40120130406010 2-3-4-5-6

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –

6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME

71

A HIGH CAPACITY DIGITAL AUDIO WATERMARKING USING

DISCRETE WAVELET AND COSINE TRANSFORM

G. B. Khatri, Dr. D. S. Chaudhari

Department of Electronics and Telecommunication,

Government College of Engineering,

Amravati, India.

ABSTRACT

Piracy and copyright protection is a major issue among the music owners and publishers.

Major improvements in digital and internet technologies have made the illegal production, storage

and distribution of the digital multimedia files very easy. As a remedy digital audio watermarking is

nowadays gaining popularity among the original audio content producers. This technique embeds the

owner information as a copyright material in the digital audio as a proof of ownership. This paper

introduces the watermarking, its applications and techniques developed. An algorithm is proposed

based on wavelet transform which has efficient computational load and also on cosine transform for

increasing the watermark embedding capacity. Algorithm is attacked with different signal processing

attacks and then evaluated on the basis of watermarking parameters like imperceptibility, robustness,

and embedding capacity.

Index Terms: Watermark, Audio, DRM, DFT, DCT, DWT and DSSS.

I. INTRODUCTION

The chain of production, sales and copyright has been drastically disturbed due to invention

and widespread use of digital media and internet. Virtuality of digital media affects the economic

behind the production and distribution of media and the copyright of the original media. The piracy

of non-physical form of media is due to their digitalization; this gets exaggerated due to negligible

cost of reproduction and their ability to be digitally delivered.

The production, storage, distribution of digital multimedia data is very easy over the internet.

Hence it creates the problem of protecting the intellectual copyrights and the ownership of the media.

The copyrighted digital multimedia data is pirated without notification to the owner.

Digital watermarking is a technique to embed the owner information as a copyright material

in the digital data as a proof of ownership. Watermark as the name suggest is as transparent as water

INTERNATIONAL JOURNAL OF ELECTRONICS AND

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ISSN 0976 – 6464(Print)

ISSN 0976 – 6472(Online)

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when watermark data is embedded in the original audio. Thus a watermark as a signature can be

embedded in the digital multimedia files as a proof of ownership [1, 2].

Digital watermarking can be applied to image, audio or video and the watermark data can be

an image, audio and text. Generally there is less attention towards audio watermarking because HAS

(human auditory system) is more sensitive than HVS (human visual system) and human ears can

easily detect the presence of the watermark as low as one part in ten million [3].

This paper presents an overview on applications and techniques of digital audio

watermarking. Section II describes the digital audio water marking, its applications and previous

watermarking techniques while in section III we describe in details the watermark embedding and

extraction procedures of the proposed algorithm; while the performance of the algorithm and

simulation results with respect to inaudibility, robustness and payload capacity is presented in

Section IV. Section V concludes the paper with some remarks.

II. DIGITAL AUDIO WATERMARKING: APPLICATIONS AND TECHNIQUES

Digital audio watermarking is a technique of embedding watermark data such as image, audio

and text in the original audio stream to create copyrighted watermarked audio. Audio watermarking

application areas include such as vendor identification, evidence of proprietorship, validation of

genuineness, copy protection, etc. Each application puts desirable feature necessity on the

watermarking technique. Hence the watermarking technique to be used depends on the area of

application [4]. Thus a variety of applications are discussed below,

1) Vendor Identification Text form of copyright notices occurs on the packaging of copyrighted materials. This type of

protection does not prove sufficient as it would be easily removed. Digital audio watermarking can

be used to embed copyright notice in the audio signal itself. As notice forms an integrated part of

audio one can determine the vendor of the copyrighted audio.

2) Evidence of Proprietorship One can prove its proprietorship in the case of copyright dispute. The original owner can

prove its proprietorship by extracting the watermark copyright information from the watermarked

audio, in the case when another person tries to sell the copyrighted material on behalf of his name by

pirating it.

