Evaluating the Audio Watermarking Schemes Initiated …acadpubl.eu/jsi/2017-114-7-ICPCIT-2017/articles/7/40.pdf · Broad work has been performed th roughout the years in understanding

Evaluating the Audio Watermarking Schemes

Initiated on Fibonacci Numbers Using Fast Fourier

Transformation 1T. Geetamma and

2J. Beatrice Seventline

1Dept. of ECE, GMR Institute of Technology,

Rajam, India. 2Dept. of ECE, GITAM University,

Visakhapatnam, India.

Abstract

In the present information age, with the fast advancement of

different communication techniques, exchanging digital multimedia

content turns out to be increasingly regular. Be that as it may, the

unlawful duplicate and conveyance of digital multimedia content

has additionally turned out to be less demanding, and a huge

number of creators and distributers protected innovation copyrights

have suffered from violation, which have prompted gigantic harm of

their advantages in numerous applications. Accordingly, individuals

give careful consideration to copyright administration and assurance

these days. Embedding mystery information, known as watermarks,

into multimedia content is considered as a potential answer for

copyright infringement. The key thought is to isolate the FFT range

into short casings and change the greatness of the chose FFT tests

utilizing Fibonacci numbers. Exploiting Fibonacci numbers, it is

conceivable to change the recurrence tests adaptively. Utilizing the

nearest Fibonacci number to FFT magnitudes brings about a hearty

and straightforward system. The test results will demonstrate that

the technique has a high limit and gives heartiness against common

audio signal preparing, for example, included noise.

Key Words:Multimedia security, audio watermarking, fibonacci

numbers, golden ratio.

International Journal of Pure and Applied MathematicsVolume 114 No. 7 2017, 435-445ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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1. Introduction

The development of the Internet, sudden generation of minimal effort and solid

storage gadgets, digital media creation and Tampering innovations have

prompted far reaching imitations of digital reports and unapproved sharing of

digital information. Subsequently, the music business alone cases multi-billion

illicit music downloads on the Internet consistently. In this way, it is

indispensable to create hearty advancements to shield copyrighted digital media

from illicit sharing and Tampering. Illicit duplicating of digital audio have

turned out to be more across the board. As a conventional information assurance

technique, encryption can't be connected in that the content must be played back

in the first style. Under the limitation of indistinctness (SNR, signal-to-noise

proportion, ought to be higher than 20 dB), the watermark in the audio signal

ought to be and have the capacity to oppose common signal handling controls,

for example, MP3 pressure and low-pass sifting (LP). In the audio

watermarking territory, the heartiness against de-synchronization assaults is a

standout amongst the most difficult issues. These assaults cause genuine

dislodging of tests in the time domain. Subsequently, it is exceptionally

troublesome for the watermark to survive. Customary information assurance

techniques, for example, encryption, are insufficient for audio copyright

authorization. Digital watermarking is a prominent system for digital

information security and digital rights administration. Every one of these

prerequisites are in regularly clashing with each other, which makes the outline

of high limit, straightforward and strong audio watermarking plans a testing

errand. Broad work has been performed throughout the years in understanding

the qualities of the Human Auditory System and applying this information to

audio pressure and audio watermarking. As it can be watched, individuals have

a tendency to be touchier towards frequencies in the range from 1 to 4 kHz,

while the limit increments steeply at high and low frequencies. In view of the

HAS, the human ear affectability in higher frequencies is lower than in center

frequencies.

2. Related Work

Few watermarking techniques have as of now been proposed by different

authors which give the concise talk related digital audio watermarking are

given: Mehdi Fallahpour and David Megías [1] built up the audio watermarking

system to install information and concentrate them in somewhat correct way by

changing a portion of the sizes of the FFT range. T. Thiede and W. C. Treurniet

[2] recommended that rather than a steady watermark, a biometric watermark

that is novel to an individual is embedded into an audio. Keystroke Dynamics is

stamped the responsibility for individual to the audio record. Chen and Brian[3]

concentrates on implanting Quantization index modulation in that a class of

provably good methods for digital watermarking and information embedding.

Watermark data into a digital have protest in an indistinguishable attach to

confirm the last mentioned. In [5] W. Li and X. Xue proposes a Content based

International Journal of Pure and Applied Mathematics Special Issue

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localized robust audio watermarking robust against time scale modification

watermarking procedure for audio signals.M. Mansour and A. Tewfik[4]

presented data embedding in audio using time-scale modification.

3. Research Methodology

Visible Watermarking

Visible watermarking alludes to the data visible on the image or video or image.

For instance, in a TV broadcast, the logo of the telecaster is visible at the correct

side of the screen.

