042-051 - International Journal of Application or Innovation in

International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected]
Volume 3, Issue 6, June 2014
ISSN 2319 - 4847
A Hybrid Approach for Information Hiding and
Encryption using Multiple LSB’s Algorithms
H.B.Kekre1, Tanuja Sarode2 and Pallavi Halarnkar3
1
Senior Professor, MPSTME, NMIMS University, Mumbai
2
Associate Professor, TSEC Mumbai University, Mumbai
3
PhD Research Scholar, MPSTME, NMIMS University, Mumbai
Abstract
A number of techniques are available in literature to provide security to digital images, these techniques give their best to
Information Hiding, Image Scrambling and Image Encryption. In this paper we have proposed a Hybrid Approach to secure
digital images. The proposed framework is a combination of Information Hiding and Image Encryption. For Information Hiding,
four different methods of Multiple LSB’s Algorithm are used and evaluated. A number of parameters are also used to evaluate
the proposed framework. Experimental results show a good performance.
Keywords: Information Hiding, Image Steganography, Image Scrambling, Image Encryption
1. INTRODUCTION
Image security is a very important issue nowadays. Image security includes different aspects like Information Hiding,
Image Encryption etc. All these are different ways of providing security to digital images.
Kaliappan proposed an Information Hiding Technique for JPEG Compressed images[1]. The color image is firstly
converted into 1D signal in red, green and blue components, audibly masked frequencies in the 1D signal are determined
for each segment. Information hiding is carried out by modifying the spectral power at a pair of commonly occurring
masked frequencies. To enable the transmission of stego image, it is compressed via the JPEG coding technique.
Experimental results gives a good quality stego image, the method is also very simple to implement. Data retrieval at low
level compression and low level noise levels is possible. Higher payload is possible at the cost of quality of the stego
image.
The drawback of LSB insertion method is that it is easily detected with statistical analysis like RS and chi-square
methods. To overcome the drawback Hong Juan et al. proposed a steganography algorithm against the statistical analysis
[2]. In this method two sample LSB bits are combined using addition modulo 2 (or m) to form a value which is compared
to the part of the secret message. If these two values are equal, then no change is made else add the difference of these two
values to the second sample. This process is continued till all the secret message data is embedded. Experimental results
show that the proposed method resists the statistical analysis of RS and chi-square test.
Ching Yu Yang proposed a color image steganography method based on module substitution [3]. Three types of module
substitutions are used to embed secret bits, which is based upon the base value of the blocks under consideration. To
improve hiding capacity, the R, G and B component is further encoded by mod u, mod u-v and mod u-v-w substitution.
Experimental results show that hiding capacity and PSNR generated are better compared to the existing techniques.
Daniela et al. [4] proposed a novel approach of image steganography in YUV color space and its derivative. In this
method, RGB cover image is first converted to YUV color space, embedding is carried out only in the V component. The
standard LSB method is used for secret message embedding. After embedding in the V component the image is converted
back to RGB format. The reason for not using any other component in YUV color space for embedding is that any change
in the Y component will linearly propagate directly to the RGB color space.
Kekre et al. proposed a multiple LSB’s technique in [5], the method makes use of multiple LSB’s for hiding the secret
message. The method is compared to existing technique of steganography called as PVD. The proposed method was
implemented on grayscale images, however it is also extensible over color images. The proposed method gives much
more hiding capacity then PVD.
Kekre et al. proposed a variation of Multiple LSB’s algorithm in [6]. The method named KIMLA and KAMLA, KIMLA
makes use of the four MSB’s , analyses the binary value, converts it to decimal, increments it with one , then again its
binary is reconsidered, the number of one’s in it is used as number of LSB’s to be replaced in the cover image byte for
embedding the secret data. In KAMLA the maximum number of 1’s are considered for replacing the cover image byte for
embedding the secret data. Experimental results show a very huge increase in the hiding capacity of the cover image.
