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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
2 Department of Telecommunication Engineering, RV College of Engineering,
Mysore Road, Bengaluru-560059, India
3 Department of Telecommunication Engineering, RV College of Engineering,
Mysore Road, Bengaluru-560059, India
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.
Wutan Huatan Jisuan Jishu
Volume XVI, Issue V, May/2020
ISSN:1001-1749
Page No:478
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
Wutan Huatan Jisuan Jishu
Volume XVI, Issue V, May/2020
ISSN:1001-1749
Page No:479
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
Wutan Huatan Jisuan Jishu
Volume XVI, Issue V, May/2020
ISSN:1001-1749
Page No:480
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
Wutan Huatan Jisuan Jishu
Volume XVI, Issue V, May/2020
ISSN:1001-1749
Page No:481
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
Wutan Huatan Jisuan Jishu
Volume XVI, Issue V, May/2020
ISSN:1001-1749
Page No:482
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.
Wutan Huatan Jisuan Jishu
Volume XVI, Issue V, May/2020
ISSN:1001-1749
Page No:483
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
Wutan Huatan Jisuan Jishu
Volume XVI, Issue V, May/2020
ISSN:1001-1749
Page No:484
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.
Wutan Huatan Jisuan Jishu
Volume XVI, Issue V, May/2020
ISSN:1001-1749
Page No:485
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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Wutan Huatan Jisuan Jishu
Volume XVI, Issue V, May/2020
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.
Wutan Huatan Jisuan Jishu
Volume XVI, Issue V, May/2020
ISSN:1001-1749
Page No:487