A two-layer Audio Encryption system using Quaternion

Abstract: With the ever-extending need for data security and

integrity, new cryptographic systems are developed to ensure the

same. This paper proposes a novel cryptographic system wherein

an audio message is encrypted such that it is encrypted using

multiple layers of security such as, a standard encryption process

and also an additional steganographic process with the help of

Quaternion domain mathematical representation of numbers

which helps in transforming the audio file to an image which could

be used for steganography. Least-Significant-Bit encoding

algorithm is used to mask the cipher image with a cover image and

consequently Chaos encryption algorithm is used for encrypting

the steganographic image making it transmission ready. An image

is used as a variable key to encrypt the audio file by using each

individual pixel of the image as a key for a particular sound sample

in first layer followed by steganography and chaos encryption as a

second layer of security thus increasing the robustness of the whole

process manifold. The performance parameters such as PSNR and

MSE are used to analyse the quality of the images obtained through

the cryptographic processes at each stages of decryption involving

extraction of the steganographic image and the sound embedded

image. Correlation coefficient and MSE are used to compare the

obtained and original audio message. To develop and implement

this particular cryptographic system, MATLAB software has been

used.

Keywords: Cryptography, Steganography, Audio Cryptography,

Chaos Algorithm, LSB Algorithm, MSE, PSNR.

I. Introduction

Cryptography is an essential tool for maintaining the secrecy

and integrity of communication between two parties in the

presence of any eavesdroppers. With exponential

development in communication technologies, Cryptography

plays an invaluable role in transmitting, securing and

authentication of information shared across public channels of

communication. Cryptography is a complex establishment

that is a confluence of mathematics, informatics and electrical

engineering disciplines. This paper presents a new technique

for a symmetric encryption/decryption to protect the audio

signal and thus ensure end-to-end confidentiality of speech or

audio in communication systems. The proposed methodology

uses

keys that is different from keys used in other popular

algorithms on two folds. Firstly, the secret key in this method

is not a single unique value but instead it is a collection of

different values that add up to from a digital image. The

second difference is the mathematical approach used in for the

encryption and decryption processes is different from any

algorithms in use. The performance and the efficiency of the

method will be measured and analyzed using performance

parameters such as MSE, PSNR and correlation coefficient

[4].

This paper proposes a novel method for the encryption and

decryption of an audio signal using a digital image and its

RGB components as a set of keys. The audio samples and the

RGB components of every pixel in the key image is

represented as a Quaternion domain number and the two sets

of quaternions are added correspondingly. This quaternion

number is then normalized and further operated upon adding

multiple layers of security. It is then transmitted and when

received, the value of sound sample is extracted by multiple

processes of decryption and by using quaternion mathematics

[1]. The intermediate layers of security that are incorporated

in this technique are steganography of the image and chaos

encryption scheme. The proposed algorithm is designed and

implemented by using MATLAB software.

A two-layer Audio Encryption system using

Quaternion Transform and image as a Variable

Key

Prithvik H C1, Naman Jain2 and Rakesh K R3

1 Department of Telecommunication Engineering, RV College of Engineering,

Mysore Road, Bengaluru-560059, India

[email protected]



[email protected]



[email protected]

Wutan Huatan Jisuan Jishu

Volume XVI, Issue V, May/2020

ISSN:1001-1749

Page No:477

II. Cryptography and Steganography Concepts

A. Cryptography

Cryptography is process of protecting data by manipulating it

using various mathematical operations and keys to lock and

unlock the information, so that it can only be interpreted and

processed by the intended sender and receiver. Thus,

preventing the unauthorized access to information. The

"crypt" prefix means "writing" and the "graphy" suffix means

"writing”.

The techniques used to encrypt/decrypt information in

cryptography are made from mathematical principles and

algorithms to translate messages in ways that make it difficult

to discern and decode [11]. This encryption techniques and

algorithms encapsulates a complex and various layer of

security such as key generation, digital signature,

authentication, confidentiality etc. Cryptography is widely

classified into two and they are as follows.