Volume 3, Issue 6, June 2014
Page 42
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected]
Volume 3, Issue 6, June 2014
ISSN 2319 - 4847
Another variation of Multiple LSB’s Algorithm (KMLA) was proposed by Kekre et al. in [7]. In this method, while
embedding the secret data and checking for cover image byte, if the byte is between 240 to 255, four LSB’s can be used
for embedding the secret data, the fifth LSB is checked with the message bit to be embedded if it matches with the
message bit then a fifth bit is utilized for embedding the secret data. If the value of the cover image byte is between 224
and 239, then again the message bit is checked with the fourth LSB, if it matches then it is utilized for embedding the
secret data, else only 3 bits are used for embedding the secret data. Rest of the steps are the same as KMLA. Experimental
results show a good increase in the hiding capacity of the cover image.
The drawback of the traditional Arnold transform is that it applies only to a square image, this limitation is overcome by
Min Li et al. in [8]. The paper proposed an image scrambling method which can be applied to a non square image by
dividing the image into multiple square areas and then scrambles each region. Experimental results show better security
of the proposed method which provides a safe and a reliable transmission.
Kekre et al. proposed an Image scrambling method based on the concept of Relative Prime [9]. Two numbers are said to
be relative Prime to each other if they do not have any factors in common except one. Using the concept of relative
prime, and finding the correlation between the first row and successive prime rows and placing that row next to first one,
that has minimum correlation. The same process is repeated for columns. In this way the image is scrambled. The
positions with respect to rows and columns is used as a key for descrambling the same.
R-Prime Shuffle technique was traditionally applied over the entire image, it was further extended to blocks in the image
of different sizes in [10]. Every block had a relative prime with respect to row and column. This together of all the blocks
made the key which can be kept secret for descrambling. Compared to the earlier version of R-Prime, this particular
method provides more security.
Yicong Zhou et al. proposed a new scrambling technique based on a sequence produced using shift registers [11]. The
keys used for scrambling, r the number of shift operations and the distance parameter p, with the help of which it is
possible to generate different m-sequences allows user with a wide variety of options of m-sequence to be used for image
scrambling. This provides a more secure method and difficult to decode the scrambled image. The proposed algorithm
can be applied to 2-D or 3-D images in one step. The method is also robust against attacks such as filters and noise
attacks.
Mohammed et al. proposed an Image encryption technique called as XLLS [12], it consists of two main parts:
encryption/decryption algorithm and ciphered key. In diffusion stage XOR operation is used to modify the pixel values,
for encryption purpose Lagrange Process (LP) and Least Square Process (LSP) is used. Decryption of the image is just the
reverse operation. Two different options for keys are possible, the proposed system uses a key whose length is 192 bits
which is expanded using key expansion algorithm AES -192. The second approach makes use of an image as a key and
makes use of CBI key expansion algorithm to expand.
Sanfu Wang et al. proposed a new Image Scrambling technique based on folding transform [13]. The folding transform is
an orthogonal matrix which allows the image to be folded either up-down or left –right. The repeated folding of the
image using this procedure scrambles the image. Experimental results show that the method has a good hiding ability
with lesser computation burden. The method is also robust under common attacks.
Image scrambling techniques are applied over digital images so that the image content becomes meaningless. Dimitri et
al. proposed an extension of one dimensional scrambling technique based on discrete Prolate Spheroidal Sequences
(DPSS) to two dimensional [14]. The DPSS is optimal in energy concentration in a given frequency sub band.
The idea of inverse transformation in finite integer domain is applied to image scrambling by Zhang Ruihong et al. in
[15]. In involves both gray and position transformation. The paper also analyzes the security and the scrambling cycle.
Experimental results show that the method has a good scrambling effect, a long scrambling cycle.
Random numbers are generated using Discrete distributions, they are scaled in the range of 0 to 255 so as to make them
suitable for a digital Images. Using the concept of MOD operator and its inverse, image is encrypted. Experimental
results show a good quality encryption, histogram analysis is also plotted so as to show the flat histogram obtained from
the encrypted image. This method is proposed by Kekre et al. in [16].
Volume 3, Issue 6, June 2014
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected]
Volume 3, Issue 6, June 2014
ISSN 2319 - 4847
Shuhong Li et al. proposed a fast scrambling algorithm using multi-dimensional orthogonal transform in [17]. The
sequence produced using orthogonal transform can be used for image scrambling and descrambling. The proposed
method has a high security, as the keys used for scrambling is the one used for generating the sequence. The drawback of
the method is that it has a high time complexity, to overcome the said drawback, serial and parallel fast algorithms are
proposed. An image is also used as a key image. Experimental results show that this concept can greatly reduce the time
complexity.
