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(IJCSIS) INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND INFORMATION SECURITY , VOL. 8 , NO. 4 , JULY 2010 1 A Novel approach of Data Hiding Using Pixel Mapping Method (PMM) Souvik Bhattacharyya , Lalan Kumar and Gautam Sanyal Abstract—Steganography is a process that involves hiding a mes- knowledge of steganography methodology the reader may sage in an appropriate carrier like image or audio. The carrier can be see [14], [17].Some Steganographic model with high security sent to a receiver without any one except the authenticated receiver features has been presented in [4], [5] and [6].Almost all only knows existence of the information. Considerable amount of work has been carried out by different researchers on steganography. digital ﬁle formats can be used for steganography, but the In this work the authors propose a novel Steganographic method for image and audio ﬁles are more suitable because of their high hiding information within the spatial domain of the gray scale image. degree of redundancy [17]. Fig. 1 below shows the different The proposed approach works by selecting the embedding pixels categories of steganography techniques. using some mathematical function and then ﬁnds the 8 neighborhood of the each selected pixel and map each two bit of the secret message in each of the neighbor pixel according to the features of that pixel in a speciﬁed manner.This approach can be modiﬁed for mapping of four bits of the secret message by considering more no of features of the embedding pixel. Before embedding a checking has been done to ﬁnd out whether the selected pixel or its neighbor lies at the boundary of the image or not. This solution is independent of the nature of the data to be hidden and produces a stego image with minimum Fig. 1. Types of Steganography degradation. A block diagram of a generic image steganographic system Keywords—Cover Image, Pixel Mapping Method (PMM), Stego Image. is given in Fig. 2. I. I NTRODUCTION TEGANOGRAPHY is the art and science of hiding infor- S mation by embedding messages within other, seemingly harmless messages. Steganography means “covered writing” in Greek. As the goal of steganography is to hide the presence of a message and to create a covert channel, it can be seen as the complement of cryptography, whose goal is to hide the content of a message. Another form of information hiding is digital watermarking, which is the process that embeds data called a watermark, tag or label into a multimedia object such that watermark can be detected or extracted later to make an Fig. 2. Generic form of Image Steganography assertion about the object. The object may be an image, audio, video or text only. A famous illustration of steganography A message is embedded in a digital image (cover image) is Simmons’ Prisoners’ Problem [16].An assumption can through an embedding algorithm, with the help of a secret key. be made based on this model is that if both the sender The resulting stego image is transmitted over a channel to the and receiver share some common secret information then receiver where it is processed by the extraction algorithm using the corresponding steganography protocol is known as then the same key. During transmission the stego image, it can be the secret key steganography where as pure steganography monitored by unauthenticated viewers who will only notice means that there is none prior information shared by sender the transmission of an image without discovering the existence and receiver. If the public key of the receiver is known of the hidden message. In this work a speciﬁc image based to the sender, the steganographic protocol is called public steganographic method for gray level image has proposed. In key steganography [2], [3] and [10].For a more thorough this method instead of embedding the secret message into the cover image a mapping technique has been incorporated to S. Bhattacharyya is with the Department of Computer Science and Engi- generate the stego image. This method is capable of extracting neering, University Institute of Technology, The University of Burdwan, West Bengal, India e-mail: (souvik.bha@gmail.com). the secret message without the presence of the cover image. L. Kumar is with the Central Institute of Mining and Fuel Research , This paper has been organized as following sections: Sec- Dhanbad, Jharkhand, India e-mail:(lalan.cimfr@gmail.com). tion II describes some related works, Section III deals with G. Sanyal is with with the