INTELLIGENT ENCODER AND DECODER MODEL

FOR ROBUST AND SECURE AUDIO

WATERMARKING

A synopsis for the research submitted to

Shivaji University, Kolhapur

For the Degree of Doctor

Of Philosophy

In

Electronics Engineering

By

Mrs. Meenakshi Ravindra Patil Dr. J. J. Magdum College of Engineering, Jaysingpur

Email: meenakshirpatil@gmail.com

Under the Guidance of

Dr. Mrs. S. D. Apte Rajarshi Shahu College of Engineering, Pune.

Email: sdapte@rediffmail.com

2009

2

Topic of Research :Intelligent encoder and decoder model for

robust and secure audio watermarking

Name of Research Student : Mrs. Meenakshi Ravindra Patil

Address : ‘Vidyasagar’, Plot No. 7, Majale Housing Society

Near Parvati Housing Society, Agarbhag

Jaysingpur-416101.

Phone: 9422727612

Name of the Research Guide : Dr. Mrs. S.D. Apte.

A7, Whispering Wind, Pashan Baner Link

Road, Baner, Pune-

Phone: 9975185707

Signature of Research Guide Signature of Research Student

Dr. Mrs. S. D. Apte Mrs. Meenakshi Ravindra Patil

.

3

1. INTRODUCTION: Presently there is increased usage of Internet and multimedia information.

The availability of different multimedia editing software and the ease with which

multimedia signal are manipulated have opened many challenges and opportunities

for the researchers. A possibility for unlimited copying without a loss of fidelity

causes a considerable financial loss for copyright holders. The ease of content

modification and a perfect reproduction in digital domain have promoted the

protection of intellectual ownership and the prevention of the unauthorized

tampering of multimedia data to become an important technological and research

issue

Encryption of digital multimedia prevents access of the multimedia content

to an individual without a proper decryption key. Therefore, content providers get

paid for the delivery of perceivable multimedia, and each client that has paid the

royalties must be able to decrypt a received file properly. Once the multimedia has

been decrypted, it can be repeatedly copied and distributed without any obstacles.

Modern software and broadband Internet provides the tools to perform it quickly and

without much effort and deep technical knowledge. It is clear that existing security

protocols for electronic commerce serve to secure only the communication channel

between the content provider and the user and are useless if commodity in

transactions is digitally represented.

With the fast growth of the Internet technology unauthorized copying and

distribution of digital media is become difficult. As a result the music industry

claims a multibillion-dollar annual loss due to piracy, which is likely to increase. A

promising solution to this problem is marking the media signal with a secret, robust

and imperceptible watermark. The invention of digital watermarks not only triggered

worldwide research on digital watermarking technology but also ways of attacking,

counterfeiting and falsifying digital watermarks, with the goal of making illegal

profit or bypassing laws. The research on watermark attacks made it significant to

develop the new methods of digital watermarking which are resilient to attacks.

Digital watermarking has been proposed as a new, alternative method to

enforce the intellectual property rights and protect digital media from tampering. It

involves a process of embedding the digital signature into a host signal in order to

4

"mark" the ownership. The digital signature is called the digital watermark. The

digital watermark contains data that can be used in various applications, including

digital rights management, broadcast monitoring and tamper proofing. The existence

of the watermark is indicated when watermarked media is passed through an

appropriate watermark detector. Watermark Secret Key

Host Watermarked Detected Audio Audio Watermark

Secret Key

Fig1.1 Block diagram of watermarking system

Figure 1.1 gives an overview of the general watermarking system. A

watermark, which usually consists of a binary data sequence, is inserted into the host

signal in the watermark embedder. Thus, a watermark embedder has two inputs; one

is the watermark message accompanied by a secret key and the other is the host

signal (e.g. image, video clip, audio sequence etc.).

Applications for digital watermarking include copyright protection,

authentication copy control, tamper detection and data hiding applications. For the

robust watermarking which include copyright protection where each copy gets a

unique watermark to identify the end user so that tracing is possible for cases of

illegal use and authentication where the watermark can represent a signature and

copy control for digital recording devices.

Perceptibility

Perceptibility can be assessed to different levels of assurance. The problem

here is very similar to the evaluation of compression algorithms. The watermark

could be just slightly perceptible but not perceptible under domestic/ consumer

viewing/listening conditions. Another level is nonperceptibility in comparison with

the original under studio conditions.

The robustness can be assessed by measuring the detection probability of the

mark and the bit error rate for a set of criteria that are relevant for the application

Watermark

Embedder Watermark

Detecter

5

that is considered. The levels of robustness range from no robustness to provable

robustness.

The first levels of robustness can be assessed automatically by applying a

simple benchmark algorithm:

For each medium in a determined set:

1. Embed a random payload with the greatest strength that does not introduce

annoying effects. In other words, embed the mark such that the quality of the output

for a given quality metric is greater than a given minima.

2. Apply a set of given transformations to the marked medium.

For each distorted medium try to extract the watermark and measure the certainty of

extraction. Simple methods may just use a success/failure approach, that is, to

consider the extraction successful if and only if the payload is fully recovered

without error. The measure for the robustness is the certainty of detection or the bit

error rate after extraction.

Capacity

In most applications the capacity will be a fixed constraint of the system so

robustness testing will be done with a random payload of a given size. While

developing a watermarking scheme, however, knowing the tradeoff between the

basic requirements is very useful and graphing with two varying requirements—the

others being fixed—is a simple way to achieve this. This is useful from a user’s

point of view: the performance is fixed (we want only 5% of the bits to be corrupted

so we can use error correction codes to recover all the information we wanted to

hide) and so it helps to define what kind of attacks the scheme will survive if the

user accepts such or such quality degradation.

Speed

Speed is dependent on the type of implementation: software or hardware.

The complexity is an important criteria and some applications impose a limitation on

the maximum number of gates that can be used, the amount of required memory, etc.

6

Statistical Undetectability All methods of steganography and watermarking substitute part of the cover

signal, which has some particular statistical properties, with another signal with

different statistical properties; in fact, embedding processes usually do not pay

attention to the difference in statistical properties between the original cover signal

and the stegosignal. This leads to possible detection attacks.

