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Chapter 5 Spread spectrum Audio Watermarking Algorithms
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Page 1: Chapter 5 Spread spectrum Audio Watermarking Algorithmsshodhganga.inflibnet.ac.in/bitstream/10603/4352/14/14_chapter 5.pdf · Chapter 5 Spread Spectrum Audio Watermarking ... we propose

Chapter 5 Spread spectrum

Audio Watermarking

Algorithms

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Chapter 5 Spread Spectrum Audio Watermarking algorithms

Introduction

Most of the existing audio watermarking techniques embed the watermarks in

the time domain / frequency domain where as there are few techniques which embed

the data in cepstrum or compressed domain. Spread spectrum technique is most

popular technique and is used by many researchers in their implementations [13, 14,

15, 16, 17, 18, 19, 29]. Amplitude scaled Spread Spectrum Sequence is embedded in

the host audio signal which can be detected via a correlation technique. Generally

embedding is based on a psychoacoustic model (which provides inaudible limits of

watermark).

The spread spectrum techniques can be divided in to two categories blind and

non blind techniques. Blind watermarking techniques detect the watermark without

using the original host signal whereas the non blind techniques use the original host

signal to detect the watermark. The most blind watermarking techniques studied in

chapter 2 only detects the presence of the valid watermark and not concentrate on

recovering (extracting) the embedded watermark. Non blind techniques recover the

watermark whereas it requires the original signal to recover the watermark.

Section 5.1 provides the brief overview of conventional spread spectrum

method. Section 5.2 highlights on the non blind technique suggested by Li et al [29]

and implemented in the initial stage of the research to test the watermarking scheme

for our database and then compare the results with our proposed schemes. The results

of this implementation tested for our database are appeared in paper VI. In section 5.3

we propose the adaptive blind watermarking technique based on SNR using DWT and

lifting wavelet transform. The results of these implementations are published partly in

paper VII and paper VIII. To improve the imperceptibility between the original audio

signal and the watermarked audio signal the adaptive SNR based scheme using DWT-

DCT is implemented. Section 5.4 describes this scheme the results of which are

published in paper IX and Paper X. To make the system robust and secure we propose

to embed the watermark using cyclic coding. The scheme which encodes the

watermark using cyclic codes and embeds the watermark is proposed in section 5.5.

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5.1. Spread Spectrum watermarking: Theoretical background

A general model considered [14, 15] for SS-based watermarking is shown in

Figure 5.1. Vector x is considered to be the original host signal transformed in an

appropriate transform domain. The vector y is the received vector, in the transform

domain, after channel noise. A secret key K is used by a pseudo random number

generator (PRN) to produce a chip sequence with zero mean and whose elements are

equal to +σu or -σu. The use of secret key is essential to provide the security to the

watermarking system. The sequence u is then added to or subtracted from the signal

x according to the variable b, where b assumes the values of +1 or -1 according to the

bit (or bits) to be transmitted by the watermarking process (in multiplicative

algorithms multiplication operation is performed instead addition [25]). The signal s is

the watermarked audio signal. A simple analysis of SS-based watermarking leads to a

simple equation for the probability of error. Thus, inner product and norm is defined

as [15]: ⟩⟨=∑=⟩⟨−

=xx,x&uxux,

1N

0iii Where N is the length of the vectors x, s, u, n,

and y in Figure 5.1. Without a loss of generality, we assume that we are embedding

one bit of information in a vector s of N transform coefficients. Then, the bit rate is

1=N bits/sample. That bit is represented by the variable b, whose value is either +1 or

-1. Embedding is performed by

s= x + b u (5.1)

The distortion in the embedded signal is defined by x s − . It is easy to see

that for the embedding equation (5.1), we have

uσ=== ubuD (5.2)

x s y b

k u n u

k b Fig. 5.1 General model of SS-based watermarking

PRNPRN

Correlation detector

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The channel is modeled as an additive noise channel y = s + n, and the

watermark extraction is usually performed by the calculation of the normalized

sufficient statistics r:

nc++=⟩++⟨

=⟩⟨⟩⟨

= xu

c b un,xbuuu,uy,

(5.3)

and estimating the embedded bit as sign(r) b̂ = , where uun,

nuux,

x c and c ⟩⟨⟩⟨ == .

Simple statistical models for the host audio x and the attack noise n are assumed.

Namely, both sequences are modeled as uncorrelated white Gaussian random

processes. Then, it is easy to show that the sufficient statistics r is also

Gaussian )σN(0,n and)σN(0,x 2ni

2xi ∼∼ , ~ Sian variable, i.e.:

[ ] 2u

2n

2x2

rr 2rr Nσ

σσσ b, rEm),σ,N(mr

+===∼ (5.4)

Let us consider the case when b is equal to 1. In this case error occurs when r <

0 and therefore, the error probability p is given by

{ } ( ) ⎟⎟⎠

⎞⎜⎜⎝

+=⎟⎟

⎞⎜⎜⎝

⎛==<= 2

n2x

2u

r

rr σσ2

Nσerfc21

2σmerfc

211 b0 b̂ P p (5.5)

Where erfc(.) is complementary error function. The equal error probability is

obtained under the assumption that b = -1. A plot of that probability as a function of

the SNR (in this case defined as (mr/σr) is given in Figure 5.2. For example, from

Figure 5.2, it can be seen that if an error probability lower than 10-3 is needed, SNR

becomes:

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Fig. 5.2. Error probability as a function of the SNR.