Invisible Watermarking

It alludes to including data in a video or image or audio as digital information. It

is not visible or recognizable, but rather it can be identified by various means. It

might likewise be a shape or kind of steganography and is utilized for far

reaching use. It can be recovered effortlessly.

Fragile

Recognition fizzles with even minor Temptation.

Helpful in tampering location.

Common in straightforward added substance watermarking.

Robust

Recognition is exact even under Temptation.

Requirement for strength reliant on utilization of information.

Watermarking Applications

The primary utilizations of digital watermarking are introduced as:

Copyright Protection

Watermarking can be accustomed to ensuring redistribution of copyrighted

material over the endowed system like Internet or shared (P2P) systems.

Content mindful systems (P2P) could consolidate watermarking advances to

report or sift through copyrighted material from such systems.

Content Archiving

Watermarking can be utilized to embed digital question identifier or serial

number to help chronicle digital contents like images, audio or video.

Consequently Embedding the protest identifier inside the question itself

diminishes the likelihood of Tampering and henceforth can be viably utilized as

a part of chronicling frameworks as appeared in fig 1.

Figure 1: Application in contents archiving


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Broadcast Monitoring

Tamper Detection

Digital Fingerprinting

Significance of Audio Watermarking

Audio watermarking is characterized as installing the Audio Watermark in an

audio signal. There are a few applications for audio watermarking including

copyright, security, duplicate assurance, and content confirmation,

fingerprinting and broadcast checking. In the course of the most recent couple

of years, audio watermarking has turned into an issue of critical intrigue. This is

essentially roused by a need to give copyright insurance to digital audio content.

The installed information ought to be perceptually unintelligible to keep up the

nature of the host signal.

Fibonacci Numbers and Golden Ratio

The Fibonacci succession has interested both novices and expert

mathematicians for a considerable length of time because of their bounteous

applications and their omnipresent propensity for happening in absolutely

shocking and random spots. In this venture Fibonacci numbers are utilized for

the first time for audio watermarking.

How Fibonacci Number's Appeared

Today the answer for this issue is known as the Fibonacci succession, or

Fibonacci numbers. There is a little numerical industry in view of Fibonacci

numbers. A scan of the Internet for "Fibonacci" will find many Web

destinations and several pages of material. There is even a Fibonacci

Association that distributes an insightful diary, the Fibonacci Quarterly. On the

off chance that Fibonacci had not specified a month for the infant match to

develop, he would not have a succession named after him. The number of sets

would essentially twofold every month. After n months there would be 2^n sets

of rabbits. That is a considerable measure of rabbits, however not particular

arithmetic.

Fast Fourier Transform (FFT) Fourier Analysis

Signal experts as of now have available to them a great munititions stockpile of

instruments. Fourier analysis is as a scientific strategy for changing our

perspective of the signal from time-based to recurrence based.

Fourier analysis has a genuine disadvantage. In changing to the recurrence area,

time data is lost. When taking a gander at a Fourier change of a signal, it is

difficult to tell when a specific occasion occurred. In the event that the signal

properties don't change significantly after some time that is, whether it is what

is known as a stationary signal this disadvantage isn't essential. Be that as it


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may, most intriguing signals contain various non stationary or passing

attributes: float, patterns, sudden changes, and beginnings and closures of

occasions. These attributes are regularly the most essential piece of the signal,

and Fourier analysis is not suited to recognizing them.

Becoming More Acquainted with the FFT

What is the FFT? FFT = Fast Fourier Transform. The FFT is a faster form of the

Discrete Fourier Transform (DFT). The FFT uses some smart algorithms to do

an indistinguishable thing from the DTF, however in significantly less time.

Alright, yet what is the DFT? The DFT is critical in the zone of recurrence

(range) investigation since it takes a discrete signal in the time space and

changes that signal into its discrete recurrence area portrayal. Without a

discrete-time to discrete-recurrence change it is hard to register the Fourier

change with a chip or DSP based framework. It is the speed and discrete nature

of the FFT that permits us to break down a signal's range with Matlab.

