Information Hiding Using Group of Bits Substitution

International Journal on Communications Antenna and Propagation (I.Re.C.A.P.), Vol. 7, N. 2 ISSN 2039 – 5086 April 2017

Information Hiding Using Group of Bits Substitution Aditya Kumar Sahu, Gandharba Swain Abstract – The importance of any steganographic approach is based on its capacity and security in data transmission. This article proposes an image steganographic method, called three group of bits substitution (3GBS) which hides 3 bits of secret data in a pixel. Each pixel of an image can hide 3 bits of secret data. The Peak Signal-to-Noise Ratio (PSNR) and hiding capacity are two important parameters to evaluate the strength of any steganographic method. This article compares the PSNR and hiding capacity of the proposed 3GBS method with the existing GBS methods. The experimental results of the proposed method gives a conclusive evidence that the hiding capacity increased significantly with an acceptable visual fidelity of the produced-image. Copyright © 2017 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: Steganography, Least Significant Bit (LSB), Group of Bits Substitution, Capacity, Peak Signal-to-Noise Ratio (PSNR)

Steganography can be grouped into four types, such as image steganography, text steganography, audio steganography and video steganography [6], [13]. Image steganography uses an image to transmit any type of data. The image can be called a cover image, the new image; after hiding, secret data can be called a produced-image. The efficiency of any steganographic technique can be measured through parameters, such as (1) hiding capacity (2) peak signal-to-noise ratio (PSNR) (3) bits per pixel (BPP) [4]. The capacity of an image is the maximum number of secret data bits that a pixel of an image can hold. PSNR is the metrics to evaluate the quality of the produced-image. Bits per pixel is the maximum amount of data that can be hidden in the individual pixels of an image. This article proposes a modified group of bits substitution method to improve the hiding capacity of the original image.

Nomenclature I K k N n S T BPP GBS LSB MSB PSNR PVD Q MSE

Binary image Image pixel Individual bits of an image pixel Individual bits of an image pixel Total number of secret bits Individual bits of an image pixel Produced image Bits per Pixel Group of Bit Substitution Least Significant Bit Most-Significant-Bit Peak Signal-to-Noise Ratio Pixel Value Differencing Quality Index Mean Square Error

I.

II.

Introduction

Related Work

Least significant bit (LSB) substitution is one of the straightforward insertion techniques [5], [8]. Here the LSB bits of the pixels are replaced by the secret bits. A simple LSB method offers good capacity but the cost is a significant degradation in the quality of the producedimage. Recently, edge detection methods are being extensively used for transmitting data inside an image. The more the edges in an image the larger the data it can hide. To achieve higher payload capacity, Bai et. al. [7] combined the LSB substitution method and existing edge detector techniques, such as Canny, Sobel and Fuzzy. Here instead of using the original image for edge detection, a new image has been created from the original image which consists of the last 3 MSB bits of each pixel of the original image. Shet et. al. [8] implemented the LSB image steganography using an integrated circuit

Internet plays a decisive role in the field of digital data communication. Extensive study is going on and a large number of articles has already been published in literature concerning the security of digital data in communication. Generally, the methods employed to secure data is segregated into cryptography and steganography. Although cryptography confuses unauthorized people by scrambling information, he/she is aware of the communication. Primitively, steganography is a Greek word. It stands for covered writing [1], [3] & [4] Steganography hides the message from the intruder. Therefore, in case of steganography, the attacker cannot even sense the existence of information. Steganographic approaches are proposed in both spatial and transformation domains.

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https://doi.org/10.15866/irecap.v7i2.11675

