HIGH SECURITY AUDIO

WATERMARKING USING FIBONACCI SERIES WITH IMAGE ENCRYTION

Vijetha Kura, Buchhibabu Rachakonda

Assistant professor,

Student

Electronics and

communication department, Matrusri engineering college, Hyderabad, India

Electronics and

communication engineering , Matrusri engineering college ,Hyderabad ,India

Abstract:

This

paper will present a highly secured, very large capacity audio watermarking

system in which watermark/data i.e. an image can be encrypted and hidden by

modifying the magnitude values in FFT spectrum. The main idea is to modify

magnitude of FFT samples with respect to Fibonacci series .Here an image will

be encrypted and is embedded into audio which improves the security of

watermarking drastically. XOR sum of image bits and PN sequence is used as

embedding bit stream and is chosen as it is fastest and simple in computation

when compared to other encryption techniques. This technique mathematically proves

that maximum changes in FFT samples is less than 61% and the average error rate

considering a single sample is 25% .This technique is not only robust and

transparent but also highly secured. The additional feature is its ability to

handle large capacity i.e from experimentally its proved to handle 700bps to

3kpbs efficiently, moreover this technique is blind, i.e. original signal is

not required near receiver.

Index Terms: audio watermarking , FFT, image encryption,

XOR sum,

I. INTRODUCTION:

In this

progressive era in which everything changes faster than the prick of the eye,

inventions followed by exploitation of its weakness is a casual scenario. One

of the important affected field is digital audio. As distribution of audio has

became very easy, which paved way for illegal distributions by which huge

intellectual scholars, authors suffered heavy losses due to copyright

infringements by illegitimate methods followed by criminals.

Digital

watermarking is a simple process in which a watermark which can be audio/Image

can be embedded into the host signal and can be later extracted and used for

multiple purposes.

Audio

watermarking has four different significant properties.

1.

Imperceptibility:

It is defined by the quality of the embedded signal i.e after adding watermark

in terms of objective and subjective measures.

2.

Security:

The basic theme of the security is it should broadcast any clue from the

embedded signal. Security of a watermark defines how well it is ready to face

different attacks. The stronger the encryption the stronger is the security.

3.

Robustness:

Robustness of and audio watermarking is defined by its ability to withstand

different types of attacks on embedded signal.

4.

Payload:

Here the payload is simply watermarking bits .It’s usually measured in bps i.e.

bits per second. The payload can be defined as the number of bits that can

embedded into host audio signal without losing significant imperceptibility of

the audio.

There

are different techniques available which includes D.C.T, M.D.C.T, Walsh

Hadamard-D.C.T, and D.W.T, in which imperceptibility and reduction of noise are

considered as main theme. This paper considers all properties i.e. imperceptibility,

security, robustness and payload and stands at the center of the tradeoff

triangle.

Watermark

can be embedded using different techniques.

1)

Time

domain

2)

Frequency

domain

Watermark

can be embedded in frequency domain using different transforms i.e. FFT, DCT

,MDCT , Arnold DWT,DWT-DCT etc. out of which FFT is simple in complexity and

fastest out of them. One of the added advantage of FFT is its translation

invariant property.

II. LITERATURE SURVEY

2.1 Fibonacci series:

Fibonacci

series origin takes place us to a scenario when Leonardo Fibonacci was resting

in a garden and watching rabbits, wondering how many rabbits would be born in

future if two rabbits mate and multiplication take place into their next

generation.

This

wonder number has distinguished qualities which has multiple applications which

include apple design logo, Benz logo .Our ear has a unique shape which can be

attributed to Fibonacci series.

All

this logos and different designs are designed using golden ratio i.e. 1.618, if

two numbers/Quantities are defined are golden ration if their ratio is equal to

the sum of the quantities to larger quantities.

Consider

a, b (a>b)

Then

these two quantities are said to be in golden ratio if

(a+b)/a = a/b

Proofs:

Theorem

1: The typical maximum distortion that gets embedded in the FFT samples

(magnitude) using this algorithm is between the span of 0.38 to 0.61

Proof:

if ‘l’ is converted to Fn+1

Then

Max error rate = Max error / Fn+1

=(Rn-1)Fn/Fn+1

=(Rn-1)Fn/rnFn

=Rn-1/Rn

If

‘l’is converted to Fn

Then

Max error rate =Max error/Fn

=Rn-1

Here,

If we assume the general /typical value of Rn it is i.e golden ratio the max

error would be between 0.38-0.61. it indicates that the maximum error rate is

0.50.