2. LITERATURE SURVEY Several digital watermarking techniques are proposed which include

watermarking for images, audio and video. The watermarking is primarily

developed for the images [1-11], the research in audio is started later. During the last

decade audio watermarking schemes [12-50] have been applied widely. These

schemes are sophisticated very much in terms of robustness and imperceptibility.

Robustness and imperceptibility are important requirements of watermarking, while

they conflict each other.

2.1 Spread spectrum audio watermarking: Spread spectrum (SS) technique is most popular technique and is used by many

researchers in their implementations [12-18]. Amplitude scaled Spread Spectrum

Sequence is embedded in the host audio signal which can be detected via a

correlation technique. Boney et al [12] proposed a non blind audio watermarking

which generates different watermark for different audio signal and spread in to the

host audio. J. Seok et al [13] and Kirovski et al [14] developed the audio

watermarking schemes that embed the information by modulating it by a direct

sequence spread spectrum method. Malvar et al [15] proposes a new embedding

approach based on traditional SS embedding by slightly modifying it. In this scheme

they introduced two parameters to control the distortion level and control the

removal of carrier distortion on the detection statistics. The content based audio

watermarking technique was implemented by S. Esmaili et al [16] to satisfy and

maximize both imperceptibility and robustness of the watermark. They used short-

time Fourier transform of the original audio signal to estimate a weighted

instantaneous mean frequency of the signal. D. Kirovski et al [17] devised a scheme

7

for robust covert communication over a public audio channel using spread spectrum

by imposing particular structures of watermark patterns and applying nonlinear filter

to reduce carrier noise. Hafiz Malik et al [18] proposed an audio watermarking

method based on frequency selective direct sequence spread spectrum. The method

improves the detection capability, watermarking capacity and robustness to

desynchronization attacks.

2.2 Methods using patchwork algorithm:

The patchwork technique was first presented in 1996 by Bender et al [19] for

embedding watermarks in images. It is a statistical method based on hypothesis

testing and relying on large data sets. The watermark embedding process uses a

pseudorandom process to insert a certain statistic into a host audio data set, which is

extracted with the help of numerical indexes (like the mean value), describing the

specific distribution. The method is usually applied in a transform domain (Fourier,

wavelet, etc.) in order to spread the watermark in time domain and to increase

robustness against signal processing modifications [19].

Multiplicative patchwork scheme developed by Yeo et al [20] provides a

new way of patchwork embedding. Cvejic et al [21] presented a robust audio

watermarking method implemented in wavelet domain which uses the frequency

hopping and patchwork method. R. Wang et al [22] proposed an audio

watermarking scheme which embedded robust and fragile watermark at the same

time in lifting wavelet domain.

2.3 Methods in time domain: There are few algorithms implemented in time domain [23-26]. W. Lie et al

[23] proposed a method of embedding digital watermarks which exploits differential

average-of-absolute-amplitude relations within each group of audio samples to

represent one-bit information. In a method suggested by Bassia et al [24] watermark

embedding depends on the audio signals amplitude and frequency in a way that

minimizes the audibility of the watermark signal. A. N. Lemma et al [25]

investigated an audio watermarking system is referred to as modified audio signal

8

keying (MASK). In MASK, the short-time envelope of the audio signal is modified

in such a way that the change is imperceptible to the human listener.

2.4 Methods implemented in Transform domain:

A blind digital audio watermarking scheme against synchronization attack

using adaptive quantization is proposed by X.Y. Wang et al [26]. Barker code has

better self-relativity, so Huang et al. [27] chooses it as synchronization mark and

embeds it into temporal domain and embeds the watermark information into DCT

domain. S. Wu, J. Hang. et al [28] proposed a self-synchronization algorithm for

audio watermarking to facilitate assured audio data transmission. The

synchronization codes are embedded into audio with the informative data, thus the

embedded data have the self-synchronization ability. To achieve robustness, they

embed the synchronization codes and the hidden informative data into the low

frequency coefficients in DWT (discrete wavelet transform) domain. Li et al [29]

developed the watermarking technique in wavelet domain based on SNR to

determine the scaling parameter required to scale the watermark. The intensity of

embedded watermark can be modified by adaptively adjusting the scaling parameter.

A new watermarking technique capable of embedding multiple watermarks based on

phase modulation is devised by A. Takahashi et al [30]. H. H. Tsai et al [31]

proposed a new intelligent audio watermarking method based on the characteristics

of the HAS and the techniques of neural networks in the DCT domain. C. Xu et al

[32] implemented a method to embed and extract the digital compressed audio. The

watermark is embedded in partially uncompressed domain and the embedding

scheme is high related to audio content. X. Li et al [33] developed a data hiding

scheme for audio signals in cepstrum domain. Cepstrum representation of audio can

be shown to be very robust to a wide range of attacks. S. K. Lee et al [34] insert a

digital watermark into the cepstral components of the audio signal using a technique

analogous to spread spectrum communications, hiding a narrow band signal in a

wideband channel. There are various techniques implemented in wavelet domain

[35-41]. In these papers it is proved that the wavelet domain is the more suitable

domain compare to the other transform domains.

9

2.5 Other recently developed algorithms:

Audio watermarking is usually used as a multimedia copyright protection

tool or as a system that embed metadata in audio signals. In the method suggested by

S. D. Larbi et al [42] watermarking is viewed as a preprocessing step for further

audio processing systems: the watermark signal conveys no information; rather it is

used to modify the nonstationarity. T. Furon et al [43] investigated an asymmetric

watermarking method as an alternative to direct sequence spread spectrum technique

(DSSS) of watermarking.

Conventional watermarking techniques based on echo hiding provide many

benefits, but also have several disadvantages, for example, lenient decoding process,

weakness against multiple encoding attacks etc. B.S. Ko et al [44] improve the weak

points of conventional echo hiding by time-spread echo method. Spreading an echo

in the time domain is achieved by using pseudo-noise (PN) sequences. S. Eerüçük et

al [45] introduced a novel watermark representation for audio watermarking, where

they embed linear chirps as watermark signals. A new adaptive blind digital audio

watermarking algorithm is proposed by X. Wang et al [46] on the basis of support

vector regression (SVR). This algorithm embeds the template information and

watermark signal into the original audio by adaptive quantization according to the

local audio correlation and human auditory masking.