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hardware called field-programmable gate array (FPGA). Using FPGA, the delay of the steganography system can be reduced to a greater extent and further this also increases the throughput of the system. Swain and Lenka [9] suggested a method called dynamic steganography by combining the advantage of both cryptography and steganography. The secret data is first encrypted using the block cipher cryptographic algorithm having 128 bit key length. The 6th, 7th and 8th bit locations of the original pixels have been chosen for embedding secret data. LSB techniques are productive in terms of hiding capacity. Kumar and Chand proposed a new technique called reversible bit flipping [10]. Here the 8th LSB bit is used to hide the secret message. Secret data can be hidden in various layers. As the layers increase, the capacity also increases, but the PSNR remains almost constant. In 2003, Wu and Tsai [11] recommended a novel method in the area of image steganography called pixel value differencing (PVD). The cover image is segregated into smooth and edge areas. It finds a difference value called ‘d’ between every non-overlapping block of two consecutive pixels. The original value of ‘d’ will be replaced by the new value ‘d' to embed secret data. Swain [12] proposed five, six, seven and eight neighbor differencing methods. An experimental study reveals that, in case of five neighbors differencing the quality of the image is better and the eight neighbors differencing offers a larger capacity. Various ideas have been proposed by many authors by bringing together the advantages of LSB and PVD methods [14]-[20], [24][29]. A new track called LSB array has been proposed in [21]. The LSB array stores LSB bits of the original pixel in an array. The secret data have to be converted to binary. Finally, the binary secret data have been mapped with the LSB array. The position in which secret data matches the LSB array has been recorded. The matched position is sent to the receiver for the extraction of secret data. This work has been further extended in [22]. Swain [2] came with a new way of embedding secret data called group of bits substitution (GBS). He proposed two variants of the group of bit substitution method, namely 1GBS and 2GBS. The 1GBS method hides one bit and 2GBS hides two bits per pixel respectively.

k 1k 2k 3k 4k 5k 6k 7 k 8 , K 2 = k 2 k 3k 4 k 5 k 6 k 7 k 8 , K3 = k 3k 4 k 5 k 6 k 7 k 8 , K 4 = k 4 k 5 k 6 k 7 k 8 , K 5 = k 5 k 6 k 7 k 8 , K 6 = k 6k 7 k 8 , K 7 = k 7 k 8 . Step-2: If K 1 is 01111111 or 10000000 and Sj is 0, then after embedding, K 1 = 01111111. Otherwise if Sj is 1, then after embedding set K 1 = 10000000. Step-3: Step-2 continues for Ki , where i = 2 to 7, where Sj values are 0 and 1. Finally, when K 7 is 01 or 10, If Sj is 0, then after embedding, K 7 = 01. Otherwise if Sj is 1, then after embedding set K 7 = 10. From the receiver side, the extraction procedure is the opposite of embedding, i.e if the pixel K 1 = 01111111, then the extracted bit is ‘0’. Again, when K 1 = 10000000, then the extracted bit is ‘1’. Similarly, for all the values of Ki , such as K 2 to K 7 , the corresponding secret data bit, such as either 0 or 1, will be recovered. Thus, the 1 GBS method hides 1 bit of secret data in the pixel. III.2. GBS Embedding and Extraction Method Step-1: Suppose the eight bits of an image are denoted as Ki = K 1K 2 K 3 K 4 K 5 K 6 K 7 K 8 . Data S comprise secret bits Sj , Sj  1 , for j= 1 to n, and Sj , Sj  1 can be 00,01,10,11. Let, K 1 = k 1k 2k 3k 4k 5k 6k 7 k 8 , K 2 = k 2k 3k 4k 5k 6k 7 k 8 , K 3 = k 3k 4 k 5 k 6 k 7 k 8 , K 4 = k 4 k 5 k 6 k 7 k 8 , K 5 = k 5 k 6 k 7 k 8 , K 6 = k 6k 7 k 8 , K 7 = k 7 k 8 . Step-2: If K 1 is 01111111 or 10000000 and Sj , Sj  1 is 00, then after embedding, K 1 = 01111111. Else if Sj , Sj  1 is 01, then after embedding set K 1 = 01111110. Else if Sj , Sj  1 is 10, then after embedding set K 1 = 10000001. Else if Sj , Sj  1 is 11, then after embedding set K 1 = 10000000. The embedding process continues for Ki , where i = 2 to 7, and Sj , Sj  1 values are 00, 01, 10 and 11. Step-3: Finally, for K 7 is 01 or 10 and Sj , Sj  1 is 00, then after embedding, K 7 = 11. Else if Sj , Sj  1 is 01, then after embedding set K 7 = 10. Else if Sj , Sj  1 is 10, then after embedding set K 7 = 01. Else if Sj , Sj  1 is 11, then after embedding set K 7 = 00. From the receiving side, data can be recovered by doing the opposite task of embedding, i.e if the pixel K 1 = 01111111, then the bits extracted are ‘00’. Similarly for K 1 = 10000000, K 1 = 01111110, K 1 = 10000001 the bits recovered are 11, 01, 10. In the same way for all the values of Ki , such as K 2 to K 7 , the corresponding secret data bits such as 00,01,10,11 are recovered. Thus, the 2 GBS method hides 2 bits of secret data in a pixel.