1) Symmetric Key Cryptography

The earliest known usage of this type of cryptography was in

the Roman Empire by Julius Caesar who used it to send secret

messages. In general, symmetric encryption is a faster method

than asymmetric one. It often uses the same common private

key, also referred to as a common encryption key, both for

encrypting and deciphering the message back into plain text.

The secret key may be as simple as a number, letter string or

a combination of the two. Only the exchanging parties should

know the word. That needs confidence among the exchanging

parties. Good key management therefore plays an important

role for this form of encryption to ensure the safe exchange of

information.

Although key management is a problem, its effectiveness

against brute force attack is determined by the algorithm used

to generate the key. The key lengths decide how many

attempts it will take to guess the key by attempting randomly

generated keys before one works. Longer keys produce

stronger algorithms. Through increasing the length of the

word, it is exponentially more difficult to speculate. Even so,

a brute-force attack will ultimately solve all of the symmetric

algorithms. Today we are talking about days, months, years,

hundreds of years, thousands of trillions or more than the

known universe existed in some instances.

The brute force system prevents replication of any failed

attempts by only one attempt on each potential key to

minimize the time of a successful attack [15]. For this reason,

good key management best practice is to restore a new key in

less time than a correct guess will take for an attacker. Thus,

there is a fair risk that the new key is already omitted from the

list of potential key combinations by an intruder. This is why

key management when working with symmetric keys is

important.

2) Asymmetric Key Cryptography

Asymmetric key cryptography protects us from the burden of

sharing the same or common secret key. Rather, we use an

Asymmetric pair of keys that consists of a private key and a

public key. Let's continue with the plain text. That's going into

the algorithm for encryption. Now there are several widely

recognized encryption algorithms. Therefore, the key is what

makes encryption special.

The public key encrypts the message into unreadable text,

which needs the corresponding private key to decrypt the

message back into the literal. To be considered as a good

Encryption algorithm, we should not be able to work out the

private key from public key or from the cipher text and

without the private key we should not be able to decipher the

plain text from the cipher text. Then, with a decryption

algorithm and a key, when we need to use this text again or

once it is sent to the recipient, the reverse happens. This time,

the algorithm runs through the cipher text and the key, and

again results back to our plain text. The difference is, sharing

your public key with others is safe; think of it as an email

address for the general public.

B. Quaternions

Quaternions forms a complex and a fascinating algebra where

each object comprises of 4 scalar variables [5], these entities

can be added and multiplied as a single unit in a similar

manner to the normal algebra of numbers. However, on the

contrary, the algebra of scalar numbers a*b is not always equal

to b*a (where a and b are quaternions). Quaternion

multiplication is not commutative in nature. The quaternion

domain is four dimensional (each quaternion consists of four

scalar numbers), one real dimension and three imaginary

dimensions. The unit value of each of these imaginary

dimensions is square root of -1, but they are different square

roots of -1 and are all mutually perpendicular to each other,

and represented as i, j and k [9]. A quaternion number system

is shown in fig. 1 and it can thus can be represented as (1):

𝑄 = 𝑤 + 𝑥𝒊 + 𝑦𝒋 + 𝑧𝒌 (1)

Where,

𝑖2 = 𝑗2 = 𝑘2 = 𝑖𝑗𝑘 = −1 (2)

Figure 1. Quaternion representation

C. Steganography

Steganography is the art and process of hiding secret messages

using a cover message in a way that no one knows the

presence of the message but the sender and intended recipient.

This is derived from two Greek words, "steganos," meaning

covered and "graphia" means writing. Steganography is an

ancient technique, which has been in practice for thousands of

years in different ways to keep interactions a secret. The Fig.

2 depicts a basic steganographic model used to hide message

in an image.



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Figure 2. Steganography representation

As seen in the image, the cover file (X) and secret message

(M) are given in as input to the steganographic encoder.