2.6 Audio watermarking techniques against time scale

modification: Mansour and Twefik [47] proposed to embed watermark by changing the

relative length of the middle segment between two successive maximum and

minimum of the smoothed waveform, the performance highly depends on the

selection of the threshold and it is a delicate work to find an appropriate threshold.

Mansour and Twefik [48] proposed another algorithm for embedding data into audio

signals by changing the interval lengths between salient points in the signal. The

extreme point of the wavelet coefficients from the selected envelop is adopted as

salient points. W. Li et al [49] have suggested a novel content – dependent localized

scheme to combat synchronization attacks like random cropping and time-scale

modification. The basic idea is to first select steady high-energy local regions that

10

represent music edges like note attacks, transitions or drum sounds by using

different methods, then embed the watermark in these regions. S. Xiang et al [50]

presented a multibit robust audio watermarking solution by using the insensitivity of

the audio histogram shape and the modified mean to TSM and cropping operations.

A blind digital audio watermarking scheme against synchronization attack using

adaptive mean quantization is developed by X-Y. Wang et al [51].

2.7 Papers studied on performance analysis and evaluation of

watermarking systems: J. D. Gordy et al [52] have presented an algorithm independent set of criteria

for quantitatively comparing the performance of digital watermarking algorithms.

The criteria are simple to test, and may be applied to any type of watermarking

system (audio, image, or video). 1) Bit rate refers to the amount of watermark data

that may be reliably embedded within a host signal per unit of time or space, such as

bits per second or bits per pixel. 2)Perceptual quality refers to the

imperceptibility of embedded watermark data within the host signal. 3)

Computational complexity refers to the processing required to embed watermark

data into a host signal, and / or to extract the data from the signal. 4) Watermarked

digital signals may undergo common signal processing operations such as linear

filtering, sample requantization, D/A and A/D conversion, and lossy

compression. Although these operations may not affect the perceived quality of

the host signal, they may corrupt the watermark data embedded within the

signal. It is important to know, for a given level of host signal distortion,

which watermarking algorithm will produce a more reliable embedding.

The performance of spread-transform dither modulation watermarking in the

presence of two important classes of non additive attacks, such as the gain attack

plus noise addition and the quantization attack are evaluated by F. Bartolini et al

[53]. Many systems have been proposed by different authors but it was difficult to

have idea of their performance and hence to compare them. Then F.A.P. Petitcolas et

al [54] propose a benchmark based on a set of attacks that any system ought to

survive. G.C. Rodriguez et al [55] presented a survey report on audio watermarking

in which watermarking techniques are briefly summarized and analyzed. M. Wu et

11

al [56] points out weaknesses in the watermark techniques and suggest directions for

further improvement. Authors have provided the general framework for analyzing

the robustness and security of audio watermark systems.

2.8 Watermark attacks: Frank Hartung et al [57] have shown that the spread spectrum watermarks

and watermark detectors are vulnerable to a variety of attacks. However with

appropriate modifications to the embedding and extraction methods, methods can be

made much more resistant to variety of such attacks. Martin Kutter et al [58]

suggested the watermark copy attack, which is based on an estimation of the

embedded watermark in the spatial domain through a filtering process. Alexander et

al [59] suggested the watermark template attack. This attack estimates the

corresponding template points in the FFT domain and then removes them using local

interpolation. J. K. Su et al [60] suggested a Channel Model for a Watermark

Attack. Authors have analyzed this attach for images and stated that the attack can

be applied to audio/video watermarking schemes. D. Kirovski et al [61] analyzed the

security of multimedia copyright protection systems that use watermarks by

proposing a new breed of attacks on generic watermarking systems.

My research concentrates on developing an audio watermarking technique to

detection convergence and robustness, improving watermark imperceptiveness. An

attempt is also made to embed the audio data in audio signal during this research.

3. RESEARCH PROBLEM:

The fundamental process in each watermarking system can be modeled as a

form of communication where a message is transmitted from watermark embedder

to the watermark receiver [2]. The process of watermarking is viewed as a

transmission channel through which the watermark message is being sent, with the

host signal being a part of that channel. In Figure 3.1, a general mapping of a

watermarking system into a communications model is given.

12

Fig. 3.1. A watermarking system and an equivalent communications model.

After the watermark is embedded, the watermarked signal is usually distorted

after watermark attacks. The distortions of the watermarked signal are, similarly to

the data communications model, modeled as additive noise.

Thus, the research problem can be divided into three specific sub problems.

They are:

SP1: What is the highest watermark bit rate obtainable, under perceptual

transparency constraint, and how to approach the limit?

SP2: How can the detection performance of a watermarking system be improved

using algorithms based on communications models for that system?

SP3: How can overall robustness to attacks of a watermark system be increased

using an attack characterization at the embedding side?

The division of the research problem into the three sub problems above

defines the following three research hypotheses:

RH1: To obtain a distinctively high watermark data rate, embedding algorithm can

be implemented in a transform domain.

RH2: To improve detection performance, a spread spectrum method can be used. To

make the system intelligent encode the watermark, add synchronization pattern,

remove the isolated pixels at the end of decoder.

RH3:To achieve the robustness of watermarking algorithms, an attack

characterization can be introduced at the embedder. The perceptual transparency

(inaudibility) of a proposed audio watermarking scheme can be confirmed through

Input

Message

wk

n

Noise

cwn

Output

Message

Host

signal Watermark

key

Watermark

key

cw

com Wa

Watermark detector

Watermark

Decoder

Watermark embedder

Watermark

encoder

13

subjective listening tests in a predefined laboratory environment with participation

of a predefined number of people with a different music education and background.

4. HIGH CAPACITY COVERT COMMUNICATION FOR AUDIO Usually embedding is performed in high amplitude portions of the signal

either in time or frequency domain. The technique proposed in this section uses the

audio information as the watermark. The objective of the method implemented here

is to provide an audio watermarking technique that can be used for covert

communication of audio signals. In the proposed technique the input audio is

decomposed using discrete wavelet transform (DWT). The covert message (audio

watermark) is embedded to the detailed coefficient of three level decomposed

original audio signals. The watermark embedding and watermark extraction process

is shown in fig 2 and fig 3 respectively.