III. Existing 1GBS and 2GBS Methods Swain [2] proposed 1GBS and 2GBS methods. Both methods substitute a group of bits having an exact length to hide 1 or 2 bits in every pixel of an image. The embedding and extraction of both methods have been given below. III.1. GBS Embedding and Extraction Method Step-1: Suppose the eight bits of an image are denoted as Ki = K 1K 2 K 3 K 4 K 5 K 6 K 7 K 8 . Let the secret data be Sj for j = 1 to n, where, Sj can be 0 or 1. Let, K 1 = Copyright © 2017 Praise Worthy Prize S.r.l. - All rights reserved

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order to compare the existing and proposed methods. PSNR speaks about the distortion in the produced-image. It is measured using the mean square error (MSE) between the original image and the produced-image. The higher is the PSNR the better it is. High PSNR also suggests a lower distortion in the produced-image. Any steganographic method which gives the PSNR a value of more than 30db is considered to be good.

IV. Proposed 3GBS Method The existing 1GBS and 2GBS methods proposed by Swain [2] hide 1 and 2 bits of secret data in the pixel of an image respectively. The capacity can be further improved to 3 bits per pixel element. It is proved by the experimental study that the capacity of proposed 3GBS method is three times than that of the existing 1GBS and 1.5 times than 2GBS. This is clearly an advantage in terms of capacity compared to existing GBS methods. This article proposes a modified group of bits substitution which hides 3 bits per pixel in an original image. The proposed embedding and extraction method has been explained in details in the appendix.

V.

Results

(a) Lena

The MATLAB tool has been used to implement the proposed method. The original images are shown in Figs. 1 and the corresponding produced-images for 3 GBS are represented in Figs. 2, with 2,45,000 bits of data hidden in each of them. It is evident that the produced-images are quite imperceptible and do not show any visual mark. Thus, an intruder will not suspect that they carry some hidden data inside.

(d) Boat

(b) Baboon

(e) House

(g) Barbara (a) Lena

(b) Baboon

(c) Peppers

(f)Baby

(h) Bird

(c) Peppers Figs. 2. (a)-(h) Produced-Images

(d) Boat

(e) House

In Table I, the PSNR of the proposed method is acceptable i.e. it is more than 40 db. The hiding capacity speaks about the maximum amount of hidden information that can be concealed into the original image. The bits per pixel (BPP) parameter reveals the hiding capacity per pixel of an image. A higher BPP value suggests the higher capacity of an image. In the proposed method, the BPP is 3 bits per pixel. If Pij is the m×n gray-scale image and Qij is its produced-image, then MSE and PSNR values can be found using equations (1) and (2) respectively:

(f) Baby

)2 (g) Barbara

(h) Bird

(1) (2)

Figs. 1. (a)-(h) Original Images

The quality of the produced-images are evaluated by using the universal image quality index (Q) [23]. Q represents the resemblance between the original and the produced-image. It is computed by using (3):

The proposed method is compared to the existing 1 GBS and 2 GBS schemes [2], in Table I. The various image steganographic parameters [4], [23] have been considered for comparison. Parameters such as PSNR, bits per pixel (BPP), hiding capacity, universal image quality index (Q) have been taken into consideration in

(3)

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where:

1

= 1 ×

̅=

1 ×

)

(4) =

1 ×

= =

(

−1

(

)

(

−

×

(5)

̄)

−1

1 ×

−1

(

− ̄)

−

̄ (

(7)

− ̄)

(8)

The optimal value for Q is 1. The optimal value can be achieved only if both the original and produced-images are completely identical.