Steganographic Encoder function, f (X, M, K) embeds a cover

file with the hidden message. The steganographic object result

looks very similar to the cover image, without any noticeable

changes. Stego-object is fed into Steganographic decoder to

retrieve the hidden message [3].

Steganography as well as cryptography have almost the

same function, which is to protect a third-party attack on the

information. Therefore, the best of the two worlds are used to

secure the information. Cryptography switches the cipher-text

information that cannot be interpreted as information without

the usage of a proper decryption key. If someone tries to

intercept this encrypted message, they could easily see that

there has been an encryption system in place. Whereas,

steganography does not alter the format of the details but

disguises the presence of the message.

III. Methodology

A. Embedding audio in image

The audio file is taken and it is samples at regular intervals

using standard sampling rate. At the same time a digital image

is selected which is RGB in nature and has number of pixels

which are either equal to greater than the number of samples

in the audio file message. The sound samples are converted

into a quaternion number where the real part of the number is

marked as zero and the coefficient of i is assigned to the sound

sample value. This quaternion number is defined as q1 (3).

𝑞1 = 0 + (𝑠𝑜𝑢𝑛𝑑 𝑠𝑎𝑚𝑝𝑙𝑒)𝒊 + (0)𝒌 + (0)𝒌 (3) The n pixels’ values of the key image that are taken, each

act as a key. Each such pixel is converted into a quaternion

number with the real value as zero and the i, j and k co-

efficient values set with the R, G and B values of each pixel

respectively. Each such quaternion number is labelled as q2

(4).

𝑞2 = 0 + (𝑅)𝑖 + (𝐺)𝑗 + (𝐵)𝑘 (4)

The final quaternion number qT is the addition of the numbers

q1 and q2.

𝑞𝑇 = 𝑞1 + 𝑞2 (5)

This added quaternion number which holds the final value qt

might exceed the range of the image pixel value due to

addition, so this value is rescaled back to 0-255 and then a

matrix is created which is then transformed into an image.

The flow of this algorithm can be represented by the

flowchart in Fig. 3.

`

Figure 3. Sound embedding in image flowchart

B. Steganography

The digital image can be defined as a set of values called

pixels. Pixels are the smallest individual item of an image,

carrying values that at any particular point represent the

intensity of a given color. So, we can think of an image as a

matrix of pixels (or a 2-dimensional array) that contains a

fixed number of rows and columns [3].

In using the word "visual image" here, we refer to the

"raster graphics," which are essentially a dot matrix data

structure, representing a grid of pixels which, in effect, can be

stored as the smallest individual element of an image in image

files with varying formats of pixels. So, every pixel is a sample

of an original picture. It says, more samples have more reliable

original representations. Each pixel is variable in intensity.

For color images, three intensity components such as red,

green, and blue or cyan, magenta, yellow, overlap on one

another to give a color image. We're going to be working with

the RGB color standard here. The RGB color pattern, as you

might imagine, has 3 components or channels: red, green and

blue.

Audio

signal

Construct ‘n’ audio

sample frames

qt= q

2 + q

1

Get n pixels and split

the RGB values

from key image

Digital

image

Convert each

sample to

quaternion number

(q2)

Convert RGB

components to

quaternion number

(q1)

Normalize to

generate an

RGB image



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The RGB color model is an additive color model in which

red, green and blue light are fused together to replicate a wide

variety of colors in various ways. The RGB color model's

primary function is to identify, reflect and view images in

electronic devices such as televisions and computers, although

it has also been used in traditional photography. Hence, every

pixel of the image is made up of three components i.e., red,

green and blue which are eight-bit values (ranges from 0 to

255) as shown in Fig. 4.

Figure 4. RGB pixel value representation

The most significant bit (MSB) is the leftmost bit. Higher

value is stored in the MSB and if the leftmost bit is changed,

it has the largest impact on the final value of the pixel. For

example, if we change the MSB of an 8-bit value from 1 to 0

(11111111 to 01111111), the change is drastic and it will alter

the decimal value from 255 to 127 i.e., it loses nearly half its

value.