The algorithm is applied to a set of audio signals. The signals are mono with

sampling rates 44.1 KHz. In a 45 sec audio file we are able to embed 5 sec of cover

audio signal. To evaluate the watermarked audio quality the listening test [13] is

performed. The subjective listening test results are summarized under Diffgrade.

The Diffgrade is equal to the subjective rating given to the watermarked test item

minus the rating given to the hidden reference: a Diffgrade near 0.00 indicates a high

level of quality.

Audio watermark

Original Signal Watermarked Signal

Watermark extraction

3-level Wavelet

Decomposition

3-level Wavelet

Decomposition

Fig 3 Watermark extraction

Secret key

Audio Watermark

Original Signal

3-level wavelet

decomposition

Watermark

Embedding

Inverse

DWT

Watermarked signal

Fig.2 Watermark embedding

14

Table 1 Subjective listening test for mp3 song

Sr.No Type of attack Diffgrade 1 MPEG-1 Layer-3 128 kbps/mono 0.00 2 MPEG-1 Layer3 64 kbps/mono 0.00 3 Band pass filtering 0.00 4 Echo addition 0.00 5 Equalization 0.00 6 Cropping 0.00 7 Requantization 0.00 8 Down sampling 0.00 9 Up sampling -1 10 Time warping -2 11 Low pass filtering -3

The Diffgrade scale is partitioned into five ranges: imperceptible (> 0.00),

not annoying (0.00 to –1.00), slightly annoying (–1.00 to –2.00), annoying (–2.00 to

–3.00), and very annoying (–3.00 to –4.00).” The number of transparent items

represents the number of incorrectly identified items. Fifteen listeners participated in

the listening test.

To evaluate the performance of the proposed watermarking technique its

robustness is tested. To test the robustness of the scheme the watermarked signal is

passed through different signal processing attacks and then watermark is recovered.

The BER tells the similarity between the original watermark and the

extracted watermark. Experimental results obtained indicate that the present

watermarking technique is robust to common signal processing attacks such as

compression, echo addition and equalization. While performing the robustness test it

is also observed that the watermark will not withstand if the watermarked signal is

passed through low pass filtering of 6 KHz, 10 KHz.

15

Table 2 Robustness test for mp3 song

Sr.No Type of attack BER in % for this scheme

1 MPEG-1 Layer-3 128 kbps/mono

0.0017493

2 MPEG-1 Layer3 64 kbps/mono 0.0017877 3 Band pass filtering 0.0017578 4 Echo addition 0.0017591 5 Equalization 0.0017493 6 Cropping 0.003564 7 Requantization 0.001728 8 Down sampling 0.003564 9 Up sampling 0.042567

10 Time warping 0.037863 11 Low pass filtering 2.1

5. SPREAD SPECTRUM AUDIO WATERMARKING

ALGORITHM 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. Non blind techniques recover the

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

The non blind technique suggested by Li et al [29] is 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. We propose the adaptive blind

watermarking technique based on SNR using DWT and lifting wavelet transform. 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 zero of

watermark signal. 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

16

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 block schematic of watermark embedding

and extraction for the proposed scheme is shown in fig. 4 and fig. 5 respectively.

Fig.4 Watermark Embedding Process for proposed adaptive SNR based blind technique in DWT/LWT domain.

Fig.5 Watermark extraction for adaptive SNR based blind technique in DWT/LWT domain.

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)

Scaled watermark

Concatenate

the segments

PN sequence

generation

Take third level

DWT/LWT of

each segment

Take

IDWT/ILWT of

each segment

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

17

This variation of α(k) for every segment takes in to account the feature of the

host audio in that segment and then computes the value of α(k), which is similar to

finding the scaling parameter taking into consideration the perceptual transparency

of the host audio. 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

( )( )( )

)k(w

10.iA)k( i

10SNR23k∑

=α

−

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

sample of kth segment of host audio signal.

The original signal x(n) is required for computation of the value of α(k) in

order to recover the watermark properly. The formula required to detect the

watermark is exactly the reverse of watermark embedding. 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 that used during the watermark embedding process. Each

subsection is decomposed using 3-level wavelet transform. Experimental results for

this method are summarized in table 3. Table 3 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

18

To improve the imperceptibility between the original audio signal and the

watermarked audio signal the adaptive SNR based scheme using DWT-DCT is

implemented. To make the system intelligent we propose to embed the watermark

using cyclic coding. The scheme which encodes the watermark using cyclic codes

and embeds the watermark is also proposed.

It can 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.

Table 4 Comparison chart of spread spectrum audio watermarking technique

implemented and proposed.

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

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

19

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 due to

this 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.

Fig 4 Improved encoder and decoder for blind watermarking using cyclic coding.

Table 5 results obtained for (6,4) cyclic codes

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

Recovered

Watermark

Host

Audio

Watermark encoder

Watermark

Encoding using

cyclic codes

Dimension

mapping Dimension

mapping

Watermark Decoder

Encoding using

cyclic codes

20

Table 6 results obtained for (7, 4) cyclic codes

It is clear from the table 5 and 6 that the encoder and decoder model proposed using

cyclic coding is more robust as compared to all other techniques proposed.

6. ADAPTIVE WATERMARKING BY GOS MODIFICATION Unlike conventional algorithms, which add modulated and scaled PN-

sequences or other disturbing patterns, no basic form of the watermark signal is set

in advance. Instead, segments of host audio signals are modified into the “1” or “0”

state based on the principle of differential amplitudes. Patchwork algorithms work in

a similar manner. The watermark disturbance is actually strongly related, or similar

to, the host signal itself. It is just like an echo signal with a zero time delay, thus

yielding a high degree of watermark transparency. Besides, the proposed embedding

process is performed subject to the frequency-masking test of watermark signals so

that disturbances of the host audio signal are beyond the ability of human ears to

perceive.