(6)

TABLE I COMPARISON BETWEEN 1GBS, 2GBS AND PROPOSED 3GBS TECHNIQUES Swain’s 1-bit GBS scheme

Images 512×512

PSNR

Capacity

Lena Baboon Peppers Boat House

51.44 51.41 51.42 51.42 51.43

262144 262144 262144 262144 262144

Q.I

Swain’s 2-bit GBS scheme

Proposed 3-bit GBS

BPP

PSNR

Capacity

Q.I

BPP

PSNR

Capacity

Q.I

BPP

0.98 0.99 0.98 0.99 0.99

1.0 1.0 1.0 1.0 1.0

49.69 49.68 49.68 49.69 49.69

524288 524288 524288 524288 524288

0.98 0.99 0.98 0.99 0.99

2.0 2.0 2.0 2.0 2.0

44.37 44.40 44.37 44.38 44.38

786432 786432 786432 786432 786432

0.96 0.99 0.95 0.97 0.98

3.0 3.0 3.0 3.0 3.0

Baby

51.35

262144

0.97

1.0

49.69

524288

0.96

2.0

44.37

786432

0.91

3.0

Barbara

51.42

262144

0.98

1.0

49.68

524288

0.98

2.0

44.40

786432

0.97

3.0

Bird

51.33

262144

0.89

1.0

49.65

524288

0.90

2.0

43.88

786432

0.89

3.0

Average

51.40

262144

0.97

1.0

49.68

524288

0.97

2.0

44.32

786432

0.95

3.0

VI.

the message”. Otherwise go to the next step. Step3: Let, K 1 = k 1k 2k 3k 4k 5k 6k 7 k 8 , K 2 = k 2 k 3k 4 k 5 k 6 k 7 k 8 , K 3 = k 3 k 4 k 5 k 6 k 7 k 8 , K 4 = k 4 k 5k 6 k 7 k 8 , K 5 = k 5k 6 k 7 k 8 , K 6 = k 6 k 7 k 8 . Step-4: If K 1 is 01111111 or 10000000. If secret data bits, Sj , Sj  1, Sj  2 is 000 then, after embedding, K 1 = 01111111. Else if Sj , Sj  1, Sj  2 is 001 then, after embedding, K 1 = 01111110. Else if Sj , Sj  1, Sj  2 is 010 then, after embedding, K 1 = 01111101. Else if Sj , Sj  1, Sj  2 is 011 then, after embedding, K 1 = 01111100. Else if Sj , Sj  1, Sj  2 is 100 then, after embedding, K 1 = 10000011. Else if Sj , Sj  1, Sj  2 is 101 then, after embedding, K 1 = 10000010. Else if Sj , Sj  1, Sj  2 is 110 then, after embedding, K 1 = 10000001. Else if Sj , Sj  1, Sj  2 is 111 then, after embedding, K 1 = 10000000. The above substitution process will be continued for K 2 to K 6 , where K 6 can be 000 or 001 or 010 or 011 or 100 or 101 or 110 or 111. Step-5: Increment the value of i by 1 and j by 3. Step-6: If j≤ n, then move to step 4, else move to step 7. Step-7: Print “successful embedding”.

Conclusion

This article proposes an improved data hiding method called three group of bits substitution (3GBS). The existing 1-bit GBS and 2-bit GBS methods hide 1 and 2 bits of secret data per pixel respectively. The proposed 3 GBS can embed 3 bits per pixel. Thus, the capacity of the proposed method have been improved compared to the existing GBS methods. Furthermore, it is evident from the produced-images that they are imperceptible and do not invite to any attack.

Appendix Embedding Algorithm At first, change the gray-scale cover image and the secret data to its corresponding binary. The length of secret data is converted to binary having 18 bit length. Now combine the 18 bit binary length and secret binary data and this will now be treated as secret data. One pixel element will be utilized to conceal 3 bits of hidden data. Consider the original cover image as the combination of ‘N’ pixels and let the total length of secret data be ‘n’ in bits. Step 1: The binary image is Ii , for i= 1 to N, where each pixel is 8 bits in magnitude. Data S comprise secret bits Sj , Sj  1, Sj  2 , for j= 1 to n, and Sj , Sj  1, Sj  2 can range from 000 to 111. Suppose the 8 bits of a pixel are denoted by Ii = K 1K 2 K 3 K 4 K 5 K 6 K 7 K 8 . Step-2: If,  n - 18  3 >N, then print “The image cannot hold