The least significant bit (LSB) is the rightmost bit and holds

a comparatively lower value. If we alter the bits that are

towards the right end, the final value would be affected. For

example, if we change the LSB from 1 to 0 in an 8-bit value

(11111111 to 11111110) then the decimal value will change

from 255 to 254 only. Notice that the rightmost bit can only

change 1 in a total of 256 (it's less than 1 per cent).

In this application, the last four LSB bits are discarded to

accommodate the secret message and during reconstruction

the last four bits are assigned with zeros. The flowchart

depicting LSB algorithm with an example is shown by Fig. 5

[1].

Figure 5. LSB Steganography algorithm flowchart

C. Chaos Encryption Algorithm

Chaos theory is an area of mathematics that deals in many new

applications such as EEG neurology [18], embryonic chick

heart cell cardiology, weather prediction and Direct Sequence

Code Division Multiple Access method etc. 'Chaos' means a

disordered state which is extremely responsive to the initial

conditions. A minor change in initial conditions produces a

totally uncorrelated sequence. Most researchers have shown

that the chaos sequences can be used to encrypt images [15].

Logistic map function is a chaos function that has high

responsiveness to the initial state, the generated sequence is

non-periodic and unpredictable pseudo-random sequence for

proper choice of bifurcation parameter 'r.' The benefits of

using Chaos theory explicitly for image encryption are easy to

implement, computationally quicker and unassailable.

1) Logistic Map Function

Logistic map is a function that generates non-periodic

sequences in one dimension where the value of Xn lies

between zero and one and is random in nature. The equation

(6) of the logistic map is given as:

𝑋𝑛+1 = 𝑟 𝑋𝑛(1 − 𝑋𝑛) (6)

In this equation the parameter r is termed as bifurcation

parameter which lies in the range of zero to four and X0 is the

initial value which lies in the range of zero to one and the

subsequent elements are generated using the equation (6). The

bifurcation diagram is represented by Fig. 6.

Cover image Image to be hidden

Extract Each pixel

value

Extract Each pixel

value

Swap LSB bits of

cover image with

MSB bits of

image to sample

image

New cover image

pixel value

10011100

Ex: 11001101 Ex: 10011011

Take MSB bits



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Figure 6. Logistic Map bifurcation diagram

The diagram shows that when the value of r lies in the range

of 3 to 3.5, the function oscillates between two values and

when the value of r is changed to lie in between 3.75 and 3.99,

the function produces a highly random sequence which

oscillates between different values and thus produces a true

random sequence for encrypting data effectively.

2) 8-bit LSFBR Sequence

The most commonly used linear function of single bits is OR

(XOR). An LFSR with a well-chosen feedback function,

however, can generate a sequence of bits that appears random

and has a very long duration. Consequently, an LFSR is most

often a shift register whose input bit is powered by the XOR

of other bits of the total shift register value.

The initial value of the LFSR is called the seed, and since

the register operation is deterministic, the value stream

generated by the register is completely determined by its

current (or prior) state. Since the register has a set number of

possible states, the sequence will inevitably repeat itself after

a cycle [11]. In an 8-bit Linear Feedback Shift Registers, there

are 255 possible initial states. Each of those initial states can

generate a periodic sequence of 255 values. The flow of the

algorithm is shown by the flowchart Fig. 7.

Figure 7. Flowchart of Chaos Algorithm

The encryption algorithm takes the MxN pixel values of the

image and transforms it into an array of the same. It takes the

same MxN values of the key from the logistic map function

and converts it also into an 8-bit array by multiplying the value

of the map function by 255. It arranges these values in an array

too. The algorithm then takes K2 key values from the 8-bit

LFSBR and arranges it in an array. It then XORs the K1 and

K2 values and generates a final key value K. This is done by

(7).

𝐾𝑖 = 𝐾𝑖,1 ⊕ 𝐾2,𝑖 (7)

The pixel value of the image Pi is XORed with the key value

obtained and outputs the encrypted pixel value Ci. This is

shown by (8).