3) Validation of Genuineness

The copyrighted audio is genuine or not, can be proved very easily by the use of

watermarking. A signature or copyright watermark is embedded in the audio thus anyone trying to

modify the watermarked audio, modification also applies to the watermark because watermark forms

integral part of audio. Hence one can prove genuineness of copyrighted audio by extracting exact

copy of the watermark.

4) Copy Protection Above mentioned applications do not put restrictions on the illegal copying. Owner can

restrict the illegal copying or the numbers of copies permitted by the use of special watermarking

algorithms. One such method is modified patchwork algorithm (MPA) developed by Yeo and

Kim [5].

Audio watermarking techniques can be grouped as; time-domain, frequency (transform)

domain, spread-spectrum, patchwork [1, 5].



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1) Time-domain techniques It includes the least significant bit (LSB) substitution, echo hiding and quantization

techniques. The time-domain audio watermarking is relatively easy to implement, and requires few

computing resources, however, it is weak against signal processing attacks such as compression and

filtering. This type of techniques suffers from problems like low watermark data embedding

capacity, easily detectable by the attacker, easy to decode watermark from cover audio.

LSB technique is the simplest technique in which watermark details embedded in the least

significant bits of the audio sample values. As the information contained in the LSB is less, it is

replaced by the data of the watermark without producing the noticeable effect in the cover audio

signal. A maximum of 3 watermark bits per 16 bits of audio sample is allowed for imperceptibility.

Cvejic N. and Seppanen T. have tried to increase the capacity from 3bits/sample to 4bits/sample

without degrading the watermarked audio signal to noise ratio by using a three step technique. In this

degradation in SNR of the watermarked audio is minimised by using minimum error replacement

and error diffusion steps.

Echo-hiding watermarking embeds information into the original discrete audio signal by

introducing a repeated version of an original sample of the audio signal with some delay and decay

rate so as to make it undetectable [6]. Digital data is embedded by using four main parameters of

echo: initial value, decay rate and different offset for 1 and 0. The offset is made so small such that

the human ear cannot detect the presence of echo. The watermark data embedding rate is given as 16

bps (bits per second), while it can vary in the range 2–64 bps and it depends on the sampling rate and

the signal type to be echoed.

In the technique of quantization original sample of audio is replaced with the modified audio

sample. The modified audio sample is defined as below,

� � � ��, � � , �� 1 ��, � � , �� 0� (1)

Where q (.) is quantization function and is quantization step. The quantization function is given as,

��, � �� ⁄ � (2)

Where [x/A] is rounded to nearest integer. Thus in a single sample of audio signal one can embed

only one bit of watermark. Hence a blind detection can be applied for watermark data extraction.

Extraction can be done by following equation,

� � � 1 , �� 0 � � � ��, � /4 0 , �� /4 � � � ��, � 0 � (3)

This technique is simple and easy to implement and is robust to noise as long as the noise margin is

below A/4. While the technique is easy but the watermark embedding capacity is very less.

2) Frequency domain techniques

Frequency domain audio watermarking techniques generally include transforms like discrete

Fourier transform (DFT), the discrete cosine transform (DCT), and the discrete wavelet transform

(DWT). It takes the advantage of masking of different tones of human auditory system (HAS) for

effective watermarking. Discrete Fourier transform decomposes the signal into its fundamental and

harmonically related sinusoidal frequencies. The human ears sensitivity declines after the peak

sensitivity around 1 kHz. Magnitude response coefficients are replaced by the watermark data in the

frequency range of 2.4 – 6.4 kHz [7]. Also the human ears are insensitive to the absolute phase of the

audio frequency; hence the phase difference between the phase signal coefficient and phase reference

coefficient is used to modify the phase signal coefficient [8].

Discrete cosine transform is similar to the discrete Fourier transform except that its

coefficients are real valued. Properties of DCT such as high compaction of signal energy in

transform domain, highly decorrelated coefficients are used to embed data in the transform domain.