)(93/m 222r nxur N σσσσ +>⇒> (5.6)

or more generally, to achieve an error probability p we need:

( )( ) )(2 22212nxu perfcN σσσ +> − (5.7)

Malvar et al [15] shows that one can make a trade-off between the lengths of

the chip sequence N with the energy of the sequence σu2. It lets us to simply compute

either N or σu2, given the other variables involved.

5.2. Adaptive SNR based non blind watermarking technique in

wavelet domain

The SNR based watermarking technique suggested by Li et al [29] is

implemented in wavelet domain. This non blind technique is implemented here in

order to compare our results with the scheme proposed by Li et al [29]. This method

is non blind means it requires the original host signal to recover the watermark. This

section describes the adaptive watermarking technique based on SNR. The goal of the

watermarking technique is to embed (add) the cover signal (watermark) in to the host

multimedia signal. The embedding process modifies the host signal by mixing

function. In most of the watermarking technique mixing function is the addition of

original host signal and scaled cover signal. Mathematically the function is defined as

)( += ιwα x(i) y(i) (5.6)

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Where y(i) is the watermarked signal, x(i) is the original host signal, w(i) the

cover signal and α scaling parameter used as the secret key. The parameter α plays

an important role in embedding and detection process of watermarking. In embedding

process α is selected in such way that the watermark remains imperceptible

(inaudible). The watermark detection process is exactly the reverse process. Without

the knowledge of parameter α it is not possible to detect the watermark. Any

statistical analysis should not leave any possibility of detecting the cover signal or the

parameter α. The imperceptibility of the watermarking procedure is computed by

computing the SNR between the original host signal and the watermarked signal. The

SNR can be computed as

−=

i

i

iyix

ixSNR

2

2

))()((

)(log10 (5.7)

The method proposed by Wu et al [29] is implemented in wavelet domain. To

embed the watermark in to host audio signal the host audio signal is divided in to

smaller segments of size N. One bit of binary watermark is then added in to one

segment of host audio signal.

( )

⎪⎩

⎪⎨

⎧ ⋅α+=

otherwise)i(k3A

)i(Amaxis)i(k3Aif)k(w)k()i(k

3A)i(k

3B3k

(5.8)

Where )i(A3k is cd3 coefficient of 3rd level DWT of )(ixk and )(ixk is ith

sample of kth segment of host audio signal. )i(B3k is watermarked cd3 coefficient.

)k(α is scaling parameter used to scale the watermark bit )k(w so that the added

watermark is inaudible in the audio. The SNR between the original coefficient

)i(A3k and the modified coefficient )i(B3

k can be represented by

( )∑ −

∑=

i

23k

3k

i

23k

)i(B)i(A

)i(Alog10SNR (5.9)

Solving formulas (5.8) and (5.9) for scaling parameter α(k) to obtain

( )( )( )

)k(w

10.iA)k( i

10SNR23k∑

(5.10)

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The value of )k(w2 is =1 because w(k) is either +1 or -1. For the threshold

value of SNR the scaling parameter )k(α required in formula (5.8) can be computed

using this equation and used to embed the watermark. According to IFPI

(International Federation of the Phonographic Industry) [28] the imperceptible audio

watermark scheme requires at least 20 db SNR value between the watermarked signal

and the host signal, so the threshold value of SNR should be considered greater than

20 for solving the formula (5.10).

The watermark embedding process is shown in Fig 5.3. The host audio signal

x(n) is divided into subsections of size N=2i, each subsection is decomposed into 3-

level Discrete wavelet transform by means of Harr wavelet transform. The Haar

wavelet is used in the implementation because Haar wavelet gives perfect

reconstructability which is essential feature for this application. The authors in [29]

used db4 wavelet as for Daubechieves wavelets compact signal property is not

maintained if we increase the number of points in the wavelet, as well as its

reconstructability is not mentioned. So we used Haar wavelet in our implementation.

The detailed coefficients (cd3) of host audio signal are selected for embedding the

watermark in low frequency part of highest energy of audio signal.

The M1 × M2 binary image is considered as a watermark. Before embedding

this 2-D watermark into host audio it is converted into its 1-D bipolar equivalent w(k)

{1,-1}. Then w(k) is scaled by the parameter α(k) and One bit of w(k) is embedded

into a single subsection of host audio signal.

To find the imperceptibility of the system SNR between the host audio signal

and watermarked signal is computed by the formula (5.7). It is observed that the SNR

obtained using this technique is 43.77 db. The watermarked signal and host audio

signal is played in front of the ten personalities having the knowledge of music. It is

observed by the all five personalities that there is no perceptual difference between

the host audio signal and the watermarked audio signal.

In order to proceed to detect the watermark the knowledge of scaling

parameter α is very important without the knowledge of α(k) it is not possible to

detect the watermark. Scaling parameter α(k) is computed during the watermark

embedding process using the formula (5.10). The original signal x(n) is required to

compute the value of α(k) in order to recover the watermark properly. The formula

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required to detect the watermark is exactly the reverse of formula (5.8). To detect the

watermark the x(n) and y(n) are divided into subsections of size N where N =2i.

Where the value of i is same as used during the watermark embedding process. Each

subsection is decomposed using 3-level wavelet transform. The watermark is then

recovered as shown in Fig.5.4.

Fig.5.3. Watermark Embedding Process.

x(n) y(n) α Binary image

Fig.5.4 Watermark extraction.

Watermarked audio y(n)

Input host audio x(n)

Binary Watermark Image

Subsection x(n) with size=2k

Subsection x(n) with size=2k

DWT DWT

Watermark detection

Recovery of bipolar watermark by threshold comparison.