Department of Computer Science and En- gineering, National Institute of Technologyy West Bengal, India e-mail: proposed method. Algorithms are discussed in Section IV (nitgsanyal@gmail.com). and Experimental results are shown in Section V. Section VI 207 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND INFORMATION SECURITY , VOL. 8 , NO. 4 , JULY 2010 2 contains the analysis of the results and Section VII draws the D. Data Hiding by the method proposed by Ahmad T et al. conclusion. In this work [1] a novel Steganographic method for hiding information within the spatial domain of the grayscale image II. R ELATED W ORKS has been proposed. The proposed approach works by dividing A. Data Hiding by LSB the cover into blocks of equal sizes and then embeds the message in the edge of the block depending on the number of Various techniques about data hiding have been proposed ones in left four bits of the pixel. in literatures. One of the common techniques is based on manipulating the least-signiﬁcant-bit (LSB) [8], [9] and [13], [15]planes by directly replacing the LSBs of the cover-image III. P ROPOSED M ETHOD with the message bits. LSB methods typically achieve high In this section the authors propose a new method for capacity but unfortunately LSB insertion is vulnerable to slight information hiding within the spatial domain of any gray scale image manipulation such as cropping and compression. image.This method can be considered as the improved version of [7].The input messages can be in any digital form, and are B. Data Hiding by PVD often treated as a bit stream. Embedding pixels are selected based on some mathematical function which depends on the The pixel-value differencing (PVD) method proposed by pixel intensity value of the seed pixel and its 8 neighbors Wu and Tsai [18] can successfully provide both high embed- are selected in counter clockwise direction. Before embedding ding capacity and outstanding imperceptibility for the stego- a checking has been done to ﬁnd out whether the selected image. The pixel-value differencing (PVD) method segments embedding pixels or its neighbors lies at the boundary of the the cover image into non overlapping blocks containing two image or not. Data embedding are done by mapping each two connecting pixels and modiﬁes the pixel difference in each or four bits of the secret message in each of the neighbor pixel block (pair) for data embedding. A larger difference in the based on some features of that pixel. Fig.5 and Fig.6 shows original pixel values allows a greater modiﬁcation. In the the mapping information for embedding two bits or four bits extraction phase, the original range table is necessary. It is respectively. used to partition the stego-image by the same method as used to the cover image. Based on PVD method, various approaches have also been proposed. Among them Chang et al. [12]. proposes a new method using tri-way pixel-value differencing which is better than original PVD method with respect to the embedding capacity and PSNR. C. Data Hiding by GLM In 2004, Potdar et al.[11] proposes GLM (Gray level mod- Fig. 5. Mapping Technique for embedding of two bits iﬁcation) technique which is used to map data by modifying the gray level of the image pixels. Gray level modiﬁcation Steganography is a technique to map data (not embed or hide it) by modifying the gray level values of the image pixels. GLM technique uses the concept of odd and even numbers to map data within an image. It is a one-to-one mapping between the binary data and the selected pixels in an image. From a given image a set of pixels are selected based on a mathematical function. The gray level values of those pixels are examined and compared with the bit stream that is to be mapped in the image. Fig. 3. Data Embedding Process in GLM Fig. 6. Mapping Technique for embedding of four bits Extraction process starts again by selecting the same pixels required during embedding. At the receiver side other different Fig. 4. Data Extraction Process in GLM reverse operations has been carried out to get back the original information. 