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Discrete wavelet transform is nowadays gaining popularity because it can decompose the

signal in time and frequency at the same time while keeping the calculations to obtain DWT

coefficients small as compared to DFT and DCT. Several advantages of applying DWT on audio

signal are given by wu and huang such as a) It is able to localize the audio in time-frequency both

with multi-resolution property, b) variable decomposition levels are available, c) less number of

operations than DFT and DCT [9]. If there are N samples in the audio then number of operations in

DFT, DCT and DWT are O (N·log2(N)), O(N·log2(N)) and O(L·N) respectively, where L is the

length of wavelet filter. A data payload capacity of 172 bps is achieved by embedding the self-

synchronised watermark data in the wavelet domain without degrading the SNR too much[9].

3) Spread-spectrum technique It involves embedding of watermark in the original audio signal by spreading it over the

bandwidth of audio signal [6]. This technique utilizes DSSS (Direct Sequence Spread Spectrum)

method of spread spectrum.

In DSSS PN-sequence in used to spread the watermark data in the whole frequency range of

audio and then added to the audio signal by proper attenuation, so that the watermark data is treated

as additive random noise. Same sequence is again used to extract the watermark data by performing

correlation between watermarked audio and PN-sequence.

4) Patchwork technique This technique was first proposed for image watermarking and it is a pseudorandom

statistical approach [6]. The idea is to select two subsets (patch) of the cover signal and in order to

embed the watermark, the sample values of these two subsets are moved in opposite directions by a

constant valued, which defines the watermark strength and watermark bit. The assumption in this

method is that the difference of the means of the two patches is zero for the original cover signal and

is nonzero for the watermarked cover signal. As two subsets (patch) are used this technique can

extract the watermark without the original cover signal.

Yeo and Kim have proposed a modification on the patchwork technique [5]. They have

embedded the watermark data bit in the two subsets taken from DCT domain of original audio

signal. The use of transform domain for embedding watermark makes the technique more robust

against signal processing attacks such as down-sampling, equalization, compression, filtering.

III. PROPOSED ALGORITHM

As observed from the above techniques that transform domain are more secure than time

domain. The models proposed utilize the discrete wavelet transform for speedily and efficiently

transforming audio in time-frequency domain, while using discrete cosine transform to decor relate

and compress watermark image.

Transformed watermark image coefficients must be normalised and multiplied with an

attenuation constant before embedding. Attenuating the coefficients helps to keep noise level low in

the audio signal. Since watermark image is compressed using discrete cosine transform less number

of transform coefficients are used for embedding and this improves the signal to noise ratio and also

the watermark data embedding capacity.

Discrete wavelet transform is used to transform the audio signals into frequency sub bands.

These sub bands are called as approximate or detail frequency sub bands. Any one of this frequency

band can be used to embed watermark.

This algorithm is based on emerging wavelet transform and cosine transform for embedding

and extracting watermark in audio signal, hence it will possess characteristics such as high signal-to-



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noise ratio (SNR) of watermarked audio, high data payload capacity i.e. number of watermark bits

per second of audio signal, low computational complexity.

1) Embedding algorithm 1. The original audio signal is decomposed at level N with the help of 1D-Discrete Wavelet

Transform (1D-DWT) and accordingly we get wavelet coefficients as,

� !"#�� (4)

� $, !$, !$%&, ………… , !& (5)

$ � (()*��+��*, -*.�� .,�� /.0./ 1 (6)

!$, !$%&, ………… , !& � !.��/ -*.�� .,�� )*+ /.0./ 1 �* /.0./ 1 (7)

Figure 1 Wavelet Decomposition

Figure 2 Watermark Embedding Model



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2. Watermark is applied Discrete Cosine Transform (DCT) so as to concentrate watermark energy in

one corner. This will form a set of DCT coefficients in the form of matrix having the dimensions

as that of the watermark image.