1-D to 2-D conversion

Find start of utterance

Divide host into segment of size N

Take third level DWT of each segment

Dimension mapping

Bipolar conversion

Computation of scaling parameter α(k) for each segment

Scaled watermark

Take IDWT of each segment

Concatenate the segments

Original watermark Computatio

n of α(k)

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After detecting the presence of watermark each value is compared against the

threshold value T to recover the bipolar watermark. If the value of the sample is

greater than the threshold then watermark bit is recovered as 1 otherwise if value of

sample is less than threshold the watermark bit is recovered as 0.The recovered

watermark is 1-D signal so it is converted into the required 2-D form of size M1× M2

to recover the binary image used as watermark. In order to test the similarity between

the recovered watermark and the original watermark the correlation coefficient

between original watermark and recovered watermark is computed.

The original host audio signal and the watermarked audio signal are shown in

fig 5.5. Fig 5.5.a represents the original host audio signal and the fig 5.5.b. represents

the watermarked signal. It can be observed that there is no perceptual difference

between the two. The two audio signals are played in front of the ten personalities to

test the audible difference between them and found that there is no audible difference

between the original host audio signal and the watermarked signal. The SNR is

computed by using formula (5.7) and is 43.77 db. To find the similarity between the

original watermark and the recovered watermark the correlation coefficient is

computed and is 0.9917.

Fig 5.5. a) Original host audio signal b) Watermarked audio signal.

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a) b) Fig 5.6 a) Original Watermark b) Recovered watermark.

To test the robustness of the technique the watermarked audio signal is passed

through different signal processing attacks. The Gold wave software is used for

editing the watermarked signal using different signal processing tools available in the

software. The experimental results show that the SNR between the original signal and

processed watermarked signal is above 25db in all cases. The watermark is

successfully recovered after all processing and the correlation coefficient is also

above the 0.88 which is shown in the table 5.1. To compute the error obtained in

recovering the watermark is also computed and is presented in the table 5.1.

Low pass filtering:

The watermarked signal is passed through the low pass filter with cutoff

frequency 11025 Hz. The watermark is recovered from the filtered signal. The results

of the signal processing attacks are shown in table 5.1.

Resampling:

The watermarked audio signal with original sampling rate 44100Hz has been down

sampled to 22050Hz and upsampled back to 44100Hz. Then the watermarked signal

is upsampled with sampling rate equal to 88200 Hz and downsampled back to 44100

Hz.

MP3 Compression:

The watermarked audio signal is mp3 compressed at 64 kbps and is observed

that the watermark resist the mp3 compression.

Requantization:

The 8-bit watermarked signal is requantized to 16 bit/sample and back to 8

bit/sample.

Cropping:

10% signal of each segment of the watermarked signal is cropped and

watermark is recovered from it.

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Noise addition:

White noise with 15 of the power audio signal is added into the watermarked

audio signal.

Echo addition:

Echo signal of a delay of 100ms and a volume control of 50% is added to the

original audio signal.

Equalization:

A 10-band equalizer with the characteristics listed below is used. Frequency Hz: 31,

62, 125, 250, 500, 1000, 2000, 4000, 8000, 16000. Gain [db]: -6, +6, -6, +6, -6, +6, -

6, +6, -6, +6.

(a) (b) (c) (d) (e) (f)

(g) (h) (i)

Fig. 5.7 Results for SNR based scheme for non blind detection. a)without attack b) down

sampling c)up sampling d)MP3 compression e)requantization f)cropping g)low pass filtered with fc= 11025 h) Lpfiltered fc=22050 i)time warping

Table 5.1 Experimental Results against Signal Processing Attacks for non blind technique (MP3

song)

Sr. No

Attack SNR Correlation Coefficient

BER

Without attack 43.77 1 0 1 Down Sampling 28.56 0.9889 0.0039 2 Up Sampling 34.26 0.9917 0.0027 3 LP-filtering 19.73 0.9917 0.0027 4 Requantization 39 0.9917 0.0027 5 Cropping 37.29 0.9889 0.0039 6 Mp3

compression 41.25 0.9917 0.0027

7 Noise addition 34.26 0.9889 0.0039 9 Echo addition 37.4420 0.9889 0.0039 10 Equalization 38.518 0.9889 0.0039

The experimental results of the developed scheme are shown in table 5.1. The

SNR between the watermarked signal and the original host audio signal is computed

and entered in the 2nd column of the table. Correlation coefficient between the original

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watermark and the recovered watermark is entered in the 3rd column of the table. The

BER between the original watermark and the recovered watermark is entered in the

4th column of the table. The experimental results show that the developed technique

recovers the watermark successfully after all kinds of signal processing attacks

mentioned in the table. It is observed that the SNR between the original signal and the

distorted signal is within the acceptable limit of 20db and is above this limit except

for the LP-filtering and down sampling.

The major draw back of this scheme is that it requires the original host signal

and the original watermark signal in order to recover the watermark and to provide the

proof of ownership. To overcome these drawbacks we modified this scheme and

proposed the SNR based blind watermarking scheme.