The proposed adaptive audio watermarking technique which modifies the

GOS in DWT domain and embeds and extracts the watermark is shown in fig.6 and

fig 7 respectively. First the complete audio signal is partitioned into consecutive

GOS each containing two non overlapping sections of equal/ unequal length. To

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

21

implement cryptographic method the length of sections can be kept unequal of size

L1, L2 selected randomly for each GOS based on cryptographic key. Hence a GOS

contains L = L1 + L2 samples. One watermark message represents one binary bit of

value 0 or 1, embedded in one GOS. To embed one watermark message the mean of

two sections of each GOS is considered as a feature space and is modified. To

embed the watermark bit 1 if A≤B then no operation is performed and if A>B then

decrease A and increase B by the amount δ so that the condition A≤B satisfies.

To embed the watermark bit 0 if A>B then no operation is performed and if

A<B then increase A and decrease B by the amount δ so that the condition A>B

satisfies.

Fig 6 Block schematic of GOS based watermark embedding in DWT domain.

Fig 7 Block schematic of GOS based watermark extraction in DWT domain.

The results are obtained by implementing the proposed scheme in

DWT/DWTDCT/DCT domains and are verified for the various Indian musical

Watermarked

signal

Input x DWT

transformation

Computation

of mean A &

B

Selection of

length L1 & L2Segmentation

Scaling

parameter K

WDimension

mapping

IDWT

transformation

Concatenation

of Segments

Watermark

insertion

Watermarked

signal DWT

transformation

Computation

of mean A′

Selection of

length L1 & L2Segmentation

Watermark bit

recovery

Recovered

watermark

w

Concatenation

and Dimension

22

signals like Tabla, Harmonium and Indian classical song. The watermarking scheme

that encodes the watermark using cyclic codes and embeds the watermark is also

proposed. It is observed that the SNR between the original watermark and the

recovered watermark is increased compare to the schemes implemented in previous

sections. The scaling parameter used to scale the host signal during the watermark

embedding is adaptive meaning that the parameter is different for each GOS

depending on the audible requirements of each GOS. It is observed that the DWT-

DCT domain is more suitable domain to embed the watermark as it gives us an

advantage of embedding in low-middle frequency regions. The watermark

embedded in low-middle frequency sustains all kind of signal processing attacks.

The comparison of proposed methods with various well known algorithms is

presented in the table 7. TABLE 7 COMPARISON CHART FOR VARIOUS BLIND AUDIO WATERMARKING TECHNIQUES.

NA-Not addressed in literature.

The results of the methods [13,16,46] are taken from the literature. The entry

NA in the table indicates that the particular type of attack not addressed in the

literature. The comparison in table 6.8 is done with respect to the robustness of the

schemes against signal processing attacks. The method [13] has addressed only four

kinds of attacks and their scheme does not sustain even against mp3 compression.

The scheme in [16] has not addressed the attacks like down sampling, cropping, time

Proposed SS based method using cyclic coder

Proposed GOS based method using cyclic coder

Scheme in [13]

Scheme in [16]

Scheme in [46]

SNR in db

BER SNR in db

BER SNR in db

BER SNR in db

BER SNR in db

BER

Without attack 28.5381 0 38.7784 0 NA 0 R

ange

is b

etw

een

-10

to 2

0 0 50.445 0

Downsampled 28.3982 0 38.7784 0 NA NA NA 30.4116 0.0513 Upsampled 28.3982 0 38.7784 0 NA NA 0.04 NA NA Mp3compressed 28.3982 0 38.7784 0 NA 0.91 0.08 50.4542 0 Requantization 28.3982 0 38.7784 0 NA NA 0.08 45.3332 0 Cropping 22.3475 0.0094 38.7784 0.0038 NA NA NA NA NA Lpfiltered 26.9378 0 31.6594 0.0098 NA NA 0.06 36.6381 0.0002 Time warping -10%

23.5560 0 23.6284 0.0107 NA NA NA NA NA

Echo addition 16.0376 0.0021 19.8 0.0089 NA 0 NA NA NA Equalization 20.8502 0 41.7059 0.0098 NA 0 0.13 NA NA Bits embedded In 6sec 32*32 In 6sec 32*32 In 15 sec

128 bits In 5sec 25 bits

In 9.57sec 64*64

23

scaling and echo addition attacks. The BER for this scheme in[16] for other attacks

is always greater than 0.04 and their scheme is not able to recover the watermark

with 0 BER for any of the attacks. The scheme in [46] has reported better SNR and

good BER but their scheme produces poor BER for the down sampling attack. They

have not addressed the major sensitive attacks like upsampling, cropping, time

scaling, echo addition and equalization. Though our proposed methods give less

SNR compare to scheme in [46], it is successful to sustain all kinds of attacks. Our

schemes are more robust compared to the other schemes; also we have taken care to

add the security to the implemented schemes.

7. INTELLIGENT ENCODER DECODER MODELING This section proposes the intelligent encoder decoder model of audio

watermarking based on the implementations of section 5 and section 6. The main

aim of the present work is to make the system robust to all kinds of attacks. In this

chapter we propose the intelligent model of encoding and decoding based on spread

spectrum method (method-1)/GOS modification method (Method-2). To make the

system intelligent we propose to add the following features.

1. Add synchronization patterns to indicate the start of the file from where the

watermark is embedded in the host signal

2. To improve the robustness of the system through diversity, add multiple

watermarks at different locations in a file using time diversity.

3. Encode the watermark to reduce the bit error rate and to improve the

robustness further.

4. Make the system secure by using SS technique/ by selection of unequal

length in GOS modification method.

24

Table 8 Robustness results after attack characterization using time diversity for model-1

Table 9 Robustness results after attack characterization using time diversity for model-2.

The intelligent models we proposed are able to embed the watermark bit

adaptively and perform the blind extraction of watermark successfully. Finally it is

observed that the model proposed in method-1 is more robust compare to the

method-2. The max value of BER for method-1 is 0.0038 for time scale modification

and for method-2 is 0.0078.Hence is applicable in the areas like copy protection,

piracy control, fingerprinting applications where robustness is preferred. The

mehod-2 model is more imperceptible than method-1 and therefore applicable in

applications where high degree of imperceptibility is the requirement. The maximum

value of SNR for method-1 is 27.5764 and for method-2 is 36.4356.