208 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND INFORMATION SECURITY , VOL. 8 , NO. 4 , JULY 2010 3 IV. A LGORITHMS Let C be the original 8 bit gray scale image of size N x N i.e. C = (Pij | 0 ≤ i < N, 0 ≤ j < N, Pij ∈ 0, 1, . . . , 255). Let MSG be the n bit secret message represented as MSG =(mk | 0 ≤ k < n, mk ∈ 0, 1).A seed pixel Prc can be selected with row (r) and column (c). Next step is to ﬁnd the 8 neighbors Pr c of the pixel Prc such that r = r + l , c = c + l ,−1 ≤ l ≤ 1. The embedding process will be ﬁnished when all the bits of every bytes of secret message are mapped or embedded. Fig. 7. A snapshot of data embedding process for two bits A. Data Embedding Method for embedding of two bits Algorithm of the embedding method are described as : • Input : Cover Image(C), Message (MSG). • Find the ﬁrst seed pixel Prc . • count = 1. • while (count ≤ n) • begin (for embedding message in message surrounding a seed pixel). • cnt=Count number of ones of one of the Pr c of intensity (V). • mk =Get next msg bit. • count = count + 1. • mk+1 =Get next msg bit. • count = count + 1. Fig. 8. DFA for embedding process of two bits. • Bincvr= Binary of V. • If(mk = 0 & mk+1 = 1) • Bincvr(zerothbit) = 0 • BinMsg= ” ”. • If(cnt mod 2 = 0) • Find the ﬁrst seed pixel Prc . • Bincvr(f irstbit) = ¬Bincvr(f irstbit) • I=0. • If(mk = 0 & mk+1 = 0) • While (count ≤ N ) • Bincvr(zerothbit) = 1 • begin (for extract message in message around a seed • If(cnt ÷ 2 = 0) pixel). • Bincvr(f irstbit) = ¬Bincvr(f irstbit) • Get the (First/Next) neighbor pixel Pr c . • If(mk = 0 & mk+1 = 0) • cnt=Count number of ones of one of the Pr c of intensity • Bincvr(zerothbit) = 0 (V). • If(cnt mod 2 = 0) • Bincvr= Binary of V. • Bincvr(f irstbit) = ¬Bincvr(f irstbit) • Binmsg(i)=ZerothBit of Bincvr. • If(mk = 0 & mk+1 = 1) • count = count + 1. • Bincvr(zerothbit) = 1 • i = i + 1. • If(cnt mod 2 = 0) • Binmsg(i)=Enters according to One of ones in the inten- • Bincvr(f irstbit) = ¬Bincvr(f irstbit) sity(1 for odd :0 for even). • End • i = i + 1. • Get the next neighbor pixel Pr c for embedding based • End. on previous Pr c and repeat. • Get the next neighbor pixel Pr c for embedding based • End on previous Pr c and repeat. • Return the stego image (S). • End loop. • Binmsg is converted back to Original message. • Return Original Message. B. Data Extraction Method for extraction of two bits • End. The process of extraction proceeds by selecting those same pixel with their neighbors. The extracting process will be ﬁnished when all the bits of every bytes of secret message are C. Data Embedding Method for embedding four bits extracted. Algorithm of the extraction method are described Algorithm of the embedding method are described as : as : • Input : Cover Image(C), Message (MSG). • Input : Stego image (S) , count. • Find the ﬁrst seed pixel Prc . • count = count ÷ 2. • count = 1. 209 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND INFORMATION SECURITY , VOL. 8 , NO. 4 , JULY 2010 4 • count = count ÷ 2. • BinMsg= ” ”. • Find the ﬁrst seed pixel Prc . • I=0. • While (count ≤ N ) • begin (for extract message in message around a seed pixel). • Get the (First/Next) neighbor pixel Pr c . • cnt=Count number of ones of one of the Pr c of intensity (V). Fig. 9. A snapshot of data extracting process for extraction of two bits • Bincvr= Binary of V. • Binmsg(i)=3rd Bit of Bincvr from Right. • while (count ≤ n) • i = i + 1. • begin (for embedding message in message surrounding a • Binmsg(i)=2nd Bit of Bincvr from Right. seed pixel). • i = i + 1. • mk =Get next msg bit. • Binmsg(i)=ZerothBit of Bincvr. • count = count + 1. • i = i + 1. • Mask the 5TH bit from left with the mk in ’Bincvr’ • If (cnt mod 2 = 0) (i.e. it is even ) Binmsg(i)=0 Else • mk+1 =Get next msg bit. Binmsg(i)=1 • count = count + 1. • Binmsg(i)=Enters according to One of ones in the inten- • Mask the 6TH bit from left with the mk+1 in ’Bincvr’ sity(1 for odd :0 for even). • cnt=Count number of ones of one of the Pr c of intensity • i = i + 1. (V). • count = count + 1. • mk+2 =Get next msg bit. • End. • count = count + 1. • Get the next neighbor pixel Pr c for embedding based • mk+3 =Get next msg bit. on previous Pr c and repeat. • count = count + 1. • End loop. • Bincvr= Binary of V. • Binmsg is converted back to Original message. • If(mk+2 = 0 & mk+3 = 1) • Return Original Message. • Bincvr(zerothbit) = 0 • End. • If(cnt mod 2 = 0) One important point needs to be kept in mind that a speciﬁc • Bincvr(f irstbit) = ¬Bincvr(f irstbit) order for selecting the neighbors of the seed pixel has to be • If(mk+2 = 0 & mk+3 = 0) maintained for embedding / mapping process and also for the • Bincvr(zerothbit) = 1 process of extraction other wise it would not be possible to • If(cnt ÷ 2 = 0) retrieve the data in proper sequence. This sequence has been • Bincvr(f irstbit) = ¬Bincvr(f irstbit) shown in Figure 8. • If(mk+2 = 0 & mk+3 = 0) • Bincvr(zerothbit) = 0 • If(cnt mod 2 = 