5.3. Proposed adaptive SNR based blind watermarking using

DWT /Lifting wavelet transform:

This section highlights on the proposed adaptive SNR based blind

watermarking scheme implemented in DWT and LWT domain. In this scheme we

have made an attempt to recover the watermark signal without the help of original

audio signal. We are also successful in recovering the watermark without using the

scaling parameter. The scaling parameter used to embed the watermark is varied

adaptively for each segment in order to achieve the imperceptibility and to take the

advantage of insensitivity of HAS to smaller variations in transform domain. To

provide the security to the system the secret key is used and the secret key is a PNR

sequence generated by any cryptographic method. Without the knowledge of this

secret key it is not possible to recover the watermark hence it is required to generate

with secret initial seed. This idea of generating the pseudo random number from

initial seed is borrowed from communications.

Our aim in developing this scheme is to devise a system which embeds the

watermark using spread spectrum method and does not require the original audio

signal to recover the embedded watermark. The embedding process modifies the host

signal x by mixing function f(.). f(x, w) performs the mixing of x (host multimedia

signal) and w(watermark signal) with the help of secrete key k. In most of the

watermarking technique [12, 19] mixing function is the addition of original host

signal and scaled watermark signal mathematically represented by formula (5.1). The

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blind watermarking technique proposed here embeds the watermark in wavelet

domain using the additive formula similar to formula (5.1).

)i(r)k(w)k()i(A)i(B 3

k3k ⋅⋅α+= (5.11)

Where r(i) is the permuted pseudorandom binary signal with zero mean which

is the secret key of the owner, α(k) and w(k) are the scaling parameter and watermark

bit to be embedded in kth segment respectively. )i(A3k is cd3 coefficient of 3rd level

DWT/LWT of )(ixk where )(ixk is ith sample of kth segment of host audio signal.

)i(B3k is watermarked cd3 coefficient. Solving the equation (5.11) and equation (5.9)

for α(k).

( )

( ) ( )( )∑∀

∑∀ ⎟

⎟⎠

⎞⎜⎜⎝

⎛ −

=

ik2wi2r

i10SNR

10i2Aα(k)

3k

(5.12)

w(k) is the bipolar watermark which is either 1 or -1 therefore the value of

w2(k) is 1. The r(i) is a pseudorandom binary signal with zero mean having value of 1

or -1 so Σ r2(i) = N. The equation (5.12) reduces to

( )

Ni

10SNR

10 i2Aα(k)

3k∑

∀ ⎟⎟⎠

⎞⎜⎜⎝

⎛ −×

= (5.13)

The audio watermarking scheme developed here computes the value of α(k)

for every subsection of host audio signal using the formula (5.13) which is later on

used to embed the watermark bit in each segment of the host audio using the formula

(5.11).

5.3.1. Watermark embedding:

To embed the watermark signal in to the host audio signal present scheme uses

the additive watermarking method. The host audio signal is divided into the segments

of size N= 256, 512, 1024… etc.

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Xk(i) = X (k . N + i) i=0,1,2…..N-1, K=0,1,2….. (5.14)

N=256, 1024

Where X(i) represent the original host audio signal and Xk(i) represent the kth

segment of the host audio. Then each Xk(i) is decomposed to Lth level wavelet

transform. The scheme is implemented using Discrete Wavelet Transform (DWT) and

Lifting Wavelet transform (LWT). To embed the watermark into low frequency part

of the highest energy of audio signal by taking advantage of frequency mask effect of

HAS [29] the 3rd level detail part of coefficients is selected. After selecting the

watermark embedding formula the 3rd approximate coefficients are modified as

i)α(k)w(k)r((i)A(i)A 3

k3k += (5.15)

Where (i)A3

k are 3rd level approximate coefficients. Where r(i) is the permuted

pseudorandom binary signal with zero mean which is the secret key of the owner, α(k)

and w(k) are the scaling parameter and watermark bit to be embedded in kth segment

respectively. The block schematic of the proposed scheme is shown in the fig. 5.8.

The SNR between the original signal and the watermarked signal can be computed by

using formula (5.7) to measure the imperceptibility of the watermarked signal.

In this method we compute the scaling parameter α(k) for every segment of

host audio signal and then embed according to the rules for bit one or bit 0 of

watermark signal. This variation of α(k) for every segment takes in to account the

feature of the host audio in that segment and then compute the value of α(k), which is

similar to finding the scaling parameter taking into consideration the perceptual

transparency of the host audio.

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Fig.5.8. Watermark Embedding Process for proposed adaptive SNR based blind technique in DWT/LWT domain.

5.3.2. Watermark extraction

The block schematic of watermark extraction for the proposed scheme is

shown in fig. 5.9. To detect the watermark from the embedded signal the 3rd level

DWT/LWT of the watermarked signal is computed. Then the coefficients (i)D3k′ are

modified by the same pseudorandom signal r(i) used while embedding the watermark.

r(i) (i)Ds(i) 3k′= (5.16)

Where )i(r)k(w)k(D(i)D 3k

3k α+=′ and s(i) wavelet coefficients modified by r(i).

i))r(i)α(k)w(k)r((i)(Ds(i) 3

k +=∴ (5.17) ∑+∑=∑∴

∀∀∀ i2

i

3k

i(i)α(k)w(k)rr(i)(i)Ds(i) (5.18)

The expected value of the first term in the equation (5.18) i.e. ∑

∀i

Lk (i)r(i)D is

approximately equal to zero and the value of ∑∀i

2 (i)r is N (α(k) w(k) are independent

of summation variation i ) therefore the value of the equation is approximately equal

Initial Seed

Watermarked audio y(n)

Input host audio x(n)

Binary Watermark Image

Find start of utterance

Divide host into segment of size N

Dimension mapping

Bipolar conversion

Computation of scaling parameter α(k) for each segment

Scaled watermark

Concatenate the segments

PN sequence generation

Take third level DWT/LWT of each segment

Take IDWT/ILWT of each segment

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to Nα(k)w(k), where N is the size of the segment. If the value of ∑∀i

s(i) is greater than

threshold the watermark bit one will be recovered and if the value of summation is

less than the threshold the watermark bit 0 will be recovered.