𝐶𝑖 = 𝑃𝑖 ⊕ 𝐾𝑖 (8)

It then repeats the algorithm in a loop to encrypt all the

pixels and generated a final image by combining all the C i.e.,

encrypted pixels in the form of an image.

The decryption process happens by taking the encrypted

pixel array and by obtaining the Ki key sequence values from

the K1, i and K2, i bit blocks. The decrypted pixels are defined

by XORing the key and the encrypted pixel value blocks

defined by (9). The obtained image is thus the decrypted

image.

𝐷𝑖 = 𝐶′𝑖 ⊕ 𝐾𝑖 (9)

D. Steganographic Decryption

After achieving the chaos decryption, the image file is taken

and split into its RGB components. Each pixel value ranging

between 0-255 are represented as an 8-bit binary value. The

four LSB bits of each pixel that corresponds to the sound

embedded image is separated and recorded. Then, the sound

Generating K1 key

values by using the

logistic map function

Generating K2 key

values by using the

LFSR of 8-bit

XOR

Convert into

8-bit binary

Convert

into 8-bit

binary

KEY

IMAGE XOR

Encrypted

Image



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embedded image is reconstructed using the recorded values

and the image further undergoes mathematical operations to

extract the audio.

E. Sound Extraction from Obtained Image

The extraction process of the sound signal form the image

using the variable key is done as shown in the flowchart given

by Fig. 8. The key values from the pixels of the original key

image is taken and converted into a quaternion number. The

received image file is taken and its pixel values are also

extracted and converted into a quaternion number with the real

part as zero [5]. Subtraction operation is done of the two

quaternion numbers and the obtained complex value is then

converted into a real number form. We thus obtain the audio

signal samples. Using these we then reconstruct the audio

signal and obtain the secret message which was transmitted by

the user by using the above defined encryption system.

Figure 8. Block diagram of sound extraction

F. Performance Parameters

At every stage, two of the performance parameters that are

used to judge and analyses the quality of the image

quantitatively are the Mean Square Error (MSE) and the Peak

Signal to Noise Ratio (PSNR). The MSE is the average

squared error between the distorted/degraded and the original

image. For practical purposes it can be said to compare the

“true” pixel values of the original image with the degraded

image [5]. For color images, the MSE is taken over all the

pixels from each channel and averaged by the number of

channels. PSNR is a measure of the peak error and is the ratio

of the maximum possible value or power of a signal and the

MSE. The mathematical formulae for the two are:

𝑀𝑆𝐸 =1

𝑀𝑁∑ ∑ [𝐼(𝑥, 𝑦) − 𝐼′(𝑥, 𝑦)]2𝑁

𝑥=1𝑀𝑦=1 (10)

𝑃𝑆𝑁𝑅 = 20 ∗ 𝑙𝑜𝑔10(255

√𝑀𝑆𝐸) (11)

Where, I (x, y) is the original image, I'(x, y) is the pixel values

(original and distorted image) of the images of MxN

dimensions. A lower value for MSE corresponds to lowers

error and vice-versa. Logically, a higher value of PSNR is

good because it means that the ratio of Signal to Noise ratio is

higher. Also, at the end the decryption process, the recovered

audio signal is compared with the original audio signal and the

PSNR and co-relation co-efficient (12) is calculated, which is

the measure of the similarity or linear dependence of the two

variables.

𝜌(𝐴, 𝐵) =1

𝑁−1∑ (

𝐴𝑖−𝜇𝐴

𝜎𝐴)𝑁

𝑖=1 (𝐵𝑖−𝜇𝐵

𝜎𝐵) (12)

Where, 𝜎𝐴 and 𝜎𝐵 are the standard deviation of A and B

respectively. 𝜇𝐴 and 𝜇𝐵 are the mean of A and B.