0) • Bincvr(f irstbit) = ¬Bincvr(f irstbit) • If(mk+2 = 0 & mk+3 = 1) • Bincvr(zerothbit) = 1 • If(cnt mod 2 = 0) • Bincvr(f irstbit) = ¬Bincvr(f irstbit) • End • Get the next neighbor pixel Pr c for embedding based on previous Pr c and repeat. • End Fig. 10. Sequence of data embedding • Return the stego image (S). D. Data Extraction Method for extracting four bits The process of extraction proceeds by selecting those same E. Pixel Selection Method pixel with their neighbors. The extracting process will be Random Pixel Generation for embedding message bits is de- ﬁnished when all the bits of every bytes of secret message are pendent on the intensity value of the previous pixel selected.It extracted. Algorithm of the extraction method are described includes a decision factor (dp) which is dependent on intensity as : with a ﬁxed way of calculating the next pixel.The algorithm • Input : Stego image (S) , count. for selection of pixel for embedding is described below: 210 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND INFORMATION SECURITY , VOL. 8 , NO. 4 , JULY 2010 5 • Input: C , previous pixel position (x,y),pixel intensity value (v). • Consider dp (Decision Factor)=1 if (intensity ≤ 80),dp=2 if (intensity ≥ 80 & ≤ 160) ,dp=3 if (intensity > 160 & ≤ 255). • t = x + 2 + dp • if (t ≥ N )m = 2, n = y + 2 + dp • else m = x + 2 + dp, n = y • Return m and n. • End Fig. 13. A Segment of Cover Image with selected pixel Fig. 11. Snapshot of Selected Pixel for embedding. Fig. 14. A Segment of Stego Image with selected pixel with the embedded msg segment ”I am an Indian” (two bits per pixel) In Fig 15 shows the segment of Lena as cover image and Fig 16 shows the same segment of Lena as stego image after embedding the message (four bits per pixel) ”I am an Indian, India is my country” on that segment. Fig. 12. DFA for pixel selection. V. E XPERIMENTAL R ESULTS In this section the authors present the experimental results of the proposed method based on two benchmarks techniques to evaluate the data hiding performance based on embedding of two bits or four bits respectively. First one is the capacity Fig. 15. A Segment of Cover Image with selected pixel of hiding data and another one is the imperceptibility of the stego image, also called the quality of stego image. The quality of stego-image should be acceptable by human eyes. The authors also present a comparative study of the proposed methods with the existing methods like PVD,GLM and the methods proposed by Ahmad T et al.by computing embedding capacity, mean square error (MSE) and peak signal-to noise ratio (PSNR).The authors also compute the normalized cross correlation coefﬁcient for computing the similarity measure between the cover image and stego image. In this section experimental result of stego image are shown based on two Fig. 16. A Segment of Stego Image with selected pixel with the embedded well known images: Lena and Pepper. In Fig 13 a segment of msg segment ”I am an Indian, India is my country” (four bits per pixel) Lena as cover image has been shown. Fig 14 shows the same segment of Lena as stego image after embedding the message In Fig 17 shows the image of Lena as cover and also as (two bits per pixel) ”I am an Indian” on that segment. stego after embedding the message ”I am an Indian and I 211 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND INFORMATION SECURITY , VOL. 8 , NO. 4 , JULY 2010 6 feel proud to an Indian.”(four bits per pixel). Fig 18 shows The PSNR is computed using the following formulae: the same with Pepper as the image. P SN R = 10 log10 2552 / M SE db. A comparative study of PSNR of various methods has been illustrated in ﬁgure 21 and ﬁgure 22 respectively. Fig. 17. A) Cover Image B) Stego Image of Lena after embedding ”I am an Indian and I feel proud to an Indian.” Fig. 21. Comparison of PSNR after embedding two bits per pixel Fig. 18. A) Cover Image B) Stego Image of Pepper after embedding ”I am an Indian and I feel proud to an Indian.” A comparative study of the embedding capacity with other methods has been illustrated in ﬁgure 19 (two bits per pixel) and ﬁgure 20 (four bits per pixel) respectively. Fig. 22. Comparison of PSNR after embedding four bits per pixel B. Similarity Measure For comparing the similarity between cover image and the stego image, the normalized cross correlation coefﬁcient (r) has been computed. In statistics, correlation indicates the strength and direction of a linear relationship between two Fig. 19. Comparision of embedding capacity for two bits random