Fig.5.9 Watermark extraction for adaptive SNR based blind technique in DWT/LWT domain.

5.3.3. Experimental results

This part of the section highlights on the experimental results obtained during

the implementation of this scheme. The watermark signal is embedded in discrete

wavelet domain and lifting wavelet domain. The host audio signals used in section 5.2

are used for the experimentation of this scheme. Three level wavelet decomposition of

each segment of the host audio is performed. The subjective analysis test is conducted

in a similar manner as explained in previous section of this chapter. It is observed by

all five personalities that there is no audible difference between the original host audio

signal and the watermarked signal. The imperceptibility of watermarked signal is

measured by computing the signal to noise ratio (SNR) between the original signal

and the watermarked signal. The observed SNR is shown in table 5.2.a. To measure

the similarity between the original watermark and recovered watermark the

correlation coefficient is computed and is presented in table 5.2.b. We have also

computed the BER between the original watermark and recovered watermark and is

entered in the table 5.2. To check the robustness of the scheme the watermarked

signal is passed through different signal processing techniques and then watermark is

recovered. The watermarked signal is checked against signal processing attacks

Recovered Watermark

Watermarked audio y(n)

Find start of utterance

Divide host into segment of size N

Take third level DWT/LWT of each segment

Initial Seed PN sequence generation

Threshold comparison and bit recovery from each segment

Dimension mapping

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described in section 5.1. The results are represented in the table 5.2 are obtained for

blind technique in DWT and LWT domain. These results are obtained by considering

SNR=0.3 in equation (5.13) and length of segment=256. The original watermark

embedded in the signal is shown in fig 5.10 whereas the recovered watermark after

different signal processing attack is shown in fig. 5.11 (DWT based blind technique)

and fig. 5.12 (LWT based blind detection technique).

(a) (b) (c)

(d) (e) (f)

(g) (h)

Fig.5.11 Results for SNR based scheme for blind detection in DWT domain a) without attack b) down sampling c)up sampling d)mp3 compression e)requantization f)cropping g) Lpfiltered

h)time warping

(a) (b) (c) (d) (e) (f)

(g) (h)

Fig.5.12 Results for SNR based scheme for blind detection in LWT domain a) without attack b) downsampling c)upsampling d)mp3 compression e)requantization f)cropping g) Lpfiltered

i)timewarping

Fig. 5.10 Original Watermark

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Table 5.2. Results after signal processing attack a) SNR between the original audio signal and watermarked audio signal after various

attacks.

b) Correlation coefficient between original watermark and recovered watermark

From the results presented in table 5.2 it is clear that the blind techniques

implemented in discrete wavelet domain and lifting wavelet domain are robust against

the various signal processing attacks such as resampling, requantization,

MP3compression, L.P. filtering and cropping. From the comparison of the results

presented in table5.2 and table 5.1 one can say that non blind technique has better

SNR compared to the blind detection scheme whereas it requires the original audio

signal to recover the watermark. The correlation coefficient test between the original

watermark and recovered watermark indicate that DWT/LWT based blind detection

techniques are robust with little difference in the recovery. Hence forth we have

concentrated on DWT domain only and not considered the LWT as it produces

SNR based scheme for blind detection in DWT domain

SNR based scheme for blind detection in LWT

SNR SNR Without attack 18.6194 18.3601 Downsampled 18.3863 18.2978 Upsampled 18.3863 18.2978 Mp3compressed 18.3863 18.2978Requantization 18.3863 18.2978 Cropping 18.4208 18.3316 Lpfiltered 16.2305 16.2178 Time warping 18.3863 18.2978 Echo addition 17.4780 17.1564 Equalization 18.3863 18.2978

SNR based scheme for blind detection in DWT

SNR based scheme for blind detection in LWT

Correlationcoefficient

BER Correlation coefficient

BER

Without attack 1 0 1 0 Downsampled 0.9972 0.0027 0.9862 0.0039 Upsampled 0.9972 0.0027 0.9862 0.0039 Mp3compressed 0.9972 0.0027 0.9862 0.0039 Requantization 0.9972 0.0027 0.9862 0.0039 Cropping 0.9972 0.0027 0.9862 0.0039 Lpfiltered 0.9972 0.0027 0.9531 0.0089 Time warping 0.9972 0.0027 0.9862 0.0039 Echo addition 0.9972 0.0027 0.9862 0.0039 Equalization 0.9972 0.0027 0.9862 0.0039

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similar results. The observed SNR between the original host signal and the

watermarked signal even after signal processing attacks is less than 20 during the

experimentation results obtained in table 5.2. This is because of the parameter α(k)

calculated from (5.13) by considering SNR=30 the lowest threshold requirement. As

explained in the next subsection these imperceptibility results can be improved with

increase in SNR consideration while computing α(k).

5.3.4. Selection criteria for value of SNR in computing α(k) and selection criteria for segment length N

To increase the imperceptibility of the watermarked audio signal, we tried

varying the SNR requirement for different values of parameter α(k). We varied the

SNR from 30 to 80 and observed the results. We have also varied the segment length

and observed the performance of the system. The observed results of these variations

are shown in table 5.3.