Figure 3 Watermark Image Figure 4 DCT Transformed Watermark

3. Matrix of DCT transformed watermark is converted into row stream W by zigzag scanning

method.

4. Multiply the row stream of watermark by alpha blending constant (α) for controlling the

brightness of extracted watermark and for achieving high Signal to Noise Ratio (SNR) for the

watermarked audio signal.

" ′ � 2 3 " (8)

5. Truncate the row of DCT coefficients to a limited number of coefficients as provided by the user.

This will form an instance of DCT coefficients of the watermark.

" ′′ � �)4, ��.�" ′ (9)

6. Append the instances of watermark in series and then add to one of the sub-bands of the wavelet

decomposed audio signal. " ′′′ � +4/��(/. �,��, .� " ′′ �, �.)�.� (10) /.,5�6 *� �./. �.� �4� � ��,� 7 /.,5�6 *� " ′′′ (11) !&′ � !& �" (12)

7. Apply 1D-Inverse Discrete Wavelet Transform (1D-IDWT) of level N to watermark embedded

audio signal so as to create the watermarked audio signal.

′ � $, !$ , !$%&, ………… , !&′ (13)

��′ � 8!"#� ′ (14)

2) Extracting algorithm 1. The original audio signal and watermarked audio signal is decomposed at level N with the help of

1D-Discrete Wavelet Transform (1D-DWT).

� !"#�� (15)

� $ , !$, !$%&, ………… , !& (16)

′ � !"#��′ (17)

′ � $, !$, !$%&, ………… , !&′ (18)

2. Subtract the sub-band of original audio signal from sub-band of watermarked audio signal

containing watermark, so as to recover the instances of DCT coefficients of watermark.

" � !& � !&′ (19)



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Figure 5 Watermark Extraction Model

3. Extract any one of the instance from the series of instances recovered from watermarked audio

signal.

4. Divide the row stream of extracted watermark by alpha blending constant (α).

5. Convert row stream of extracted watermark in to matrix form by inverse zigzag scanning method.

6. The matrix obtained is processed by Inverse Discrete Cosine Transform (IDCT) to recover

watermark in its original form.

IV. RESULTS AND PERFORMANCE ANALYSIS

This research work uses a mono channel audio signal with .WAV format, 8 kHz sampling

frequency, 16 bit depth, and 13.8114 sec long as a cover signal. While for the watermark a grayscale

image with dimension 252×298 and bit depth of 8 bit in .TIF format is chosen. All algorithms,

including proposed technique, are implemented on Windows PC having Intel Core-i3 2.93 GHz

processor, 2GB RAM and are simulated using MATLAB2009.

Results are based on the parameters like Imperceptibility, Robustness and Payload

Capacity. The robustness performance of the algorithm is evaluated under different signal processing



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attacks like re-quantization, re-sampling, low-pass filtering, high-pass filtering, AWGN and

cropping. Following paragraph describes the performance metrics and then the results are presented.

1) Imperceptibility Imperceptibility is related to the perceptual quality of the embedded watermark data within

the original audio signal. To measure imperceptibility, we use Signal to Noise Ratio (SNR) as

quantitative measure, and a listening test as a qualitative measure.

Signal to Noise Ratio (SNR) is a statistical difference metric which is used to measure the similarity

between the original audio signal and the watermarked audio signal. 91:��; � 10 /*5&< ∑ ��>?>∑ @�>% �>′ A?> (20)

Where, Anis original audio signal and A’n is watermarked audio signal.

A listening test was performed to estimate the qualitative grade of the watermarked signals. These

grades are based on the criteria of Mean Opinion Score (MOS) which describes the perception

quality.

Table 1 Mean Opinion Score (MOS) Grade

MOS Grade Description

5 Imperceptible

4 Perceptible, but not annoying

3 Slightly annoying

2 Annoying

1 Very annoying

2) Robustness Watermarked audio signals can undergo common signal processing attacks such as filtering,

compression, cropping, time scale modification and many others. These attacks happen either

intentionally or unintentionally. Although these attacks may not affect the perceived quality of the

host signal, they can corrupt the watermark embedded within the signal. To evaluate robustness of

the proposed algorithm, we implemented a set of attacks that commonly affect audio signals.