A novel method based on information hiding of two-dimensional images in fractional Fourier domains is proposed in
[18]. Firstly image is random shifted using the Jigsaw transformation, then a pixel scrambling algorithm based on Arnold
transformation is applied. Then this scrambled image is iteratively encrypted in the fractional Fourier domains using the
fractional orders which are chosen randomly. The proposed method has a huge key space thus enhancing the security
level.
2. PROPOSED HYBRID FRAMEWORK FOR IMAGE SECURITY
In this paper, a Hybrid framework for Information Hiding and Image Encryption is proposed. Following are the steps of
the Embedding process
1) Take a Image of size x bytes
2) Apply R-Prime Shuffle Technique to scramble it, A scrambled image is obtained
3) Using KMLA or MKMLA or KIMLA or KAMLA Multiple LSB’s Information Hiding technique hide the required
secret message, in this paper , an ATM card image is used, a scrambled stego image is obtained.
4) Descramble the scrambled stego image by applying R-Prime shuffle, an innocent stego image is obtained.
Figure. 1. Embedding Stage
The retrieving process is as follows
1) Take the innocent stego image
2) Apply the R-Prime Shuffle technique, a scrambled stego image is obtained
3) Apply the retrieval algorithm and obtain the secret message image hidden in it.
Figure. 2. Retrieving Stage
Advantage of the proposed framework is that, if some intruder tries to retrieve the secret message image from the
innocent stego image which is being transferred he will get an encrypted message image, which will be difficult to
Volume 3, Issue 6, June 2014
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected]
Volume 3, Issue 6, June 2014
ISSN 2319 - 4847
decode. The results for the same are shown below
2.1 Kekre’s Multiple LSB’s Algorithm (KMLA)
Principle: By analyzing the intensity of every pixel the no of bits that could be embedded be decided so as to make
the change imperceptible to the human eye. This is done by embedding more number of bits in high intensity value pixels
and less number of bits for low intensity pixels. [5]
Every pixel value in this image is analyzed and the following checking process is employed
1. If the value of the pixel say gi, is in the range 240 ≤ gi ≤255 then we embed 4 bits of secret data into the 4 LSB’s of the
pixel. This can be done by observing the first 4 Most Significant Bits (MSB’s). If they are all 1’s then the remaining 4
LSB’s can be used for embedding data.
2. If the value of gi (First 3 MSB’s are all 1’s), is in the range 224 ≤ gi ≤239 then we embed 3 bits of secret data into the 3
LSB’s of the pixel.
3. If the value of gi (First 2 MSB’s are all 1’s), is in the range 192 ≤ gi ≤223 then we embed 2 bits of secret data into the 2
LSB’s of the pixel.
4. And in all other cases for the values in the range 0 ≤gi ≤191 we embed 1 bit of secret data in to 1 LSB of the pixel.
Similarly, we can retrieve the secret data from the gray values of the stego image by again checking the first four
MSB’s of the pixel value and retrieve the embedded data.
2.2 Modified Kekre’s Multiple LSB’s Algorithm (MKMLA)
The original Kekre’s Multiple LSB’s method made use of upto four LSB’s for embedding information. The number of
LSB’s selected depends on the pixel value to control the error. In this Modified Kekre’s Mulitple LSB’s Algorithm
(MKMLA) we have modified this to include fifth LSB matching to increase capacity further [7].
Every pixel value in this image is analyzed and the following checking process is employed
1. If the value of the pixel say gi, is in the range 240 ≤ gi ≤ 255, then we check for the message bit to be embedded, if it is
1 then we utilize the fifth bit of the pixel value. If the message bit is not 1 then we embed 4 bits of secret data into the 4
LSB’s of the pixel. This can be done by observing the first 4 Most Significant Bits (MSB’s). If they are all 1’s then the
remaining 4 LSB’s can be used for embedding data.
2. If the value of gi (First 3 MSB’s are all 1’s), is in the range 224 ≤ gi ≤ 239 then we check whether the message bit to be
embedded is 0 then we utilize 4 bits of the pixel value. If the message bit is not 0 then embed 3 bits of secret data into
the 3 LSB’s of the pixel.