Table 5.3 Relation between segment length, parameter α(k), observed SNR and correlation

coefficient Sr.No.

Assumed SNR between wavelet coefficients in (5.11)

for calculation of α(k)

Segment length

Observed SNR between host audio and watermarked

audio

Corr. Coeff.

01 30 512 16.3843 1 256 18.6826 1 128 21.5655 1

02 40 512 18.1608 1 256 19.6560 1 128 22.5764 1

03 50 512 19.0204 1 256 20.6227 1 128 24.5634 1

04 60 512 19.3452 1 256 21.6064 1 128 28.9675 1

05 70 512 19.6852 1 256 22.5887 0.9917 128 25.1256 0.9863

0.6 80 512 20.7438 0.9917 256 23.0733 0.9863, 128 27.7826 0.9684

From the table 5.3 it is clear that SNR can be increased by two ways i) By

increasing the SNR value in expression (5.13) ii) By reducing the segment length. For

the first case increase in SNR increases the imperceptibility of the watermarked signal

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whereas reduces the robustness. For the second case i.e reduction in segment length

improves the SNR but reduce the robustness. The optimized results can be obtained

considering all the contradictory requirements of watermarking by selecting segment

length 256 and SNR in the range 40 – 60. Therefore while obtaining the comparison

results of implementations of spread spectrum watermarking the SNR is selected as

60 and segment length is considered as 256.

5.4. Proposed Adaptive SNR based spread spectrum scheme in

DWT-DCT domain:

This section highlights on the proposed SNR based spread spectrum technique

of audio watermarking which adaptively select the embedding strength to embed the

watermark. This scheme embeds the watermark by first taking the 3rd level DWT of

the host audio signal and then computing the DCT of the low pass filtered DWT

coefficients. DWT-DCT transform is used to embed the watermark in low-middle

frequency components of host audio in order to increase the imperceptibility of the

watermarking scheme as compared to the scheme implemented in previous section.

Using DCT of the Ca3 coefficients, we can exactly track the required frequency band

to embed the watermark for better imperceptibility. The spread spectrum

watermarking techniques [2, 3, 9] modifies the host multimedia signal using the

mathematical function defined by (5.1)

The scheme proposed here first divide the host audio signal into smaller

segments of size N. Then it computes 3 rd level DWT of each segment. Then the ca3

(Low pass filtered coefficients) coefficients are selected for embedding the watermark

and take the DCT coefficients of ca3 (3rd level DWT coefficients of host audio)

coefficients to add the watermark bit using the formula (5.19) described below.

)k(w)i(r)k()i(A)i(A 3k

3k ⋅⋅α+=′ (5.19)

where r(i) is the permuted pseudorandom binary signal of length L with zero

mean which is the secret key of the owner. The length L is equal to the length of the

3rd level DWT-DCT coefficients. α(k) is the adaptive scaling parameter computed for

kth segment in which watermark is required to be added and w(k) the watermark bit to

be embedded in kth segment. )i(A3k′ is the DWT-DCT coefficient of the watermarked

signal for kth segment and )i(A3k is the DWT-DCT coefficient of the original host

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signal for kth segment. Solving the equation (5.19) and equation (5.9) for α(k).

( )

( ) ( )( )∑∀

⎟⎠⎞

⎜⎝⎛∑

ikwir

10)i(A)k( 22

)10/SNR(

i

23k

(5.20)

w(k) is the bipolar watermark which is either 1 or -1 therefore the value of

w2(k) is 1. The r(i) is a pseudorandom binary signal with zero mean with value of 1 or

-1 so Σ r2(i) = L. The equation (5.13) reduces to

L

10)i(A)k(

)10/SNR(

i

23k

∀⎟⎠⎞

⎜⎝⎛∑

=α (5.21)

The audio watermarking scheme developed here computes the value of α(k)

for every subsection of host audio signal using the formula (5.21) which is later on

used to embed the watermark bit in each segment of the host audio using the formula

(5.19). The value of SNR to be used in computing α(k) is required to be selected by

user and should be in the range 40 – 60 dB as proved in section 5.3.4. Even though

the selected value of SNR is fixed is same for all segments, ∑∀i

23k )i(A will change for

each segment and hence α(k) will change for each segment.

5.4.1. Watermark embedding

The process of the proposed watermark embedding is shown in the block

schematic of fig. 5.13. To embed the watermark signal in to the host audio signal

present scheme uses the additive watermarking method. The host audio signal is

divided into the segments of size N= 256, 512, 1024… etc

).()( iNkxixk += i=0,1,2…….N-1, k= 0,1,2……. (5.22) Where N=256,1024 X(i) represent the original host audio signal and Xk(i)

represent the kth segment of the host audio. Then each Xk(i) is decomposed to 3rd

level wavelet transform. To embed the watermark into low frequency part of the

highest energy of audio signal the 3rd level approximate part of coefficients is selected

and DCT of it is obtained. Then watermark is embedded by selecting the low

frequency DCT coefficients. The DWT-DCT coefficients are modified as

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)i(r)k(w)k()i(A)i(A 3k

3k ⋅⋅α+=′ (5.23)

Where )i(A3

k is DCT coefficient of ca3 coefficient of 3rd level DWT of )(ixk

where r(i) is the permuted pseudorandom binary signal with zero mean which is the

secret key of the owner, α(k)and w(k) are the scaling parameter and watermark bit to

be embedded in kth segment respectively, )i(A3k′ are the watermarked coefficients.

The SNR between the original audio signal and the watermarked audio signal can be

computed by using formula (5.7) to measure the imperceptibility of the watermarked

signal.