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Extraction Algorithm The extraction procedure is the opposite of the embedding procedure. Let the binary produced-image be Ti and the data to be retrieve from it be ‘d’. Step-1: Set ‘d’ as empty and the counter as count=1. Step-2: The binary produced-image ( Ti ) for i= 1 to N and Ti is 8 bits. Let the eight bits of Ti are K 1K 2 K 3 K 4 K 5 K 6 K 7 K 8 where, K 1 = k 1k 2k 3k 4k 5k 6k 7 k 8 , K 2 = k 2 k 3k 4 k 5 k 6 k 7 k 8 , K 3 = k 3k 4 k 5 k 6 k 7 k 8 , K4 = k 4 k 5k 6 k 7 k 8 , K 5 = k 5k 6 k 7 k 8 , K 6 = k 6 k 7 k 8 . Step-3 If K 1 = 01111100 then recover the bits 011. Concatenate it to d. Else if K 1 = 01111101 then recover the bits 010. Concatenate it to d. Else if K 1 = 01111110 then recover the bits 001. Concatenate it to d. Else if K 1 = 01111111 then recover the bits 000. Concatenate it to d. Else if K 1 = 10000000 then recover the bits 111. Concatenate it to d. Else if K 1 = 10000001 then recover the bits 110. Concatenate it to d. Else if K 1 = 10000010 then recover the bits 101. Concatenate it to d. Else if K 1 = 10000011 then recover the bits 100. Concatenate it to d. The above extraction process will continue for K 2 to K6 . Step-4: Set count=count+3. If count < 18 then go to step3, otherwise go to step-5. Step-5: Change the 18 bits of ‘d’ to decimal and then multiply by 7, which is the length of the embedded message in bits, let it be ‘n’. Step-6:  n - 18  Once again set d to zero, and for i = 1 to , 3 reiterate step3. Then the secret message is obtained, i.e ‘d’, with ‘n’ bits length. Now move to step-7. Step-7: Recover the secret message by converting the binary data ‘d’ to its corresponding characters and then display “successful extraction”.

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

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H. Dadgostar, F. Afsari, Image steganography based on intervalvalued intuitionistic fuzzy edge detection and modified LSB. Journal of information security and applications. 2016, Pages 27.doi: 10.1016/j.jisa.2016.07.001. G. Swain, Digital image steganography using variable length group of bits substitution. Procedia Computer Science, 85, 2016. Pages 31–38. A. K. Sahu, G. Swain, A Review on LSB substitution and PVD based image steganography techniques. Indonesian Journal of