variables. The correlation coefﬁcient ρxy between two random variables X and Y with expected values µx andµy and standard deviations σx and σy is deﬁned as cov(x, y) E((X − µx )(Y − µy )) ρx,y = = σx σy σx σy where E is the expected value operator and cov means covariance. The value of correlation is 1 in the case of an increasing linear relationship, -1 in the case of a decreasing Fig. 20. Comparision of embedding capacity for four bits linear relationship, and some value in between in all other cases, indicating the degree of linear dependence between the ** For PVD method all the images used are of size 512x512. variables. Cross correlation is a standard method of estimating the A. Peak Signal to Noise Ratio (PSNR) degree to which two series are correlated. Consider two series PSNR measures the quality of the image by comparing x(i) and y(i) where i=0,1,2,. . . ,N-1. The cross correlation r at the original image or cover image with the stego-image, i.e. delay d is deﬁned as it measures the percentage of the stego data to the image percentage. The PSNR is used to evaluate the quality of the − mx)(y(i − d) − my)] i [(x(i) stego-image after embedding the secret message in the cover. r= 2 2 Assume a cover image C(i,j) that contains N by N pixels and i (x(i) − mx) i (y(i − d) − my) a stego image S(i,j) where S is generated by embedding / where mx and my are the means of the corresponding series. mapping the message bit stream. Mean squared error (MSE) The cross-correlation is used for template matching which is of the stego image as follows: motivated through the following formula N N 1 r= f (x, y)t(x − u, y − v) M SE = [C(ij) − S(ij)]2 [N × N ] i=1 j=1 x y 212 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND INFORMATION SECURITY , VOL. 8 , NO. 4 , JULY 2010 7 where f is the image and the sum is over x, y under the the message bits are not directly embedded at the pixels of window containing the feature t positioned at u, v. the cover image, steganalysis may be able to ﬁnd out the Similarity measure of two images can be done with the embedded bits but can not be able to extract the original help of normalized cross correlation generated from the above message bits.PSNR value of the proposed method (two bits concept using the following formula: per pixel) for various sizes of the image better than compared to other methods. (C(i,j)−m1 )(S(i,j)−m2 ) r= ( 2 2 C(i,j)−m1 ) ( S(i,j)−m2 ) VII. C ONCLUSION Here C is the cover image, S is the stego image,m1 is the The work dealt with the techniques for steganography as mean pixel value of the cover image and m2 is the mean pixel related to gray scale image. A new and efﬁcient steganographic value of stego image. It has been seen that the correlation method for embedding secret messages into images without coefﬁcient computed here for all the images is almost one producing any major changes has been proposed. Although in which indicates the both the cover image and stego image are this method it has been shown that each two bit or four bit of highly correlated i.e. both of these two images are same. of the secret message has been mapped in the pixels of the cover image,but this method can be extended to map 8 no of bits per pixel by considering more no of features of the embedding pixels.This method also capable of extracting the secret message without the cover image. This approach may be modiﬁed to work on color images also. R EFERENCES [1] Ahmad T. Al-Taani. and Abdullah M. AL-Issa. A novel steganographic method for gray-level images. International Journal of Computer, Information, and Systems Science, and Engineering, 3, 2009. [2] RJ Anderson. Stretching the limits of steganography. Information Hiding, Springer Lecture Notes in Computer Science, 1174:39–48, 1996. [3] Ross J. Anderson. and Fabien A.P.Petitcolas. On the limits of steganog- raphy. IEEE Journal on Selected Areas in Communications (J-SAC), Special Issue on Copyright and Privacy Protection, 16:474–481, 1998. [4] Souvik Bhattacharyya. and Gautam Sanyal. Study of secure steganog- Fig. 23. Comparision of Similarity Measure for Lena raphy model. In Proceedings of International Conference on Ad- vancedComputing and Communication Technologies (ICACCT-2008), Panipath,India, 2008. [5] Souvik Bhattacharyya. and Gautam Sanyal. An image based steganog- raphy model for promoting global cyber security. In Proceedings of International Conference on Systemics, Cybernetics and Informatics, Hyderabad,India, 2009. [6] Souvik Bhattacharyya. and Gautam Sanyal. Implementation and design of an image based steganographic model. In Proceedings of IEEE International Advance Computing Conference, Patiala ,India, 2009. [7] Souvik Bhattacharyya. and Gautam Sanyal. Hiding data in images using pixel mapping method (pmm). 