08-08-2012, 12:47 PM
Robust Video Data Hiding with using Forbidden Zone and Data Hiding and Selective Embedding
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Abstract
Video data hiding is still an important research topic due to the
design complexities involved. We purpose a new video data
hiding method that makes use of erasure correction capability of
repeat accumulate codes and superiority of forbidden zone data
hiding. Selective embedding is utilized in the proposed method
to determine host signal samples suitable for data hiding. This
method also contains a temporal synchronization scheme in
order to withstand frame drop and insert attacks. The proposed
framework is tested by typical broadcast material against MPEG-2,
H.264 compression, frame-rate conversion attacks, as well as other
well-known video data hiding methods. The decoding error values
are reported for typical system parameters. The simulation results
indicate that the framework can be successfully utilized in video
data hiding applications.
Introduction
Data hiding in video sequences is performed in two major ways;
bitstream-level and data-level. In bitstream-level, the redundancies
within the current compression standards are exploited. Typically,
encoders have various options during encoding and this freedom
of selection is suitable for manipulation with the aim of data
hiding. However, these methods highly rely on the structure of the
bitstream; hence, they are quite fragile, in the sense that in many
cases they cannot survive any format conversion or transcoding,
even without any significant loss of perceptual quality. As a result,
this type of data hiding methods is generally proposed for fragile
applications, such as authentication. On the other hand, data level
methods are more robust to attacks. Therefore, they are suitable
for a broader range of applications.
Hiding Privacy Information
Video Surveillance has become a part of our daily lives. Closedcircuit
cameras are mounted in countless shopping malls for
deterring crimes, at toll booths for assessing tolls, and at traffic
intersections for catching speeding drivers. Terrorist attack, there
have been much research efforts directed at applying advanced
pattern recognition algorithms to video surveillance. While the
objective is to turn the labor intensive surveillance monitoring
process into a powerful automated system for counter-terrorism.
Reversible Embedding Process
The previous embedding technique, the decoder has no way to
remove the distortion introduced by the embedding process. In this
subsection, we explain a reversible embedding algorithm whose
effect can be reversed on the decoder side after data extraction.
A key requirement for our application is that the output bitstream
with hidden data must be decodable with good quality by
a standard-compliant decoder unaware of the embedding. This
implies that we need to avoid any error caused by drifting and as
such, the decoded frame with the hidden data must be used in the
feedback path in the motion loop. As the motion compensation
does not respect the DCT block boundary, the effect of hiding
one bit in a DCT coefficient may spread to different spatial areas
after many frames. It is an open question on how to make this
temporal spreading reversible. In our current implementation,
we focus on making the DCT embedding process reversible and
prevent temporal spreading by restricting our attention to either
intracoded frames or intracoded-enhanced frames in a two-layer
scalable codec.
Reversible Embedding
The reversible embedding can only be used when there is no
interdependency between the frames. Though the embedding is
done in a reversible fashion, the prediction loop used in intercoded
frames propagate the effect of hidden data to future frames making
the process irreversible. Hence this experiment is conducted in
two special encoder structures. In the first structure, each frame
is coded using intra mode (I frame) only.
Conclusions
A new video data hiding framework that makes use of erasure
correction capability of RA codes and superiority of FZDH. The
method is also robust to frame manipulation attacks via frame
synchronization marker. First compared FZDH and QIM as the
data hiding method of the proposed framework. We observed
that FZDH is superior to QIM, especially for low embedding
distortion levels. The framework was tested with MPEG-2, H.264
compression, scaling and frame-rate conversion attacks. typical
system parameters are reported for error-free decoding. The results
indicate that the framework can e successfully utilized in video
data hiding applications. For instance, Tardos fingerprinting ,
which is a randomized construction of binary fingerprint codes
that are optimal against collusion attack, can be employed within
the proposed framework with the following settings.