IV. Simulation Results

This section contains the simulation results. The parameters

which were observed are MSE and PSNR values [5]. The

quality of the secret message signal is analyzed with respect

to the original message. The cover images are also compared

with the stego-images and their similarity index is obtained.

A. Sound Embedding in Image

The sound samples that were taken from the secret audio

message signal are converted to quaternion form and then

added with the pixels of the key image. Thus, each pixel acts

as a key for the subsequent sound samples. The key image that

was used is shown in the Fig. 9.

Figure 9. Key image

The image used is of size is 800x600 which implies that it

has 420000 pixels. Each pixel acts a key for the sound sample.

The secret message is an audio of runtime of approximately

10 seconds. The audio signal is sampled at a standard rate of

44.1 KHz. The quaternion addition of the image and the sound

signal results in another quaternion number which is then

converted back into an integer number and rescaled such that

the values are between 0-255. These values are then converted

into a matrix and an image is created out of this matrix. The

image thus obtained is shown by Fig. 10.

R=S-C

Get n pixels (R,

G, B) from

cover image

Digital

image

Convert RGB

components to

quaternion number

(v)

Retrieved image file

(after stego-

decryption) and

pixel values (S)

Audio

signal



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Figure 10. Resulting image with audio embedding

The pixels of the obtained image are distorted due to the

addition of the sound. Any third party may be able to notice

that the image might have some hidden message embedded

into it. Thus, this image is further masked by a cover image so

that the hacker may not be able to grasp the true meaning of

the image being sent.

B. Steganography

The LSB algorithm is used to mask the image obtained by the

audio embedding. For checking the consistency and testing

purposes, four different images were used as a cover image to

hide the true image. The comparisons were made between

both the cover image and the image obtained after the

steganographic process. The images are represented by the

following Figures 11, 12, 13 and 14.

(a) (b)

Figure 11. Cover image (a) and stego-image (b)

(a) (b)

Figure 12. Cover image and stego-image

(a) (b)

Figure 13. Cover image and stego-image 3

(a) (b)

Figure 14. Cover image and stego-image 4

The figures are shown such that the visual comparison can

be made among them before and after the steganography

procedure. Though differentiations can be made between the

images when kept next each other but when inspected

individually the differences are inconsequential. The output of

the steganographic algorithm for the simulated images is

given by the Table-I for the chosen images.

Table-I. Performance metrics of Steganography

Images MSE PSNR (dB)

Fig. 11 (a) and (b) 0.242.7160 0.331.1658

Fig. 12 (a) and (b) 38.38.9466 0.232.2261

Fig. 13 (a) and (b)

Fig. 14 (a) and (b)

0.241.2565

39.3082

0.231.9759

32.1860

Lower the MSE, higher will be the PSNR. From the table

we can see that the applied LSB algorithm is performing

consistently with different images.

C. Chaos Encryption Algorithm

To add-on a second and the last layer of security, chaos

encryption algorithm is used. The test audio embedded image

used is given by Figure 15.



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Figure 15. Audio embedded image

This image is scrambled using the encryption algorithm and

the resulting image is given by Figure 16.

Figure 16. Chaos encrypted image

This image is almost impossible to decipher without the

right key and thus is secure enough to transmit for

communicating the secret message. On performing the

decryption of chaos encryption on Fig. 16, we should get back

our transmitted stego-image which holds the sound embedded

image and thus perform a successful secret communication.

The decrypted image is shown by the following Fig. 17.

Figure 17. Chaos decrypted image

The MSE and PSNR values of the chaos encryption

algorithm are calculated between the initial and decrypted

image, and the results were 2.3032 and 44.5074 dB

respectively.

The performance parameters MSE that is calculated for the

input and decrypted image shows a value of 2.3032 which is

a lower value suggesting that both the images are highly

correlated. The PSNR value of the images is high of about

44.5 dB which implies that the distortions added due to the

algorithm were quite less and the image quality is maintained

upon performing the encryption and decryption of the image. For proper visualization of all the three images i.e. input,

encrypted and the decrypted images, histograms are plotted to

analyses the R, G, B pixel color values. These histograms are

given by Fig. 18, Fig. 19 and Fig. 20 respectively.

