Watermarking of Video Data Using
Integer-to-Integer
Discrete Wavelet Transform
S.N. Merchant
Dept. of Electrical Engineering
Indian Institute of Technology
Powai, Mumbai - 400076.
[email protected]
ABSTRACT
This paper proposes a novel video watermarking
technique to embed a digital watermark in.video
data using integer-to-integer discrete wavelet
transform. The watermark is embedded in the
lowest frequency components of each frame. The
method exploits features of the human visual
system. The watermark can be extracted directly
from the decoded video without access to the
original video. Experimental results have
indicated that the method i s robust against mpeg
encoding and re-encoding. '[t is also perceived
that the method is effective against statistical
attacks.
1. INTRODUCTION
Digital watermarking is a process that embeds
data called a watermark, tag, or label in a
multimedia object such that the watermark san
be detected or extracted later to make an
assertion about the object. The object may be an
image or audio or video [I].
Watermarking is
closely related to the well-established fields of
cryptography and.. steganography yet it is
sufficiently different from both in a variety of
ways [2]. A good review of digital watermarking
applications may be found in [3]. Earlier
watermarking schemes were designed to embed
the copyright information into the spatial domain
of videos [4], [ 5 ] . However most of the current
watermarking
techniques
work
in the
transformed frequeicy domain. The most
commonly used transforms are Discrete Fourier
Transform (DFT) [6], Discrete Cosine Transform
A. Harchandani, S . Dua, H. Donde, 1. Sunesara
Dept. of I.T. Engineering
Thadomal Shahani Engineering College
Bandra (W), Mumbai - 400050.
v [email protected]
(DCT) [7], and Discrete Wavelet Transform
(DWT) [SI, PI.
In this paper, we present a watermarking
technique based on Integer-to-Integer DWT
(IIDWT). We embed the watermark in
luminance component of each frame of the
uncoded video using a user-supplied key. The
watermark can be later extracted from the
luminance component of the decoded video
frame using the same key.
This paper is organized as follows. A brief
background about discrete wavelet Transform is
given in section 2. Integer Wavelets [IO] are
discussed in section 3. The' embedding and
extraction algorithms are explained -in detail in
section. 4, followed by experimental results in
section 5. Finally section 6 concludes the paper.
2. DISCRETE WAVELET
TRANSFORM
The DWT gives us three parts of multiresolution
representation (MRR) and one part of multiresolution approximation (MRA) [ I I]. It is
similar to hierarchical subtiand system, where the
subbands are logarith'niically spaced, in
frequency. The subbands labeled LH,, HL,, and
HH, of MRR represent the finest scale wavelet
coefficients. To obtain the next coarser scale of
wavelet coefficients, the subband LL, (that is
MRA) is further decomposed and critically
subsampled [9]. As an example, Fig. I shows an
image decomposed into ten subbands for three
scales.
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JENCON 2003 / 940
Fig I. D W T Decomposition ofan image,
The wavelet transform has a number o f
advantages over other transforms:
e
It
provides
a
multiresolution
description.
I t allows superior modeling o f the
‘human visual system (HVS)[12].
The high-resolution subbands allow
easy detection o f features such as edges
or textured areas in transform domain.
3. INTEGER WAVELETS
In most cases, the f i l t e r s used during wavelet
transform have floating point coefficients. When
the input data consists o f integers (as i s the case
for luminance frames) the resulting filtered
outputs no longer consists o f integers. Yet for
lossless coding it would be of interest to be able
to characterize the output completely again with
integers. These modified wavelets, which
produce integer outputs, are termed as Integer
Wavelets [IO]. The two main approaches used to
produce integer-to-integer wavelet transforms
are expansion factors [I31 and lifting scheme
[14-15].
We have used the second approach during our
research to obtain an integer version of the
orthogonal Haar Transform. This transform i s
known as the S transform (Sequential).
4. ALGORITHMS
4.0. Introduction
We embed the watermark into Y component of
each frame using the IIDWT. The following
parameters will be used while explaining the
embedding and extraction algorithms:
1. L e t Fi be the i-th Y frame in the original
video with the size o f width x height, where
0 5 i < n a d n i s the total number o f frames.
2. L e t W=[wk] be the P multi-level data (Slevel), i.e. wk = O , I:.. S-l and k = I,2, ...P.
3. L e t N be the scale o f decomposition and the
decomposed elements be LL,(x,y), LH, (x.
y), HL. (x.y) where I 5 n 5 N, I 5 x 5
widthiz”, I 5 y 5 heightI2’.
4. L e t K be the user specified key that acts as a
seed to produce the pair o f secondary keys
(Kif, K,J where 0 5 i < n.
5. Let Q denote the robustness factor
6. 7 denotes the threshold value used to select
coefficients during embedding.
7. I I C j 1 I denotes L’ norm of coefficient C, that
i s considered a 1 x 1 vector.
8. T I and T2 are threshold values such that
TZ<<TI.
4.1. Embedding Algorithm:
The steps in the embedding algorithm are as
follows:
1. Accept the key K from the user and generate
a pair o f keys (Kif, KJ for each of the n
frames.
