In the literature, most work applied
on data hiding in motion vectors relies on changing the motion
vectors based on their attributes such as their magnitude, phase
angle, etc. In [2] and [3], 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. In [4], the data is encoded as a
region where the motion estimation is only allowed to generate
motion vectors in that specified region.
BACKGROUND AND NOTATIONS
In this section, we overview lossy video compression to define
our notation and evaluation metrics. At the encoder, the intrapredicted
(I)-frame is encoded using regular image compression
techniques similar to JPEG but with different quantization
table and step; hence the decoder can reconstruct it independently.
The I-frame is used as a reference frame for encoding a
group of forward motion-compensated prediction (P)- or bidirectionally
predicted (B)-frames. In the commonly used Motion
Picture Expert Group (MPEG-2) standard [8], the video is ordered
into groups 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 of size
in or and searched in target frame(s).
RESULTS
We implemented the hiding and extraction Algorithms 1, 2,
and 3 and integrated them to the MPEG-2 encoder and decoder
operation. The parameters of our experiments, presented in this
section, are: macroblock size , motion vector representation
bits . We used both the fast three-steps and exhaustive
search motion estimation algorithms with half pixel
accuracy. Each test video sequence is organized into consecutive
GOP organized as [I,B,B,P,B,B,P,B,B]. The compression
to the I-frame and the prediction error of the P- and B-frames
are implemented using JPEG compression with a quality factor
75, 70, and 30, respectively. We tested our algorithms on six
standard test sequences: car-phone, foreman, coastguard, football,
flower-garden, and mobile sequence which are all shown
in Fig. 2.
CONCLUSION
We proposed a new data-hiding method in the motion vectors
of MPEG-2 compressed video. Unlike most data-hiding
methods in the motion vectors that rely their selection on attributes
of the motion vectors, we chose a different approach
that selects those motion vectors whose associated macroblocks
prediction error is high (low PSNR) to be the candidates for
hiding a bit in each of their horizontal and vertical components.
A greedy search for the suitable value of the threshold to be
used for choosing the macroblocks corresponding to the CMV
is done such that the candidates will be identically identified
by the decoder even after these macroblocks have been lossy
compressed.