3. If the value of gi (First 2 MSB’s are all 1’s), is in the range 192 ≤ gi ≤ 223 then we embed 2 bits of secret data into the 2
LSB’s of the pixel.
4. And in all other cases for the values in the range 0 ≤ gi ≤191 we embed 1 bit of secret data in to 1 LSB of the pixel.
5. The embedding process maintains a matrix to keep a track of the pixels where 5 bits are utilized for embedding process.
This helps in the retrieving of the secret message.
The retrieving process is very simple by observing the most significant bits and using the matrix maintained.
2.3 Kekre’s Improved Multiple LSB Algorithm (KIMLA)
To Increase the hiding Capacity, KIMLA algorithm was proposed by Kekre et al. [6]. This Algorithm is based upon the
four MSB’s in the image byte. The four MSB’s of the image byte is first converted to decimal, then a one is added to this
decimal, this new decimal is then converted to 4 bit binary, the number of one’s in this binary equivalent is used to decide
the number of LSB bits to be used in the image byte for hiding the secret message. One exceptional case is the number 15
which is not changed and used as it is.
2.4 Kekre’s Advanced Multiple LSB Algorithm (KAMLA)
An improvement over KIMLA is KAMLA, in the above algorithm the number of one’s in the new decimal decides the
number of LSB’s to be used for embedding purpose. However, in some cases it may happen that the original byte decimal
equivalent’s binary may have extra one’s then the new decimal, so in this algorithm a max of both is considered for
hiding the secret message.[6]
3. EXPERIMENTAL RESULTS
For Experimental purpose five 24 bit color images of size 256x256 were used, an ATM card image was used as a secret
message for embedding.
Volume 3, Issue 6, June 2014
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected]
Volume 3, Issue 6, June 2014
ISSN 2319 - 4847
3.1 Kekre’s Multiple LSB’s Algorithm(KMLA)
(a) Original Image
(b) Scrambled Image
(c) Scrambled
Image
Stego
(d) Stego Image
Figure 3.
(a) Message Image
(b) Encrypted Message Image
(c) Retrieved Message Image
Figure 4.
Figure 3(a) shows the original image, (b) shows the scrambled image obtained by R-Prime Shuffle technique, (c) shows
the scrambled stego image after hiding the secret message in it and (d) shows the innocent stego image obtained by
descrambling the scrambled stego image using KMLA method
Figure 4(a) shows the message image used as a secret message for hiding, (b) shows the encrypted image obtained if some
intruder tries to retrieve the secret message from the innocent stego image. And (c) shows the retrieved message image
from the scrambled stego image.
3.2 Modified Kekre’s Multiple LSB’s Algorithm(MKMLA)
(a) Original Image
(b) Scrambled Image
(c) Scrambled
Image
Stego
(d) Stego Image
Figure 5.
(a) Message Image
(b) Encrypted Message Image
(c) Retrieved Message Image
Figure 6.
Figure 5(a) shows the original image, (b) shows the scrambled image obtained by R-Prime Shuffle technique, (c) shows
the scrambled stego image after hiding the secret message in it and (d) shows the innocent stego image obtained by
descrambling the scrambled stego image MKMLA method
Figure 6(a) shows the message image used as a secret message for hiding, (b) shows the encrypted image obtained if some
intruder tries to retrieve the secret message from the innocent stego image. And (c) shows the retrieved message image
from the scrambled stego image.
Volume 3, Issue 6, June 2014
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected]
Volume 3, Issue 6, June 2014
ISSN 2319 - 4847
3.3 Kekre’s Improved Multiple LSB’s Algorithm(KIMLA)
(a) Original Image
(b) Scrambled Image
(c) Scrambled Stego
Image
(d) Stego Image
Figure 7.
(a) Message Image
(b) Encrypted Message Image
(c) Retrieved Message Image
Figure 8.
Figure 7(a) shows the original image, (b) shows the scrambled image obtained by R-Prime Shuffle technique, (c) shows
the scrambled stego image after hiding the secret message in it and (d) shows the innocent stego image obtained by
descrambling the scrambled stego image KIMLA method
Figure 8(a) shows the message image used as a secret message for hiding, (b) shows the encrypted image obtained if some
intruder tries to retrieve the secret message from the innocent stego image. And (c) shows the retrieved message image
from the scrambled stego image.