Understanding the DFT

4. Proposed Methodology

Broad work has been performed throughout the years in understanding the

attributes of the human auditory system (HAS) and applying this learning to

audio pressure and audio watermarking. Fig 3 shows the scope of frequencies

and forces of sound to which the human sound-related framework reacts. The

supreme limit, the base level of sound that is perceivable by human ear, is

unequivocally reliant on recurrence. At the level of agony, sound levels are

around six requests of greatness over the insignificant perceptible edge. The

sound pressure level (SPL) is measured in decibels (dB). Decibels constitute a

logarithmic scale, to such an extent that every 6 dB increment speaks to a

multiplying of power. The apparent tumult of a sound is identified with its

force. For the most part, individuals hear sounds as low as 20 Hz and as high as

20,000 Hz. Hearing is best at around 3-4 kHz and affectability diminishes at

higher and lower frequencies, however more so at higher than lower

frequencies. In this way, obviously, by Embedding information in the high

recurrence band, which is utilized as a part of the proposed conspire, the

contortion will be for the most part indiscernible and hence more

straightforwardness will be gotten [11]. In the recommended watermarking

plan, the accompanying algorithm to insert a watermark logo (mystery bit

stream) into the FFT coefficients is utilized.

Figure 2: Run of the mill total limit bend of the human sound-related reaction


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In the algorithm recommended in this venture, a piece of the recurrence of FFT

range is chosen for inserting the mystery bits. The chose recurrence band is

partitioned into short edges and a solitary mystery bit is inserted into each

edge[1]. As said over, the FFT is utilized to outline a plan in numerous

watermarking frameworks. To the best of our insight, this is the principal audio

watermarking strategy in light of Fibonacci numbers. All watermarking

strategies depend on test results to demonstrate the loyalty of watermarking

framework. Be that as it may, in this article, notwithstanding the exploratory

outcomes,the devotion of recommended framework is demonstrated

numerically.

Tuning

The proposed framework gives two parameters to change three properties of the

watermarking framework. The recurrence band, and the casing size (d) are the

two parameters of this strategy to tamper limit, perceptual twisting and

heartiness. In this plan, general tuning rules which can achieve the prerequisites

or to draw near to them fast are utilized. The edge measure has more impact on

strength, though the recurrence band has more impact on straightforwardness

and limit. At the end of the day, by expanding the edge estimate better power is

accomplished. Besides, expanding the recurrence band prompts better limit and

more twisting.

Fig 3. demonstrates the flowchart for the determination of the tuning

parameters. In the instatement, fl is 12 kHz, fh is 16 kHz and is 5. This

flowchart encourages conforming the parameters in light of the necessities. In

any case, changing the parameters in view of a few requests is extremely

troublesome and considering an exchange off between limit, straight

forwardness and vigor is constantly vital.

Figure 3: Flowchart of the tuning procedure

Embedding the Secret Bits

The recurrence band and the edge measure (d) are the two required parameters

in the installing procedure which must be balanced by the prerequisites. In this

segment, for effortlessness, the direction of these parameters is not examined

and simply think of them as settled. The impacts of these parameters are broke

down in the trial comes about part. For embedding the watermark stream, first


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the FFT is connected to the audio signal and afterward, the FFT tests are

adjusted in light of Fibonacci numbers and the mystery bits. At last the converse

FFT is connected to create the checked audio signal. The inserting steps are

itemized underneath.

1. Apply FFT to figure the FFT coefficients of the audio signal. The

entire record (for short clasps, e.g. with short of what one moment)

or pieces of a given length (e.g. 10 seconds) for longer records.

2. Isolate the FFT tests in the chose recurrence band into edges of size.

3. For all the FFT tests in the present edge, locate the biggest Fibonacci

number fibn,i , the nth Fibonacci number for ith FFT test, which is

lower than the extent of the FFT test . It is worth to specify that the

accompanying Fibonacci set is utilized:

F = {1,2,3,5,8,13,21,34,55,......}

In the first Fibonacci set there are two ones, one of which is

evacuated in our algorithm.

4. The marked FFT samples are obtained by using Equation 1

(1)

5. Finally, use the inverse FFT to obtain the marked audio signal

Figure 3.1: Flowchart of the embedding algorithm

By developing the recurrence band, the limit and contortion increment and vigor

diminishes. Additionally, expanding the casing size, qualities the power against

assaults and decreases the limit. Likewise, the utilization FFT magnitudes

brings about better vigor against assaults contrasted with the utilization of the

genuine or the non-existent parts as it were. Above Fig 3.1 gives the flowchart

of the Embeddingalgorithm.


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5. Results and Analysis

Parameters

a) Peak Signal-to-Noise Ratio (PSNR): It is the proportion between the most

extreme conceivable energy of a signal and the energy of adulterating noise that

influences the devotion of its portrayal. It is the most effortlessly characterized

through the Mean Square Error (MSE).

PSNR (dB) = 10log10 (2552/MSE) (2)

b) Normalized cross connection (NCCR): It is the Correlation between the

watermark image W and extricated watermark image W'. In the event that the

estimation of NC is more like 1, W and W' are more comparative.