SNR Lowest BER after diversity

BER after removing isolated error pixels

Without attack 27.5764 0 0 downsampled 26.6478 0 0 upsampled 26.8021 0 0 Mp3compressed 26.8021 0 0 requantization 26.8021 0 0 cropping 23.3522 0.0029 0.0009 Lpfiltered fc=22050 24.8021 0 0 Time scaling -10% 20.4113 0.0105 0.0038 Time scaling +10% 20.3347 0.0052 0.0029 Echo addition 20.6543 0.0019 0.0009 Equalization 24.6745 0 0

SNR Lowest BER BER after removing isolated pixels

Without attack 36.4356 0 0 downsampled 35.2543 0.0009 0upsampled 35.2543 0.0009 0 Mp3compressed 35.2543 0.0009 0 requantization 35.2543 0.0009 0 cropping 33.2541 0.0029 0.0009 Lpfiltered 30.8021 0.0009 0 Time scaling -10% 23.6543 0.0185 0.0068 Time scaling +10% 22.4532 0.0193 0.0078 Echo addition 20.7645 0.0124 0.0048 Equalization 24.8437 0.0098 0.0019

25

8. DISCUSSION AND CONCLUSION The main aim of the present work is the development of audio watermarking

algorithms, with the state-of-the-art performance. The algorithms performance is

validated in the presence of the standard watermarking attacks. The models we

propose are able to embed 1024 bits in 6.5 sec duration signal preserving the

imperceptibility of the signal. The results provided in chapter 5 and chapter 6

indicates that DWT-DCT domain is the more suitable domain to embed the

information. The SS based method is more robust than the method based on patch

work algorithm whereas the patchwork algorithm provides better imperceptibility.

The SS based algorithm obtained high detection robustness and increasing the

perceptual transparency of the watermarked signal. Time required to embed and

recover the watermark from SS based scheme is 21.131 sec. Time required to embed

and recover the watermark from GOS based scheme is 24.401 sec.

8.1. Main contribution of the present research

1. Audio watermarking scheme is proposed to embed an audio watermark in the

audio signal.

2. Adaptive blind watermarking algorithms based on SNR are developed using

Spread spectrum technique.

3. Spread spectrum based techniques are implemented in different transform

domains.

4. Cyclic coder and attack characterization by diversity is used to increase the

robustness of the scheme.

5. Synchronization pattern is added to track the watermark.

6. New intelligent encoder and decoder model is proposed for an audio

watermarking system using Spread Spectrum method.

7. Blind watermarking algorithms based on GOS modification are developed in

different transform domain.

8. Cyclic coder and attack characterization by diversity is employed to increases the

robustness.

9. Synchronization pattern is added to track the watermark.

26

10. New intelligent encoder and decoder model is proposed for an audio

watermarking system using GOS method.

Our contributions:

I) “DWT based Image Watermarking” at international conference ICWCN

2003 organized by S.S.N.C.O.E., Kalavakkam, Chennai during 27-28 June

2003.

II) “DWT based Audio Watermarking” at international conference ICWCN

2003 organized by S.S.N.C.O.E., Kalavakkam, Chennai during 27-28 June

2003.

III) “Audio Watermarking for covert communication” in 12th annual Symposium

on mobile computing and applications organized by IEEE Bangalore section

November 2004.

IV) “Insight on Audio Watermarking” at national conference NC2006 organized

by Padre Conceicao College of Engineering Verna – Goa during 14-15

September 2006

V) “Performance analysis of Audio Watermarking in Wavelet Domain” at

international conference RACE organized by Engineering College Bikaner

during 24-25 March 2007

VI) “SNR based audio watermarking in Wavelet domain” International

conference ICCR-08 April 2008 Mysore.

VII) “SNR based audio watermarking Scheme for blind detection” International

conference ICETET -08, July 2008 Nagpur, published by IEEE Computer

Digital Library.

VIII) “SNR based Spread Spectrum Watermarking using DWT and Lifting

Wavelet Transform” in International Journal IJCRYPT October 2008.

IX) “Adaptive Spread Spectrum Audio Watermarking for Indian Musical

Signals by Low Frequency Modification” in proc. IEEE international

conference ASID 2009 held in 22-25 August 2009 at Hongkong.

X) “Adaptive Spread spectrum audio watermarking” in ICFAI Journal on

Information technology, September 2009.

27

XI) “Adaptive Audio Watermarking for Indian Musical Signals by GOS

Modification” selected in IEEE International conference TECNCON 2009

held on 23-26 November 2009 at Singapore.

Selected Bibliography:

[1]. I. J. Cox and Matt L Miller, “ The first 50 years of electronic watermarking”,

Journal of applied signal processing, 2002, pp. 126-132.

[2]. I. J. Cox, J. Kilian, F. Thomson Leighton and T. Shamoon, “Secure Spread

Spectrum Watermarking for Multimedia”, IEEE Transactions on Image Processing,

Vol. 6, No. 12, December 1997, p.p. 1673-1686.

[3]. J. A. Bloom, I. J. Cox, T. Kalker, J. M. G. Linnartz and M. L. Miller, C. B. S.

Traw , “Copy protection for DVD Video” ,Proc. of the IEEE, Vol. 87, No. 7, July

1999.

[4]. C.T. Hsu, J.L. Wu., “Hidden Digital Watermarks in Images”, IEEE

Transactions on Image Processing, Vol. 8,No. 1, January 1999, p.p.58-68.

[5]. D. Kundur and D. Hazinakos, “Digital watermarking for telltale tamper

proofing and authentication”, Proc. IEEE No.7, July 1999, pp. 1167-1180.

[6]. D. Kundur and D. Hazinakos, “Diversity and Attack Characterization for

Improved Robust Watermarking”, IEEE Transactions on Signal Processing, Vol. 49,

No. 10, October 2001, p.p.2383-2396.

[7]. Ming Shing Hsish and Din Chang, “Hidding the digital watermarks using

Multiresolution wavelet transform”, IEEE Transactions on industrial electronics.