Fig.5.13. Watermark Embedding Process for proposed adaptive SNR based blind technique in DWT-DCT domain.

5.4.2. Watermark extraction

The watermark extraction procedure of the proposed scheme is depicted in the

fig 5.14. To extract the watermark from the embedded signal the 3rd level DWT of the

watermarked signal is computed. Then the DCT of coefficients )i(A3k′ are modified

by the same pseudorandom signal r(i) used while embedding the watermark.

)i(r)i(A)i(s 3k ⋅′= (5.24)

Initial Seed

Watermarked audio y(n)

Input host audio x(n)

Binary Watermark Image

Find start of utterance

Divide host into segment of size N

Dimension mapping

Bipolar conversion

Computation of scaling parameter α(k) for each segment

Scaled watermark

Concatenate the segments

PN sequence generation

Take third level DWT of each segment

Take IDCT and then IDWT of each segment

DCT transform the approximate coeff. of DWT

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Where )i(r)k(w)k()i(A)i(A 3k

3k ⋅⋅α+=′ and s(i) low frequency DCT coefficients

modified by r(i).

)i(r))i(r)k(w)k()i(A()i(s 3k ⋅⋅α+=∴ (5.25)

∑ ⋅⋅α+=∑∴∀∀ i

3k

i)i(r))i(r)k(w)k()i(A()i(s

∑ ∑ ⋅⋅α+=∑∴∀ ∀∀ i i

23k

i)i(r)k(w)k()i(r)i(A)i(s (5.26)

The expected value of the first term in the equation (5.26) i.e. ∑∀i

3k )i(r)i(A is

approximately equal to zero and the value of ∑∀i

2 )i(r is L (α(k) ⋅ w(k) are independent

of variation i ) therefore the value of the equation is approximately equal to L

α(k)w(k), where L is the length of the coefficients. If the value of ∑∀i

)i(s is greater

than threshold the watermark bit one will be recovered and if the value of summation

is less than the threshold the watermark bit 0 will be recovered.

Fig.5.14 Watermark extraction for adaptive SNR based blind technique in DWT-DCT domain.

5.4.3. Experimental Results

This section highlights on the experimental results obtained during the

implementation of the scheme. The results are obtained with the same set of audio

signals used in previous sections. Three level wavelet decomposition of each segment

of the host audio is performed. Then the low frequency coefficients (ca3) are selected

to embed the watermark. The watermark is embedded by modifying the low

frequency DCT coefficients of the ca3 coefficients as per equation (5.19). The α(k)

Recovered Watermark

Watermarked audio y(n)

Find start of utterance

Divide host into segment of size N

Take third level DWT-DCT of each segment

Initial Seed PN sequence generation

Threshold comparison and bit recovery from each segment

Dimension mapping

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used in the equation (5.19) to add the watermark bit w(k) is computed using

equation(5.21) and the value of SNR considered in the equation(5.21) is 30 dB to

obtain the results for minimum threshold.

The subjective analysis test proved that there is no perceptible difference

between the host audio and watermarked audio. The signal to noise ratio (SNR)

between the host audio signal and watermarked audio signal is computed using

equation (5.9) and is shown in table 5.4. The BER between the original watermark

and recovered watermark is computed and is presented in table 5.4.

To check the robustness of the scheme the watermarked signal is passed

through different signal processing technique and then watermark is recovered. The

results are represented in the table 5.4. The original watermark embedded in the signal

is shown in fig 5.15 a, whereas the recovered watermark after different signal

processing attack is shown in fig 5.15 b - fig 5.15 h.

Table 5.4 SNR between the original audio signal and watermarked audio signal, BER of

recovered watermark

a) Without attack b) Down Sampling c) Upsample

d)Mp3compress e) Requantized f) Cropped

g) Time warping h) Lpfiltered22

Fig 5.15 Results for SNR based scheme for blind detection in DWT-DCT domain

SNR BER Without attack 31.3456 0 Downsampled 18.4872 0.0031 Upsampled 18.5820 0.0303 Mp3compressed 31.3456 0 Requantization 31.3456 0Cropping 25.4691 0.0162 Lpfiltered fc=22050 17.8538 0 Time warping -10% 16.6834 0.0137 Echo addition 31.3456 0 Equalization 31.3456 0

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From the results presented in table 5.4 it is clear that the technique

implemented here is robust against the various signal processing attacks such as

resampling, requantization, mp3compression and cropping. It is also observed that the

technique is robust against LP filtering with cutoff frequency of 22 KHz. From the

results presented in table 5.4 it is clear that SNR between the original audio and

watermarked audio after different signal processing attacks satisfies the audio

watermarking requirements provided by IFPI [28]. The BER (bit error rate) test

between the original watermark and recovered watermark indicate that the technique

is able to recover the watermark after different signal processing attacks.

The algorithm is tested for 1000 different keys and the PDF (probability

density function) of SNR is shown in fig 5.16. The PDF of SNR in fig 5.16 computed

for 1000 different keys indicate that the SNR between original signal and

watermarked signal is always greater than 28.67. That is the performance of the

proposed algorithm is very good. It always provides better imperceptibility between

the original signal and watermarked signal and the imperceptibility is not dependent

on the key used to embed the watermark. Watermark cannot be recovered without the

knowledge of the key used.

Fig 5.16 PDF of SNR

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5.5 Comparison chart of spread spectrum audio watermarking technique

implemented in this chapter.