Figure 18. Histogram of input image



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Figure 19. Histogram of encrypted image

Figure 20. Histogram of decrypted image

Through the histograms we can see that the input and the

output image R, G, B values are almost matching while the R,

G, B values of the encrypted image are highly uncorrelated to

both the images.

D. Sound Embedded Image Extraction

The sound embedded image was extracted from the stego-

image so that the sound samples can be separated from the

image using the key image. A comparison was made between

the original sound embedded image and the obtained sound

embedded image so as to gauge the efficiency of the

algorithm. Fig. 21 shows the two images side by side as

follows.

(a) (b)

Figure 21. Original (a) and Obtained (b) Sound Embedded

Image

The performance parameters such as MSE and the PSNR of

the original and obtained sound embedded image is compared

and the values obtained were 28.8443 and 32.4225 dB

respectively.

E. Final Decryption Results

The chaos algorithm decryption yields the stego-image which

is then de-masked and the audio embedded image is obtained.

From this image, the sound components are extracted using

the original image which was used as a variable key and the

resulting sound samples are obtained. These sound samples

are then con-joined to recreate the secret audio message that

was sent by the user. This sound signal is now compared to

the original sound sent to obtain the co-relation coefficient and

the PSNR to judge the quality of the message. The original

sound signal amplitude graph is given by Fig. 22. The

obtained sound signal is also analyzed using the sound sample

amplitude graph and it is shown by Fig. 23.

Figure 22. Original audio signal

Figure 23. Recovered audio signal

The performance parameters i.e., co-relation coefficient

and the PSNR of the obtained signal with the sent signal were

calculated and the obtained values were 87.53 and 20.67 dB

respectively.

From the above values we can see that the similarity index

between the original and the recovered audio signals is around

88% and after hearing the two audio signals and comparing

manually, the audio could be discerned and the original audio

message was perceived successfully.



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This section underlined the results of the simulation done

for the entire multiple layers of encryption and decryption

processes. The parameters which were observed are

similarity, co-relation, and amount of degradation between

each element. These performance metrics were used analyses

the efficiency of the proposed cryptographic system.

VII. Conclusion

An algorithm for safe transmission of a secret message using

an image as a mask was designed and implemented. The

quality of the transmitted images at every stage was checked

and analyzed. The two layers of protection was established to

highly deter the prospective hackers or any third party that

may be eavesdropping for the secret message. The

performance parameters were analyzed and the quality of the

received secret audio recording was checked. Various cover

images were chosen to see the average MSE and PSNR

between the stego-image and the original image, so as to

analyses the quality of the transmitted images. The recovered

audio message was analyzed to ensure the safety and

correctness of the intended message.

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ISSN:1001-1749

Page No:486

Author Biographies

Naman Jain, Undergraduate student, Department

of Telecommunication Engineering, R V College of Engineering, Bengaluru. His research work mostly

focuses on Digital Communication and Signal

Processing. He is currently working of 5G modulation techniques. He is looking to do his post-

graduation in digital communication.

Prithvik H C, Undergraduate student, Department

of Telecommunication Engineering, RV College of Engineering, Bengaluru. His research interests

focus on are Cryptography, Network Security and

Software engineering. Currently, he is looking on doing his Post Graduation in Advanced

Cryptography and Network Security.

Rakesh K R, Assistant Professor, Department of

Telecommunication Engineering, RV College of Engineering, Bengaluru. He obtained his Bachelor’s

Degree in Engineering from JSSATE and M. Tech

from DSCE, Bengaluru. He has an experience of 5 years in teaching. His areas of interests in research

are Cryptography, Satellite communication and

Image Processing and has corresponded with many students with their projects and help achieve the

goals.



ISSN:1001-1749

Page No:487

Documents

A two-layer Audio Encryption system using Quaternion