After extracting watermark from corrupted audio signals they are compared with original watermark

for their similarity. This similarity is calculated by finding the correlation factor (ρ) between the two

of them. B�C,C ′ � ∑ �DE 3 DE′FEGH I∑ �DE?FEGH 3 I∑ @DE′A?FEGH

(21)

Where w and w’ are original watermark and extracted watermark, respectively.

3) Payload capacity The payload capacity C of the algorithm to embed the amount of watermark bits per second

of original audio signal is calculated by the following formula; - � JKLMN OPQRST KU RVLW VO VQMXSYTVXVOMN �PZVK ZPTMLVKO (22)

- � $PQRST KU [V\SNW 3]VL ZS[L^ KU SM_^ [V\SN�PZVK ZPTMLVKO ��(� (23)

Generally due to use of Discrete Cosine Transform (DCT) the payload capacity C varies

drastically. This happens because only a length of DCT coefficients is used for embedding the

watermark. This length depends on the sub-band of DWT transformed original audio used for

watermark embedding.



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In the simulation for the analysis and synthesis of the audio signal is done using dB1 wavelet

with level 1 decomposition. The alpha constant (α) is set to 0.1 and the first 10000 DCT coefficients

of DCT transformed image is used to embed watermark in the first level detail sub band of DWT

decomposed audio. Table 2 shows the results based on imperceptibility, robustness, payload capacity

under different attacks.

Table 2 Results of proposed algorithm under various attacking conditions

Attacks

Imperceptibility Robustness Payload

Capacity

SNR (dB) MOS Correlation Factor

(ρ) C (bps)

Un-attacked 58.1767 5 0.9982 43498.0586

Low Pass Filter 58.1767 5 0.8426 43498.0586

High Pass Filter 58.1767 5 0.9968 43498.0586

Resampling (4000) 58.1767 5 0.9148 43498.0586

Resampling (16000) 58.1767 5 0.7720 43498.0586

Requantization (8-bit) 58.1767 5 0.6275 43498.0586

Requantization (24-bit) 58.1767 5 0.9982 43498.0586

Cropping 25% samples 58.1767 5 0.9982 43498.0586

Amplification (20%) 58.1767 5 0.9982 43498.0586

Amplification (-20%) 58.1767 5 0.9982 43498.0586

AWGN (90dB) 58.1767 5 0.9934 43498.0586

High value of SNR shows the similarity between original and watermarked audio, while the

Figure 6 shows it graphically.

Figure 6 Original and Watermarked Audio



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In Figure 6 one can observe that five instances of watermark in the form of DCT coefficients

are embedded in series in the audio. The peaks in the error audio show the first sample location of

the DCT coefficient.

Extracted watermark is shown in the Figure 7 below for various signal processing attacks.

(a)ρ = 0.8426 (b)ρ =0.9968 (c)ρ = 0.9148 (d) ρ = 0.7720 (e)ρ =0.6275

(f) ρ =0.9982 (g) ρ =0.9982 (h) ρ =0.9982 (i) ρ =0.9982 (j)ρ =0.9934

Figure 7 Extracted Watermark and their Correlation Factor (ρ) Under Different Attacks

(a)Low Pass Filter, (b)High Pass Filter, (c)Downsampling, (d)Upsampling, (e)Requantizaton to 8-bit,

(f)Requantizaton to 24-bit,(g)Cropping 25%, (h)Amplification 20%, (i)Amplification -20%,

(j)Gaussian Noise

In Figure 7 it is observed that the watermark is robust to most of the attacks except

resampling (Down and up) and requantization (Down). The similarity i.e. correlation factor (ρ)

between the original and watermarked audio signal is also shown which is near to 1. The high value

of ρ indicates that the system is robust to most of the signal processing attacks.