The results of all the SNR based schemes implemented in this chapter are

summarized in table 5.5. These results are obtained for the segment length=256 and

SNR=60 required in computation of α(k). It is clear from the table that the schemes

implemented here are robust against the various signal processing attacks with little

variation in the BER. Though the SNR in blind detection using DWT/LWT method is

less compare to the non blind technique, there is no audible difference observed

between the original host audio signal and the watermarked audio signal. By

implementing the method in DWT-DCT domain the imperceptibility is increased. The

watermark is robust except for time warping, cropping and echo addition. To further

improve the robustness against these attacks and improve the detection performance

we propose to implement the technique using cyclic coding.

5.5. Proposed SNR based blind technique using cyclic coding

To improve the detection performance and to make the system intelligent we

propose to encode the watermark using cyclic coder before embedding in to the host

audio signal. The block schematic of this scheme is shown in fig 5.17 the watermark

to be embedded is first encoded by Cyclic encoder and then it is embedded in to the

host signal. At the decoder side the recovered watermark is decoded using cyclic

decoder and then it is recovered. The robustness results obtained using this method is

presented in the table 5.6. From the table it is clear that the detection performance is

significantly increased even in 10% time scaling, echo addition and low pass filtering.

Non blind SNR based scheme

Proposed blind technique in DWT

Proposed blind technique in LWT

Proposed blind technique in DWT-DCT

SNR in db

BER SNR in db

BER SNR in db

BER SNR in db

BER

Without attack 52.2863 0 21.6109 0 21.2621 0 31.7109 0 Downsampled 28.5660 0.0029 21.6109 0 21.2621 0 31.7109 0 Upsampled 52.2859 0.0029 21.6109 0 21.2621 0 31.7109 0 Mp3compressed 52.2859 0.0029 21.6109 0 21.2621 0 31.3456 0 Requantization 28.5660 0.0029 21.6109 0 21.2621 0 31.3456 0 Cropping 28.9923 0.0107 21.6102 0.0068 20.3316 0.0078 31.7056 0.0156 Lpfiltered 20.3328 0.0029 18.8769 0.0009 16.2178 0.0029 27.9929 0 Time warping -10%

11.4711 0.0029 11.1957 0.0078 10.2965 0.0089 20.0535 0.0186

Echo addition 28.2335 0.0127 17.4780 0.0156 17.1564 0.0163 16.9394 0.0049 Equalization 28.2335 0.0029 19.1299 0 18.2978 0 19.1391 0

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Fig 5.17 Improved encoder and decoder for blind watermarking using cyclic coding.

Table 5.6 results obtained for (6,4) cyclic codes

Table 5.7 results obtained for (7, 4) cyclic codes

It is clear from the table 5.6and 5.7 that the encoder and decoder model

proposed using cyclic coding is more robust compare to all the techniques proposed.

SNR BER Without attack 32.2044 0 Downsampled 32.2044 0 Upsampled 32.2044 0 Mp3compressed 32.2044 0 Requantization 32.2044 0 Cropping 30.2458 0.0110 Lpfiltered fc=22050 29.1862 0 Time scaling -10% 21.2377 0.0029 Echo addition 16.9391 0.0039 Equalization 21.0204 0

SNR BER Without attack 28.5381 0 downsampled 28.3982 0 upsampled 28.3982 0 Mp3compressed 28.3982 0 requantization 28.3982 0 cropping 22.3475 0.0094 Lpfiltered fc=22050 26.9378 0 Time scaling -10% 23.5560 0 Time scaling +10% 21.2156 0.0018 Echo addition 16.0376 0.0021 Equalization 20.8502 0

Key Key Attack

Recovered Watermark

Host Audio

Watermark encoder

Watermark

Encoding using cyclic codes

Dimension mapping

Dimension mapping

Watermark Decoder

Encoding using cyclic codes

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With small sacrifice in imperceptibility test the (7, 4) cyclic coder provides the best

robustness test as our main aim is to embed the watermark which sustain all kinds of

attacks and recover the watermark successfully.

5.6. Summary of chapter:

Adaptive SNR based schemes are proposed in this chapter. The main goal of

proposing these methods is to propose the blind watermark techniques which are

robust against various signal processing attacks. From the comparison of the results

presented in table 5.5 one can say that non blind technique has better SNR compared

to the blind detection scheme, but it requires the original audio signal to recover the

watermark. It can also be observed that blind techniques are robust against various

signal processing attacks. The scaling parameter used to embed the watermark is

varied adaptively for each segment in order to achieve the imperceptibility and to take

the advantage of insensitivity of HAS to smaller variations in transform domain. To

provide the security to the system the secret key is used which is a PNR sequence

generated by any cryptographic method. Without the knowledge of this secret key it is

not possible to recover the watermark.

The DWT-DCT based blind detection technique is more robust than

DWT/LWT based blind detection technique and is more imperceptible. The SNR

between original signal and watermarked signal is improved by DWT-DCT

technique. The multiresolution characteristics of discrete wavelet transform (DWT)

and the energy-compression characteristics of discrete cosine transform (DCT) are

combined to improve the transparency of digital watermark [26]. By computing the

DCT of 3-level DWT coefficients we take advantage of low-middle frequency

components to embed the information.

To further improve the detection convergence and to make the system more

secure the idea of encoding the watermark using cyclic coding is proposed, after

encoding the watermark the attacker will not be able to understand the statistical

behavior of the embedded watermark and will also be able to correct the one bit error.

The results obtained for watermark robustness using cyclic encoder are better than the

methods not using any such encoder.