Vol. 48 No. 5. October 2001, pp. 875-891.

[8]. L. M. Marvel, C.G. Boncelet and C.T. Retter, “Spread Spectrum Image

steganography”, IEEE Transactions on Image Processing, Vol. 8. August 1999. p.p.

1075-1083.

[9]. C. S. Lu, S.K. Huang, C. J. Sze and H. Y. M. Liao, “Cocktail Watermarking for

Digital Image Protection”, IEEE Transaction on Multimedia, Vol. 2., No.4,

December 2000, p.p. 209-224.

[10]. D.Kundur, “Watermarking with Diversity: insights and Implications”, IEEE

Multimedia magazine , October-Decemer 2001, p.p 46-52.

28

[11]. A.P.Fabien and Petitcols, “Watermarking Schemes evaluation”, IEEE Signal

Processing Magazine, September 2000, p.p.58-64.

[12]. L.B. Boney ,A. Twefik and K. Hamdy, “Digital Watermarks for Audio

Signals”, IEEE Int. Conf. on Multimedia computing and Systems, June 1996, p.p.

473-480.

[13]. J. Seok, J. Hong and J. Kim, “A Novel Audio Watermarking Algorithm for

Copyright Protection of Digital Audio,” ETRI Journal, Volume 24, No. 3, June

2002, p.p. 181-189.

[14]. D.Kirovski and H.S.Malvar, “Spread spectrum watermarking of audio signals”

, IEEE Transactions on Signal Processing, Vol. 51, No. 4, April 2003 p.p. 1020-

1033.

[15]. H.S.Malvar and D.A.F. Florencio, “Improved Spread Spectrum: A New

Modulation Technique for Robust Watermarking”, IEEE Transactions on signal

Processing, Vol. 51, No. 4, April 2003, p.p. 898-905.

[16]. S. Esmaili, S. Krishnan and K. Raahemifar, “A Novel Spread Spectrum Audio

Watermarking Scheme Based on Time-Frequency Characteristics”, Proc. of CCECE

2003, p.p. 1963-1966.

[17]. D. Kirovaki and H. Malver, “Robust covert communication over a public audio

channel using spread spectrum”, available at site www.cs.ucla.edu/~darko/papers.

[18]. Hafiz Malik, Ashfaq Khokhar and Rashid Ansari, “Robust audio watermarking

using frequency selective spread spectrum theory” Proceeding of ICASSP 2004,

IEEE, p.p. V385- V388.

[19]. W. Bender, D. Gruhl, N. Morimoto and A. Lu, “Techniques for data hiding”,

IBM system Journal, 1996,Vol. 35, p.p. 313-336.

[20]. I.K. Yeo and H.J. Kim, “Modified patchwork algorithm: a novel audio

watermarking scheme”, Proc. ICITCC 2001, p.p. 237-242.

[21]. N. Cvejic and T. Seppanen, “Robust Audio watermarking in Wavelet Domain

Using Frequency Hopping and Patchwork method”, Proc. of 3rd International

Symposium on Image and Signal processing and Analysis 2003, p.p. 251-255.

[22]. R.Wang and Dawen Xu,Q. Li, “Multiple audio watermarks based on lifting

wavelet transform”, Proc. of 4th international conference on m/c learning and

cybernetics IEEE, August 2005,p.p. 1959-1964.

29

[23]. W. N. Lie and L. C. Chang, “Robust and High Quality Time-Domain Audio

Watermarking Based on Low Frequency Amplitude Modulation”, IEEE Transactions

on Multimedia, Vol.8., No.1., February 2006, p.p. 46-59.

[24]. P. Bassia and I. Pitas, “Robust audio watermarking in the time domain”, IEEE

Transactions on Multimedia,Vol. 3, No.2, June 2001 p.p.232-241.

[25]. A. N. Lemma, J. Aprea, W. Oomen and L. V. D. Kerkhof, “A Temporal

Domain Audio Watermarking Technique”, IEEE Transaction on Signal Processing,

Vol. 51, No. , April 2003, p.p. 1088-1097.

[26]. S. Wu., J. Huang, D. Huang and Y. Q. Shi, “Self-Synchronized Audio

Watermark in DWT Domain”, Proc. of ISCAS 2004, p se.p. V.712-V.715.

[27]. J. Huang, Y. Wang and Y. Q. Shi, “A blind audio watermarking algorithm with

self synchronization,” Proc. IEEE Int. Symp. On circuits and systems, vol. 3, 2002,

p.p.627-630

[28]. S. Wu., J. Huang, D. Huang and Y. Q. Shi, “Efficiently self synchronized Audio

watermarking for Assured Audio Data Transmission”, IEEE Transaction on

Broadcasting, Vol. 51, No1, March 2005, p.p. 69-76.

[29]. X. Li, M. Zhang and S.Sun, “Adaptive audio watermarking algorithm based on

SNR in wavelet domain”, Proc. of IEEE conference 2003 p.p.287-292.

[30]. A. Takahashi, R. Nishimura and Y. Suzuki, “Multiple Watermarks for Stereo

Audio Signals Using Phase-Modulation Techniques”, IEEE Transactions on Signal

Processing, Vol. 53, No. 2, February 2005, p.p. 806-815.

[31]. H. H. Tsai, J. S. Cheng and P. T. Yu, “Audio Watermarking Based on HAS and

Neural Networks in DCT Domain”, EURASIP Journal on Applied signal processing

2003, p.p. 252-263.

[32]. C.Xu, J. Wu, D.D. Feng, “Content based Digital Watermarking for

Compressed Audio”, Available on line at site

http://133.23.229.11/~ysuzuki/Proceedingsall/ .

[33]. X. Li and H.H. Yu., “Transparent and Robust Audio Data Hiding in Cepstrum

Domain”, Proc. IEEE International conference on Multimedia and Expo. New

York(I), 2000, P.P.397-400.

30

[34]. S. K. Lee and Y. S. Ho, “Digital Audio Watermarking in the Cepstrum

Domain”, IEEE Transaction on Consumer Electronics. Vol.46, No.3 August 2000,

p.p.744 -750.