The performance depends on proper selection of α; Graph1 provides the effect of alpha

constant (α) on the signal to noise ratio and on the correlation factor (ρ) of the watermarked signal. A

low SNR value yield high robustness but the quality of the watermarked signal is degraded as shown

in graph using high α; and lower value of α gives high SNR which make the watermark

imperceptible, however robustness is decreased. Hence, α value is chosen in a way that the SNR of

the watermarked signal is between 40 dB and 70 dB for efficient performance. The results shown in

Graph 1 and Graph 2 are for no signal processing attack on watermarked audio signal.



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Graph 1 Effect of Alpha Constant (α) on Signal to Noise Ratio (SNR)

Graph 2 Effect of Alpha Constant (α) on Correlation Factor (ρ)

The most suitable value of α from the Graph 1 and Graph 2 is found to be 0.1 and this value

of α gives SNR value above 55dB and value of ρ close to 1. The high similarity between original and

extracted watermark is due to higher values of ρ.

Performance of the algorithm depends on the number of DCT coefficients embedded. Graph3

provides the effect of number of DCT coefficients embedded on the signal to noise ratio and on the

correlation factor (ρ) of the watermarked signal. Embedding less number of DCT coefficients

increases the number of instance in the watermarked audio which increases the robustness but the

quality of the watermarked signal is degraded as shown in graph; and if more number of DCT

coefficients are embedded SNR is increased which make the watermark imperceptible, however

robustness is decreased. Hence, number of DCT coefficients is chosen in a way that the SNR of the

watermarked signal is between 40 dB and 70 dB for efficient performance without degrading the

quality of extracted watermark i.e. increase in robustness. Considering the same conditions for the

experiment following graphs are obtained by varying the number of DCT coefficients to be

embedded.

0

10

20

30

40

50

60

70

80

90

100

110

120

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

SN

R (

dB

)

Alpha Constant (α)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Co

rrel

ati

on

Fa

cto

r ρ

Alpha Constant (α)



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Graph 3 Effect of Number of DCT Coefficients Embedded on Signal to Noise Ratio (SNR)

Graph 4 Effect of Number of DCT Coefficients Embedded on Correlation Factor (ρ)

From the results and analysis it can be stated that the imperceptibility of the system largely

depends on the values of the alpha constant (α) and number of DCT coefficients of watermark to be

embedded. The imperceptibility of the system is optimised when the value of α is 0.1. The length of

the level-1 detail subband is maximum and this is the reason for choosing the level-1 detail subband

for embedding audio signal. Imperceptibility also depends on the value of the number of DCT

coefficients embedded; hence from the results it is shown that 10000 is the optimised value.

V. CONCLUSION

In this dissertation a new algorithm has been proposed taking features of DWT and DCT;

where the DWT is the developing area in signal processing theories. Proposed algorithm is based on

signal energy compaction in DCT domain while DWT as a faster approach to convert the signal into

transform domain.

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

0 2500 5000 7500 10000 12500 15000 17500 20000

SN

R (

dB

)

Number of DCT coefficients embedded

0.5

0.6

0.7

0.8

0.9

1

0 2500 5000 7500 10000 12500 15000 17500 20000

Co

rrel

ati

on

Fa

cto

r ρ

Number of DCT coefficients embedded



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The performance of the algorithm is provided by evaluating the performance parameters such

as signal to noise ratio SNR, correlation factor (ρ), and MOS. Capacity as a performance parameter is

the goal of this developed algorithm and has been achieved by using DCT which is most efficient

and popular for compacting energy of the signal onto very few coefficients. There is no effect on the

payload capacity due to the number of coefficients to be embedded because it is calculated on the

basis of original watermark bits. If less number of DCT coefficients are embedded the extracted

image gets somewhat blurred and hence we never gets the correlation factor (ρ) equals to 1.