F o r i = 0 to n-l do the following:
2. Randomize the frame Fi depending upon the
value of the key K,r.This step i s performed
to improve rohustness against collusion
attacks.
3. Apply I I D W T to each frame to obtain the
transformed frame.
4. Calculate R as the number o f coefficients in
the MRA (LL,).
F o r k = l , 2 ...Pdothefollowing:
5. Generate a random number r between 0 and
R using key K,. as seed.
6. Calculate E as sum of norms o f coefficients
C, of HLNand LHN.
7. If E 2 T I then go to step 8.
IfT I > E 2 T multiply coefficients C, o f
HLNand LHNby a factor greater than I and
go to step 8.
If T > E 2 T2 multiply coefficients C, of
HLNand LHNby a factor less than I and go
to step 5.
If E < T2 go to step 5.
This step prevents the watermark data from
being embedded in low frequency regions.
This is because according to the HVS, high
frequencies are less visible than low
frequencies [ 161.
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Digital Watermarking 1941
8. Calculate q, = C, / Q and select the integer
q,' such that it is a multiple of S closest to q,
9. Add value of wk to q,'.
IO. Now recalculate the coefficient C,using q,'.
I I . Finally, use inverse llDWT to obtain the
watermarked frame E,',
4.2 Extraction Algorithm
The steps.in the extraction algorithm are as
follows:
1 . Accept the key K from the user and generate
a pair of keys (K,f, K,J for each of the n
frames.
For i = 0 to n-I do the following:
2. Randomize the frame F, depending upon the
value of the key K,t. This step is performed
to improve robustness against collusion
attacks.
3. Apply IIDWT to each frame to obtain the
transformed frame.
4. Calculate R as the number of coefficients in
the MRA (LL,).
.
Fork = 1,2 ...P do the steps 5 to 7:
5. Generate a random number r between 0 and
R using key K,, as seed.
6 . If E 2 T go to step 7 else go to step 5.
7. Calculate qG= C, / Q and extract wk :
wk = q, mod S.
8. The individual watermarks extracted from
each frame are then compared and the value wk,
which occurs maximum number of times, is
stored at position k in the averaged watermark
W,,
Fig. 2. Original Frame (top left) and its Y
component (bottom lei?); Corresponding
Watermarked Frame (top right) and its Y
component (bottom right)
The watermarked frames shown above were
obtained using P = 32, N=3,T=I00,S=2 and Q =
2. Figure 3 shows the relationship between
length of the binary watermark P and PSNR [ 171
where Q = 2 and PSNR denotes average PSNR
of 30 frames. Table 1 shows the Bit Error Rate
(BER) of the embedded data (watermark) versus
the Length ofthe watermark (P) after performing
MPEG encoding once and Table 2 shows the
same for two MPEG encoding iterations. The
frames are encoded and decoded using an mpeg
codec provided by MPEG Software Simulation
Group (MSSG) [IS]. The BER for each frame is
simply the number of error bits divided by the
length of watermark. An identical watermark is
embedded in each frame. As can be seen from
the table, the watermark is quite robust to MF'EG
encoding and BER increases if MPEG reencoding is performed.
5. EXPERIMENTAL RESULTS
We have done extensive simulation to prove that
the proposed technique is effective under
different conditions. We evaluated the algorithm
using 30 frames of the "flower" mpeg video file,
Each luminance frame is of size 352 x 240 as
shown in fig. 2. As mentioned earlier, we have
used the S Transform during our experiments.
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JENCON 2003 / 942
Table 1 . BER versus P after performing MPEG
encoding once for the proposed as well as the
technique presented in [9].
Length of
watermark
P
2
4
6
14
16
6. CONCLUSION
18
20
22
24
26
28
30
32
34
36
38
40
The proposed method is successfully able to
extract the waterinirk from each frame without
using the original video. The averaged
watermark W,,, obtained i s exactly the same as
the embedded original watermark. Besides, the
embedding of data in only those coefficients
whose norm is greater t h m specified threshold
makes the embedded watermark perceptually
invisible and robust against MPEG encoding and
re-encoding. The randomization of frames prior
to embedding of the watennark increases the
robustness of the watermark towards collusion
and other statistical attacks.
-
In the future, we would like to improve the
proposed method by considering the following
points:
Table 2. BER versus P after performing MPEG
encoding twice for the proposed as well as the
technique presented in [9].
I
Lengthof
watermark
P
I
Proposed
Technique
BER I BER
fur all
frames
I
I
I
I I
Technique using
DWT
BER I RER
for all fur W,
frames
~~
I
~~
I
I
According to [I91 the filters most suitable
for watermarking using wavelets are Villa,
Antonini and Brislawn. We are working on
using integer versions of these wavelets to
improve the BER.'
2. Using the concept ortemporal watermarking
can make another improvement. In this case
the third dimension i.e. time is also taken
into' consideration.
3. This technique can be used for real time
applications by extending it to the
compressed domain.
I.
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Digital Watermarking/ 943
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