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Electrical Engineering and Computer Science, 2(3), 2016. Pages 712-719. A. Pradhan, A. K. Sahu, G. Swain, & K. R. Sekhar, Performance evaluation parameters of image steganography techniques. International Conference on Research Advances in Integrated Navigation Systems, 2016,Pages1-8. DOI:10.1109/RAINS.2016. 7764399 X. Zhou, W. Gong, W. Fu & L. Jin, An improved method for LSB based color image steganography combined with cryptography. In Computer and Information Science (ICIS), IEEE/ACIS, 2016, Pages 1-4. A. K. Sahu, M. Sahu, Digital image steganography techniques in spatial domain: a study, International Journal of Pharmacy & Technology, Vol. 8, Issue No.4, 2016, Pages 5205- 5217 J. Bai, C. C. Chang, T. S. Nguyen, C. Zhu, & Y. Liu, A High Payload Steganographic Algorithm Based on Edge Detection. Displays. 2017, Pages 42-45. K. S.Shet, A. R. Aswath, M. C. Hanumantharaju & X. Z. Gao, Design and development of new reconfigurable architectures for LSB/multi-bit image steganography system. Multimedia Tools and Applications, 2016, Pages 1-23. G. Swain, & S. K. Lenka, A technique for secret communication using a new block cipher with dynamic steganography. International Journal of Security and Its Applications, 6(4), 2012, Pages 13–24. R. Kumar, & S. Chand, A reversible data hiding scheme using bit flipping strategy, Journal of Discrete Mathematical Sciences and Cryptography, 19(2), 2016, Pages 331-345. D. C. Wu, & W. H. Tsai, A steganographic method for images by pixel-value differencing. Pattern Recognition Letters, 24(9–10), 2003, Pages 1613–1626. G. Swain, Steganography in digital images using maximum difference of neighboring pixel values. International Journal of Security and Its Applications, 7(6), 2013, Pages 285–294. Swain G, Lenka SK. Classification of image steganography techniques in spatial domain: A study, International Journal of Computer Science & Engineering Technology (IJCSET). 2014; 5: 219 232. A. Nilizadeh & A. R. N. Nilchi, A novel steganography method based on matrix pattern and LSB algorithms in RGB images. In Swarm Intelligence and Evolutionary Computation (CSIEC), 2016, Pages pp. 154-159. S. Khan, N. Ahmad & M. Wahid, Varying index varying bits substitution algorithm for the implementation of VLSB steganography. Journal of the Chinese Institute of Engineers, 39(1), 2016, Pages 101-109. M. Khodaei, B. S. Bigham, & K. Faez, Adaptive Data Hiding, Using Pixel-Value-Differencing and LSB substitution. Cybernetics and Systems, 47(8), 2016, Pages 617-628. M. Hussain, A. W. A. Wahab, A. T. Ho, N.Javed, & K. H. Jung, A data hiding scheme using parity-bit pixel value differencing and improved rightmost digit replacement. Signal Processing: Image Communication, 50, 2017, Pages 44-57. X. Liao, and L. Ding, Data Hiding in Digital image using Four Pixel-value-differencing and Multiple-base Notational. In Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), IEEE. 2015 International Conference on, IEEE, 2015, Pages 76-80. G. Swain, Digital image steganography using nine-pixel differencing and modified LSB substitution. Indian Journal of Science and Technology, 7(9), 2014, Pages 1444–1450. A. Malik, G. Sikka, & H. K. Verma, A modified pixel-value differencing image steganographic scheme with least significant bit substitution method. International Journal of Image, Graphics and Signal Processing. 4, 2015, Pages 68–74. M. Juneja, & P. S. Sandhu, Designing of robust image steganography technique based on LSB insertion and encryption. International Conference on Advances in Recent Technologies in Communication and Computing, 2009, Pages 302–305. G. Swain, & S. K. Lenka, A novel steganography technique by mapping words with LSB array. International Journal of Signal and Imaging Systems Engineering, 8(1), 2015, Pages 115–122. Z. Wang, & A. Bovik, A universal image quality index. IEEE Signal Processing Letters, 9(3), 2002. Pages 81–84.

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Authors’ information Department of CSE, K L University, Guntur, Andhra Pradesh, India. E-mail: [email protected] Mr. Aditya Kumar Sahu was born and brought up in Odisha, India. He received B.Tech (I.T) degree from G.I.E.T, Gunupur, Odisha, India, under Biju Pattnaik University in 2007, M.Tech (CS) degree from M.I.T.S Rayagada, Odisha, India, under Berhampur University, in 2011. He is pursuing Ph.D. (CSE) in KL University. He is working as an assistant professor in the Department of Computer Science & Engineering, K L University, Vaddeswaram, Andhra Pradesh, India. Previously he had worked at MITS engineering college. Rayagada, and Odisha, India. He has more than 10 years of teaching experience and published 10 research articles in international journals and conferences. His research interests include network security and image steganography. Mr. Sahu is a life member of the Computer Society of India (CSI). Dr. Gandharba Swain was born and brought up in Odisha. He has received B.Sc. (Hons) degree from Berhampur University in 1995, MCA degree from VSS Universityof Technology (VSSUT), Burla, in 1999, M.Tech (CSE) degree from NIT, Rourkela, in 2004. He was awarded with PhD degree from SOA University, Bhubaneswar in 2014. He is working as a Professor in the Department of Computer Science & Engineering, K L University, Vaddeswaram, and Andhra Pradesh, India. Previously he worked at the GMR Institute of Technology, Rajam, Andhra Pradesh, India and at IACR Engineering College, Rayagada, Odisha, India. He has more than 16 years of teaching experience and more than 7 years of research experience. He has authored one text book, published more than 40 research articles in international journals and conferences in the areas of security, image processing and Image Steganography. He is a member of CSI, ISTE, IAENG, and ACM.

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