Performance of the algorithm is evaluated by applying the signal processing attacks. The algorithm

proposed by using DWT and DCT transformation is not robust against resampling. In addition, the

effects of the alpha blending constant (α) and the number of DCT coefficients to be embedded on the

SNR and on correlation factor (ρ) is shown and analysed.

The audio watermarking is relatively new and has wide scope for research. This research can

be continued to colour images from the limitation of embedding only grayscale image. Also the

technique can be implemented on live signals rather than a fixed signal. Some of the real time audio

signals include speech and music. Further research can be carried on embedding watermark in video

sequences i.e. movies or surveillance.

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[1] Wikipedia.(2013).DigitalWatermarking.Available:

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[3] Gruhl D., Lu A., Bender W., “Echo Hiding,” in Proceedings of the International Conference on

Info Hiding, pp. 295–315, 1996.

[4] Cox I., Miller M., Bloom J., “Watermarking applications and their properties,” International

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[5] Yeo I. and Kim H., “Modified Patchwork Algorithm: A Novel Audio Watermarking scheme,”

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[9] Wu S., Huang J., “Efficiently Self-Synchronized Audio Watermarking for Assured Audio Data

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[11] Mr. N. R. Bamane, Dr. Mrs. S. B. Patil, Prof. B. S. Patil and Prof. R. K. Undegaonkar, “Hybrid

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March–April 2013

[12] Shefaly Sharma and Jagpreet Kaur, “A Robust and Secure Visible Watermarking Scheme Based

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AUTHORS DETAILS

Govind B. Khatris pursuing M.Tech. in Electronic System and

Communication from Government College of Engineering, Amravati

(Autonomous Institute of M.S.). He received his B.E. in Electronics and

Telecommunication from H.V.P.M.’s College of Engineering & Technology in

2011. He also received Diploma in Electronics and Communication from

Government Polytechnic, Amravati (Autonomous Institute of M.S.) in year

2008. His area of research includes Digital Signal Processing, Wavelet

Processing, Digital Watermarking, and HDL with FPGA.

Dr. Devendra S. Chaudhari obtained BE, ME, from Marathwada

University, Aurangabad and PhD from Indian Institute of Technology,

Bombay, Mumbai. He has been engaged in teaching, research for period of

about 25 years and worked on DST-SERC sponsored Fast Track Project for

Young Scientists. He has worked as Head Electronics and Telecommunication,

Instrumentation, Electrical, Research and in charge Principal at Government

Engineering Colleges. Presently he is working as Head, Department of

Electronics and Telecommunication Engineering at Government College of Engineering, Amravati.

Dr. Chaudhari published research papers and presented papers in international conferences abroad at

Seattle, USA and Austria, Europe. He worked as Chairman / Expert Member on different committees

of All India Council for Technical Education, Directorate of Technical Education for Approval,

Graduation, Inspection, Variation of Intake of diploma and degree Engineering Institutions. As a

university recognized PhD research supervisor in Electronics and Computer Science Engineering he

has been supervising research work since 2001. One research scholar received PhD under his

supervision. He has worked as Chairman / Member on different university and college level

committees like Examination, Academic, Senate, Board of Studies, etc. he chaired one of the

Technical sessions of International Conference held at Nagpur. He is fellow of IE, IETE and life

member of ISTE, BMESI and member of IEEE (2007). He is recipient of Best Engineering College

Teacher Award of ISTE, New Delhi, Gold Medal Award of IETE, New Delhi, Engineering

Achievement Award of IE (I), Nashik. He has organized various Continuing Education Programmes

and delivered Expert Lectures on research at different places. He has also worked as ISTE Visiting

Professor and visiting faculty member at Asian Institute of Technology, Bangkok, Thailand. His

present research and teaching interests are in the field of Biomedical Engineering, Digital Signal

Processing and Analogue Integrated Circuits.

40120130406010 2-3-4-5-6

Technology

Transcript of 40120130406010 2-3-4-5-6