3.4 Kekre’s Advanced Multiple LSB’s Algorithm(KAMLA)
(a) Original Image
(b) Scrambled Image
(c) Scrambled
Image
Stego
(d) Stego Image
Figure 9.
(a) Message Image
(b) Encrypted Message Image
(c) Retrieved Message Image
Figure 10.
Figure 9(a) shows the original image, (b) shows the scrambled image obtained by R-Prime Shuffle technique, (c) shows
the scrambled stego image after hiding the secret message in it and (d) shows the innocent stego image obtained by
descrambling the scrambled stego image KAMLA method
Figure 10(a) shows the message image used as a secret message for hiding, (b) shows the encrypted image obtained if
some intruder tries to retrieve the secret message from the innocent stego image. And (c) shows the retrieved message
image from the scrambled stego image.
Volume 3, Issue 6, June 2014
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected]
Volume 3, Issue 6, June 2014
ISSN 2319 - 4847
Table 1:Values of Average correlation between rows and columns of original image and scrambled image, Average
Moving Distance and Distance Scrambling factor between original image and scrambled image
Lena Image
Rows: 0.8454 Cols: 0.7005
Avg Corr Rows
Avg Corr Cols
AMD(Max)
AMD
DSF
Kutub Image
Rows: 0.2784 Cols: 0.3896
KMLA
MKMLA
KMLA
MKMLA
KMLA
MKMLA
KAMLA
KIMLA
KAMLA
0.1989
0.1901
360.6245
132.1023
0.3663
KMLA
MKMLA
KIMLA
KAMLA
0.1854
0.1916
360.62
130.44
0.3617
Avg Corr Rows
Avg Corr Cols
AMD(Max)
AMD
DSF
Fruits Image
Rows: 0.6203 Cols: 0.6317
KIMLA
0.1816
0.1865
360.62
133.58
0.370
Avg Corr Rows
Avg Corr Cols
AMD(Max)
AMD
DSF
Baboon Image
Rows: 0.4355 Cols: 0.5276
KAMLA
0.3069
0.2687
360.62
136.48
0.378
Avg Corr Rows
Avg Corr Cols
AMD(Max)
AMD
DSF
Vegetable Image
Rows: 0.5176 Cols: 0.4994
KIMLA
KMLA
MKMLA
KIMLA
KAMLA
0.1975
0.2139
360.62
133.26
0.3696
Avg Corr Rows
Avg Corr Cols
AMD(Max)
AMD
DSF
Table 2: Values of RMSE, PSNR, between original Image and Stego image
Lena
RMSE
PSNR
Kutub
RMSE
PSNR
Vegetable
RMSE
PSNR
Baboon
RMSE
PSNR
Fruits
RMSE
PSNR
Volume 3, Issue 6, June 2014
KMLA
1.2724
46.038
MKMLA
2.0006
42.107
KIMLA
1.9070
42.523
KAMLA
1.9885
42.160
1.4270
45.042
2.2549
41.068
1.9138
42.492
1.9943
42.135
2.0819
41.761
3.0569
38.425
2.1490
41.486
2.1680
41.409
1.0453
47.745
1.8014
43.018
1.7457
43.291
1.8632
42.725
1.6000
44.048
2.4120
40.483
1.9412
42.369
1.9874
42.165
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
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Volume 3, Issue 6, June 2014
ISSN 2319 - 4847
Table 3: Values of PAFCPV and NPCR, between original Message Image and Scrambled Message image
Lena
PAFCPV
NPCR
Kutub
PAFCPV
NPCR
Vegetable
PAFCPV
NPCR
Baboon
PAFCPV
KMLA
0.4156
98.834
MKMLA
0.4157
99.179
KIMLA
0.4163
98.359
KAMLA
0.4161
98.270
0.4145
98.613
0.4080
99.088
0.4172
98.125
0.4181
98.359
0.4168
98.868
0.4143
99.227
0.4185
98.540
0.4118
98.397
0.4182
0.4148
0.4163
0.4162
NPCR
Fruits
PAFCPV
NPCR
98.942
99.157
98.605
98.315
0.4156
98.829
0.4164
99.326
0.4174
98.324
0.4130
98.354
Figure 11. Embedding Capacity in bits for KMLA, MKMLA, KIMLA and KAMLA
4. CONCLUSION
For Experimental analysis, a number of parameters are used to evaluate the proposed framework. The framework makes
use of four different variations of Multiple LSB’s Algorithm for Information Hiding and R-Prime shuffle technique for
Image scrambling. In a scrambled image, it is necessary that the correlation between the rows and columns be minimum
or reduced as that found in the original image. Average row and column correlation is calculated in the original image
and the scrambled image, from the results obtained it is clear that the correlation between the rows and columns are
reduced , minimum obtained in fruits image with a row reduction of 68.17 % and column reduction of 66.14%. To
evaluate how good is the scrambling technique, parameters called as Average Moving Distance (AMD), AMD-Max and