Now

if we consider that fft values have equal probabilities then the average error

rate is 0.25 which indicates the average change per fft value is 25% only.

Therefore

it has good imperceptibility.

2.2 Image encryption:

There

are different types of image encryption process available for different

purposes. Some of the famous techniques are chaotic image encryption, rubics

cube based image encryption ,steganography and many more out of which

encrypting with PN sequence is simpler and faster one. As our main concern is

to provide good security with fast computation encryption with PN sequence is

chosen.

Stenography

is the art of hiding information in a image, this can be done by varying the

gray values of the original image and then encrypting the information in it.

Now a days it is popular because of its robustness.

Image

encryption can be of three types i.e. block permutation in which image is

divided into separate blocks and then permutated, in pixel permutation the

image pixels are permutated to create encrypted image.

In

block permutation image is divided into multiple parts and they are permutated

with proper techniques and they are inserted embedded into host signal

In

pixel permutation the image pixels are permutated with a secret key and them

embedded in the host signal .This method is widely used due to its robustness

and high fidelity.

In

bit permutation bits are permutated and then embedded into host signal, this

method is highly reliable due its complexity to decrypt compared to others.

In this paper first the pixel gray value are

noted down and then converted into bits, Then XOR sum of grey values and

pseudo-random bit stream is generated.

A

simple XOR of watermark bits and PN sequence would produce same number of

watermark bits as original but XOR sum of watermark bits and PN sequence would

reduce drastically, the total number of watermark bits present after encryption

when compared to initial watermark bits available.

This

helps in increasing the payload or capacity of the audio watermarking without a

significant side effects.

This

generated bit stream is embedded into different frames of FFT coefficients that

are created after choosing required parameters (Frame size and bandwidth). The

FFT co-efficient are manipulated or changed w.r.t Fibonacci numbers and the bit

that is going to get embedded. This is main principle of embedding bits.

2.3 Tuning:

The

quality of watermarked audio is decided by few parameters i.e. Objective

degradation (ODG) , BER (Bit error rate) , payload (capacity) .,etc.

The

parameters can be varied to required values by altering two characteristics of

the algorithm i.e frequency bandwidth (Fl,Fh) and frame size (d).

Fl

– Lower frequency limit

Fh

– Higher frequency limit (Default value of fl=12 kHz and fh=16 kHz and d=5 is

considering Human auditory response).

The

default value can be as low as 10 kHz considering Human auditory response,

similarly 16 kHz is an average peak frequency in most of the audios.

Initially

by setting default parameters we should vary the characteristics as per our

requirements .If we look carefully we can observe that all the parameters are

interlinked to each other. This indicates the tradeoff triangle .Security, functionality,

imperceptibility are the three corners of the tradeoff triangle. This trade of

triangle is limited only to certain frequency bands and frame sizes, i.e.

varying them smartly we can overcome the inefficiency in tradeoff triangle.

Below figure describes about tuning and its

interrelation between different parameters.

Fig 2.1

Tuning parameters

III. PROPOSED:

This

paper is extension to audio water marking using Fibonacci series , Here instead

of embedding simple bits we are embedding an image into the audio (Encrypted

image).

This

is achieved by first encrypting the image with PN sequence generated by PN

generator (PNRG) i.e. XOR sum of image bits and pseudo random bit stream and

embedding them into frames that are obtained after applying FFT to original audio.

By using XOR sum the payload or capacity can be increased drastically.

3.1 Watermarking process:

3.1.1 Encryption:

1)

First the frame size and frequency band length will be received securely to the

receiver.

1)

First convert the given audio into frequency domain signals, i.e apply FFT

2)

Now select the co-efficients which falls between the selected frequency band.(

Fl, Fh)

3)

The above step can be completed using different bandpass filters

4)

After filtering, arrange all the co-efficients according to the frame size d.

5)

Here 0 and 1 are removed from Fibonacci series in this process i.e.

Fk={1,2,3,5,8,13,21,34,55,…}

Here

k=1, 2, 3, 4, 5,… n.