(3)

c) BIT ERROR RATE: The bit mistake rate (BER) is the number of bit blunders

per unit time. The bit blunder proportion (additionally BER) is the number

Table 1: For different audio signals Peak signal to noise ratio and correlation coefficient are

calculated

Number of samples present in the audio

Watermark Watermarked audio

PSNR Correlation factor

201600

[Audio_1](sterio)

Binary data

51.6

1

185317

[ Audio_3](sterio)

Binary data

55.18 1

165360

[Audio_4 ](sterio)

Binary data

56.32 1

174576

[ Audio_5] ](sterio)

Binary data

57.58 1

217728

[ Audio_2](mono)

Binary data

76.41 1


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From above table 1 it is clear that Audio_1 (stereo) and Audio2 (mono) shows

better PSNR values compared to before methods. In brief Audio_1 is stereo

signal it has two channels which get affected by watermark so the PSNR of

stereo signal is less than mono (audio_2) signal.

6. Conclusion

This paper indicates distinctly enchanting the benefit of Fibonacci numbers, it is

conceivable to change the recurrence tests adaptively. Utilizing the nearest

Fibonacci number to FFT magnitudes brings about a strong and straightforward

system. The casing size and the chose recurrence band are the two flexible

parameters of this framework that decide the perceptual twisting and the power

exchange off of the framework precisely. Moreover, the proposed plan is

visually impaired, since it needn't bother with the first signal for separating the

concealed bits. Furthermore, the proposed strategy is very powerful and shows

great outcomes for mono signals contrasted with before techniques.

Future Scope

Watermarking field has such a variety of advances each day another procedure

is utilized presenting a temperate approach for taking care of the current issue.

The benefit of Fibonacci numbers, it is conceivable to change the recurrence

tests adaptively so it can be stretched out to video watermarking in view of

Fibonacci numbers utilizing Fast Fourier Transform, Discrete Wavelet

Transform and Dual Tree Complex Wavelet Transform.

References

[1] Fallahpour M., Megías D., Audio Watermarking Based on Fibonacci Numbers, IEEE Trans. Speech Audio Process 23(8) (2015).

[2] Thiede T., Treurniet W.C., Bitto R., Schmidmer C., Sporer T., Beerens J.G., Colomes C., Keyhl M., Stoll G., Brandenburg K., Feiten B., PEAQ-The ITU standard for objective measurement of perceived audio quality, J. AES 48(1/2) (2000), 3–29.

[3] Chen B., Wornell G.W., Quantization index modulation: A class of provably good methods for digital watermarking and information embedding, IEEE Trans. Inf. Theory 47(4) (2001) 1423–1443.

[4] Mansour M., Tewfik A., Data embedding in audio using time-scale modification, IEEE Trans. Speech Audio Process 13(3) (2005), 432–440.


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[5] Li W., Xue X., Content based localized robust audio watermarking robust against time scale modification, IEEE Trans. Multimedia 8(1) (2006), 60–69.

[6] Wang X.Y., Zhao H., A novel synchronization invariant audio watermarking scheme based on DWT and DCT, IEEE Trans. SignalProcess. 54(12) (2006), 4835–4840.

[7] Kalantari N.K., Akhaee M.A., Ahadi M., Amindavar H., Robust multiplicative patchwork method for audio watermarking, IEEE Trans. Audio, Speech, Lang. Process. 17(6) (2009), 1133–1141.

[8] Fallahpour M., Megías D., DWT–based high capacity audio watermarking, IEICE Trans. Fundam. Electron.,Commun. Comput. Sci. (2010), 331–335.

[9] Kang X., Yang R., Huang J., Geometric invariant audio watermarking based on an LCM feature, IEEE Trans. Multimedia 13(2) (2011), 181–190.

[10] Fallahpour M., Megías D., Secure logarithmic audio watermarking scheme based on the human auditory system, Multimedia Syst. (2013).

[11] Yüksel Tokur, Ergun Erçelebi, Spread Spectrum Audio Watermarking Scheme Based On Psychoacoustic Model, Gaziantep University, Electrical & Electronics Engineering.

[12] Fallahpour M., Megías D., High capacity robust audio watermarking scheme based on FFT and linear regression, Int. J. Innovative Comput., Inf. Control 8(4) (2012), 2477–2489.

[13] Fallahpour M., Megías D., High capacity audio watermarking using FFT amplitude interpolation, IEICE Electron. Express 6(14) (2009), 1057–1063.


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Evaluating the Audio Watermarking Schemes Initiated …acadpubl.eu/jsi/2017-114-7-ICPCIT-2017/articles/7/40.pdf · Broad work has been performed th roughout the years in understanding