Distance Scrambling factor is calculated, the maximum obtained in Lena image. To analyze the quality of the stego
image, RMSE and PSNR is found out between the original image and innocent stego image. The minimum RMSE is
obtained in baboon image in KMLA. To evaluate the quality of the encrypted message image obtained by the framework
two parameters Peak Average fractional Change in Pixel Value (PAFCPV) [16] and Number of Pixel Change Rate
(NPCR) [12] are used. The highest PAFCPV is obtained in baboon image for KMLA method. As there were different
Multiple LSB’s techniques used , Embedding capacity was also measured the highest capacity in bits been obtained in
Lena for KAMLA method which can be seen from Figure 11.
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ISSN 2319 - 4847
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AUTHOR’s
Dr. H. B. Kekre has received B.E (Hons.) in Telecomm Engineering from Jabalpur University in 1958, M.Tech
(Industrial Electronics) from IIT Bombay in 1960, M.S.Engg. (Electrical Engg.) from University of Ottawa, Canada in
1965 and Ph.D. (System Identification) from IIT Bombayin 1970. He has worked as Faculty of Electrical Engg. and then
HOD Computer Science and Engg. at IIT Bombay. After serving IIT for 35 years he retired in 1995. After retirement from
IIT, for 13 years he was working as a professor and head in the Department of Computer Engg. and Vice Principal at Thadomal
Shahani Engineering. College, Mumbai. Now he is Senior Professor at MPSTME, SVKM’s NMIMS University. He has guided 17
Ph.Ds, more than 100 M.E./M.Tech and several B.E./ B.Tech projects, while in IIT and TSEC. His areas of interest are Digital Signal
processing, Image Processing and Computer Networking. He has more than 450 papers in National / International Journals and
Conferences to his credit. He was Senior Member of IEEE. Presently He is Fellow of IETE, Life Member of ISTE and Senior Member
of International Association of Computer Science and Information Technology (IACSIT). Recently fifteen students working under his
guidance have received best paper awards. Currently eight research scholars working under his guidance have been awarded Ph. D. by
NMIMS (Deemed to be University). At present eight research scholars are pursuing Ph.D. program under his guidance.
Dr. Tanuja K. Sarode has received M.E. (Computer Engineering) degree from Mumbai University in 2004, Ph.D. from
Mukesh Patel School of Technology, Management and Engg. SVKM’s NMIMS University, Vile-Parle (W), Mumbai,
INDIA. She has more than 11 years of experience in teaching. Currently working as Assistant Professor in Dept. of
Computer Engineering at Thadomal Shahani Engineering College, Mumbai. She is member of International Association
of Engineers (IAENG) and International Association of Computer Science and Information Technology (IACSIT). Her areas of interest
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected]
Volume 3, Issue 6, June 2014
ISSN 2319 - 4847
are Image Processing, Signal Processing and Computer Graphics. She has 150 papers in National /International Conferences/journal to
her credit.
Ms. Pallavi N.Halarnkar has received M.E. (Computer Engineering) degree from Mumbai University in 2010, currently
persuing her Ph.D. from Mukesh Patel School of Technology, Management and Engg. SVKM’s NMIMS University,
Vile-Parle (W), Mumbai, INDIA. She has more than 8 years of experience in teaching. Currently working as Assistant
Professor in Dept. of Computer Engineering at Mukesh Patel School of Technology, Management and Engg. SVKM‟ s
NMIMS University, Vile-Parle (W), Mumbai. She has 27 papers in National /International Conferences/journal to her credit
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