6)

Now add the watermark signal to the FFT co-efficient according to Fibonacci

numbers and bit that to be embedded.

7) Formulae:

f ‘ = fib(k,i) , if k modulus 2=0 and wl=0

fib (k+1,i) , if k modulus 2 =1 and

wl=1

Similarly

f’ =

fib(k+1,i) , if k modulus 2 =1 and wl=1

fib (k,i) , if k modulus 2 =1 and wl=1

Here

k represents kth Fibonacci number

8)

Repeat the same process to all the FFT co-efficients in the frame

9)

Repeat the same process to all the frames available.

10)

Finally apply IFFT

11)By

this step encryption is completed

3..1.2 Extracting /Decrypting:

1)

Apply FFT to watermarked signal as the operations are to performed in frequency

domain.

2)

Divide the samples with given frame size d

3)

Now change the FFT magnitude of given samples approximating to Fibonacci series

according to given formulae

4)

Formula: D(i)= 0 , if k modulus 2 = 0

1 , if k

modulus 2 =1

5)

Now by polling method we can decide whether it is 1 or 0 , if the number of

samples found as zero out of half of the samples present in the frame , Then

its is considered as 0 else 1 .

Considering

there are 6 FFT co-efficients in a frame and 4 of the FFT Co-efficients when

decoded gives zero then the water bit embedded in the frame is ‘0’.

6)

Now re-frame the encrypted image by using the extracted watermark bits

7)

Now decrypt the image by using PRNG i.e XOR sum (same method) , The decryption

cannot be done without the seed of XOR

8) We

will get the decrypted image as output.

Fig

3.1 Encryption flowchart

IV. EXPERIMENTS RESULTS:

4.1 Transparency,

Robustness, capacity, security:

Signal

to noise ration and ODG is used to measure imperceptibility, I.e greater the

SNR greater the imperceptibility similarly if ODG=0 ODG (Objective

degradation).it indicates that there is no degradation, if ODG=-4 then it

indicates very annoying distortion is present in the watermarked signal.

Similarly

SDG (Subjective degradation) is five-point subjective grade i.e if S.D.G =1 it

is excellent and if S.D.G =1 it’s ridiculous

BER

(Bit error rate):%BER = Number of error bits / Total number of bits

The

first layer of security in this algorithm is its frame size and frequency band length,

without knowing these two parameters it is merely impossible to decrypt the

watermark.

The

second layer of security is the seed of PNRG that is required at both at sender

and receiver end without which decryption would be practically impossible.

The

embedding rate or capacity can be increased by either increasing the frequency

bandwidth (Fl,FH) or decreasing the frame size.

Aftermath

has little side effects as some of the security is sensitive to certain attacks

if frequency bandwidth is significantly increased.

Below table shows the trade between capacity,

transparency and frame size which decide the quality of encryption.

Table

4.1 Results of 5 Mono signals

Table

7.2 Robustness test results of two selected files

Below indicates the reason why we have chosen

only Fibonacci series in this encryption algorithm.

Table 7.3

Comparisons between different series that are generated with different k values

(K- ratio)

Below table show comparison of different methods

Table 7.4 Comparisons between different

methods

Note:

The above tables are constructed with reference to 27

V. CONCLUSION:

In

this paper a high security, high capacity, robust, transparent, algorithm to

encrypt watermark into audio is presented. The image is encrypted by generating

bit stream of sum of XOR of pixel values and pseudo random sequence (generated

by PRNG) and this method is blind as we do not require original signal during decryption.

By using XOR sum for encryption the payload capacity is drastically increased.

The two deciding factors to change the above parameters are the frame size and

frequency bandwidth This paper provided proof that the maximum change of FFT

samples is less than 61% but average change of a single FFT sample is just

below 25%.Experimental proofs shows that this algorithm can handle capacity

ranging from 700bps to 3kbps and is robust against all common signal processing

attacks.

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Vijetha Kura is currently

assistant professor in matrusri engineering college had completed M.tech and

has 8+ Years’ experience in teaching field , published 5 papers on different

topics and the areas of research interest

are VLSI , Digital Communication , Digital encryption techniques , Data

processing techniques.

Buchhibabu Rachakonda is a student

pursuing graduation in Matrusri engineering college, Hyderabad, India.