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In IEEE International Conference on Industria In this article the authors proposed an efﬁcient image lInformatics., pages 355–368, Berlin, Germany, 2004. based steganography approach for hiding information in a [12] P Huang. K.C. Chang., C.P Chang. and T.M Tu. A novel image steganography method using tri-way pixel value differencing. Journal gray scale image. Comparison has been shown with some of Multimedia, 3, 2008. existing methods like PVD, GLM and the technique proposed [13] Y. K. Lee. and L. H.Chen. High capacity image steganographic model. by Ahmad T et al. From the experimental results in can be IEE Proc.-Vision, Image and Signal Processing, 147:288–294, 2000. [14] N.F.Johnson. and S. Jajodia. Steganography: seeing the unseen. IEEE seen that the embedding capacity of the proposed method is Computer, 16:26–34, 1998. better compared to PVD, GLM and the other technique in most [15] C.F. Lin. R.Z. Wang. and J.C. Lin. Image hiding by optimal lsb cases and also the similarity measures proves that the proposed substitution and genetic algorithm. Pattern Recognition, 34:671–683, 2001. method better among these four methods which ensures that [16] Gustavus J. Simmons. The prisoners’ problem and the subliminal cover image and the stego image is almost identical. As channel. Proceedings of CRYPTO., 83:51–67, 1984. 213 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND INFORMATION SECURITY , VOL. 8 , NO. 4 , JULY 2010 8 [17] JHP Eloff. T Mrkel. and MS Olivier. An overview of image steganog- raphy. In Proceedings of the ﬁfth annual Information Security South Africa Conference., 2005. [18] D.C. Wu. and W.H. Tsai. A steganographic method for images by pixel- value differencing. Pattern Recognition Letters, 24:1613–1626, 2003. Souvik Bhattacharyya received his B.E. degree in Computer Science and Technology from B.E. College, Shibpur, India, presently known as Bengal Engineering and Science University (BESU) and M.Tech degree in Computer Science and Engineer- ing from National Institute of Technology, Durgapur, India. Currently he is working as a Senior Lecturer in Computer Science and Engineering Department at University Institute of Technology, The University of Burdwan. He has a good no of research publica- tion in his credit. His areas of interest are Natural Language Processing, Network Security and Image Processing. Dr. Lalan Kumar received his Ph.D. degree from the Indian School of Mines(ISM), Dhanbad Jhark- hand. Joined National Informatics (NIC) Centre, under Planning Commission of Govt. of India in 1990 and worked till 25th Nov.’02. Joined Central Institute of Mining and Fuel Research (CIMFR) on 25th Nov.’02. Prior to joining CMRI as Scientist, he has studied, designed, developed and implemented many packages for the District, state and some of the packages are running in almost all the districts of the country. He has been appointed as a panel expert for local governance and community engagement for the various departments of state government. He has published more than 50 papers in International and National Journals of repute. He is member of many advisory board/Review committee/Chairman/Resource person of Universities/journals/ International/national Seminar cum Symposia/Institutions.Dr.Kumar has orga- nized many International and National seminar cum exhibition time to time and edited books. Gautam Sanyal has received his B.E and M.Tech degree from Regional Engineering College (REC), Durgapur, now, National Institute of Technology (NIT), Durgapur, West Bengal, India. He has re- ceived Ph.D (Engg.) from Jadavpur University, Kolkata, West Bengal, India, in the area of Robot Vision. He possesses an experience of more than 25 years in the ﬁeld of teaching and research. He has published nearly 40 research papers in International and National Journals / Conferences. His current research interests include Natural Language Process- ing, Stochastic modeling of network trafﬁc, High Performance Computing, Computer Vision. He is presently working as a Professor in the department of Computer Science and Engineering and also holding the post of Dean (Student’s Welfare) at National Institute of Technology, Durgapur, West Bengal, India. 214 http://sites.google.com/site/ijcsis/ ISSN 1947-5500