[35]. N. Sriyingyong and K. Attakimongeol, “Wavelet based Audio watermarking

using adaptive Tabu search”, Proc. of IEEE Int. Symp. Wireless Pervacive

computing 2006 pp 1-5 available at

http://sutir.sut.ac.th:8080/sutir/bitstream/123456789/1821/1/BIB990_F.pdf.

[36]. Wang, Xu Dawen, Chen Jiner and Duchengtou, “Digital Audio Watermarking

algorithm based on Linear Predictive Coding in wavelet domain”, ICSP-04 Proc. of

IEEE, 2004, p.p. 2393-2396.

[37]. Chin-Su Ko, K.Kim, R.Hwang, Y. Kim and S.Rhee, “Robust Audio

Watermarking in wavelet domain using PN sequences”, Proc. of ICIS-2005

published by IEEE.

[38]. R.Vieru, R. Tahboub,C. Constantinescu and V. Lazarescu, “New results using

the audio watermarking based on wavelet transform”, International Symposium on

Signals, Circuits, and Systems, Kobe, Japan 2 (2005) published by IEEE 2005, p.p.

441-444.

[39]. R.Wang, Dawen Xu and L Qian, “Audio Watermarking based on wavelet

packet and Psychoacoustic model”, IEEE Proc. of PDCAT-2005.

[40]. Wang, Xu, Z. Hang and C. Youngrui, “2-D digital Audio Watermarking based

on Integer Wavelet Transform”, IEEE Proc. of ISCIT 2005, p.p. 877-880.

[41]. S.Ratansanya, S.Poomdaeng, S. Tachaphetpiboon and T. Amornraksa, “New

Psychoacoustic models for wavelet based Audio watermarking”, IEEE Proc. of

ISCIT 2005, p.p. 582-585.

[42]. S. D. Larbi, M. J. Saidane , “Audio Watermarking A Way to Stationnarize

Audio Signals”, IEEE Transactions on Signal Processing, Vol. 53, No. 2, February

2005 p.p. 816-823.

[43]. T. Furon and P. Duhamel, “An Asymmetric Audio Watermarking Method”,

IEEE Transactions on Signal Processing, Vol. 51, No. 4, April 2003 p.p. 981-995.

[44]. B. S. Ko, R. Nishimura and Y. Suzuki, “Time-spread Echo method for digital

audio watermarking”, IEEE Transaction on multimedia, Vol. 7, No.2, April 2005,

pp 212-221.

31

[45]. S. Eerüçük, S. Krishnan and M. Zeytinoğlu, “A Robust audio watermark

representation based on linear chirps”, IEEE Transaction on multimedia, Vol. 8,

No.5, October 2005, pp 925-936.

[46]. X. Wang, W. Qi and P. Niu, “A new Adaptive Digital Audio watermarking

based on support vector regression”, IEEE Transaction on audio , speech and

language processing, Vol. 15, No.8, November 2007, pp 2270-2277.

[47]. M.Mansour and A. Twefik, “Audio watermarking by time scale modification”,

in Proc. Int. conf. on Acoustics, speech and signal processing, May 2001, Vol. 3, pp

1353-1356.

[48]. M.Mansour and A. Twefik, “Data embedding in audio using time-scale

modification”, IEEE Transaction on speech audio Processing, Vol. 13 No.3 pp432-

440, 2005.e

[49]. Wei Li, X Xue and P. Lu , “Localized Audio watermarking technique Robust

against Time scale modification”, IEEE Transaction on multimedia, Vol. 8, No.1,

February 2006, pp 60-69.

[50]. S. Xiang and J. Huang, “Histogram-based audio watermarking against time-

scale modification and cropping attacks”, IEEE Transaction on multimedia, Vol. 9,

No.7, November 2007, pp1357-1372.

[51]. X. Y. Wang and H. Zhao, “A Novel Synchronization Invariant Audio

Watermarking Scheme based on DWT and DCT”, IEEE Transaction on signal

processing, Vol.54, No.12, December 2006, pp 4835-4839.

[52]. J. D. Gordy and L. T. Bruton, “Performance Evaluation of Digital Audio

Watermarking Algorithms”, Proc. of Midwest symposium on Circuits and Systems,

Vol. 1, August 2000, p.p. 456-459.

[53]. F. Bartolini, M. Barni and A Piva, “Performance Analysis of ST-DM

Watermarking in Presence of Non Additive Attacks”, IEEE Transactions on Signal

Processing, Vol. 52, No. 10, October 2004 p.p. 2965-2974.

[54]. F.A.P. Petitcolas and R. J. Anderson, “Evaluation of copyright marking

systems”, Proc. of IEEE Multimedia systems’ 99, Vol. 1, pp 574-579.

[55]. G.C. Roddriguez, M. N. Miyatake and H. M. P. Meana, “Analysis of Audio

Watermarking Schemes”, Proc. of ICEEE 2005, p.p. 17-20.

32

[56]. M. Wu, S. A. Craver, E. W. Felten and B. Liu, “Analysis of Attacks on SDMI

Audio Watermarks”, IEEE Int. Conf. on acoustics, speech and Signal Processing,

Vol. No.3, p.p. 1369-1372.

[57]. F. Hartung, J. K. Su and B. Girod, “Spread Spectrum Watermarking: Malicious

Attacks and Counterattacks”, Proc. SPIE, Vol. 3657 January 1999.

[58]. M. Kuttur, S. Voloshynovskiy and A. Herrigel, “ The Watermark Copy

Attack”, Proc. SPIE, Vol. 3971, security and watermarking of multimedia. 2000,

p.p.371-380.

[59]. Alexander Herrigel, Sviatoslav Voloshynovskiy and Yuriy Rystar, “The

watermark template attack.” Proc. SPIE, Vol 4314, 2001, p.p. 394-400.

[60]. J. K. Su., F. Hartung and B. Girod, “A Channel Model for a Watermark

Attack”, Proc. SPIE, Vol. 3657, January 1999, p.p. 159-170.

Signature of Research Guide Signature of Research Student

Dr. Mrs. S. D. Apte Mrs. Meenakshi Ravindra Patil