Computer Engineering and Intelligent Systems                                                  www.iiste.orgISSN 2222-1719 ...
Computer Engineering and Intelligent Systems                                                 www.iiste.org ISSN 2222-1719 ...
Computer Engineering and Intelligent Systems                                                   www.iiste.orgISSN 2222-1719...
Computer Engineering and Intelligent Systems                                                                      www.iist...
Computer Engineering and Intelligent Systems                                                      www.iiste.orgISSN 2222-1...
Computer Engineering and Intelligent Systems                                                www.iiste.orgISSN 2222-1719 (P...
Computer Engineering and Intelligent Systems                                          www.iiste.orgISSN 2222-1719 (Paper) ...
Computer Engineering and Intelligent Systems                                        www.iiste.orgISSN 2222-1719 (Paper) IS...
Computer Engineering and Intelligent Systems                                       www.iiste.orgISSN 2222-1719 (Paper) ISS...
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11.secure compressed image transmission using self organizing feature maps

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11.secure compressed image transmission using self organizing feature maps

  1. 1. Computer Engineering and Intelligent Systems www.iiste.orgISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)Vol 3, No.4, 2012 Secure Compressed Image Transmission using Self Organizing Feature Maps G. Mohiuddin Bhat (Corresponding Author) University Science Instrumentation Centre, University of Kashmir, Hazratbal, Srinagar; gmbhat_ku@yahoo.co.in Asifa Baba School of Technology, Islamic University of Science & Technology, Awantipora; asifababa@gmail.comAbstract: Due to the widespread use of multimedia applications, the data communication channels feelshort of bandwidth with regard to channel capacity requirements. As such, the need for improved imagecompression techniques, together with image security, is increasing day by day. In this paper, the concept ofcompressed image security has been explored. The input image data is applied to the image partitioning andvectorization block where the whole image is divided into 4x4 non-overlapping blocks. Each block servesas a vector of 16 elements for the Self Organizing Feature Map (SOFM) network by which the indexes ofthe codewords are determined. These indexes are coded in a binary stream of 0’s and 1’s using a variablelength Entropy Coding Scheme. These long strings of 0’s and 1’s are scrambled by a typical scrambler inorder to secure the image data from the unauthorized receiver. The simulation results demonstrate theimproved coding efficiency of the proposed method, when compared with JPEG, in addition to providingthe message security. The proposed scheme achieves a compression ratio upto 38:1.Keywords: SOFM, Entropy Coding, Codewords, Image Security, Scrambler, encryption, JPEG, ArithmeticCoding.. 80
  2. 2. Computer Engineering and Intelligent Systems www.iiste.org ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online) Vol 3, No.4, 20121. Introduction: Message security is the science and study of methods for protecting message signals from unauthorized disclosure and modification. The message signals include digital data from computers and other communication systems, digital image data, analog messages including audio signals like speech signals, video signals and other instrumentation signals. Secure image transmission is of paramount importance in defense, surveillance and other strategic applications. The compressed image data can be encrypted before transmission over an insecure channel. The image information can thus be denied to an unauthorized receiver thereby safeguarding eavesdropping. However, an intended receiver equipped with proper ‘Key’ and the decryption technique should be able to recover the original image from the received encrypted data. Cryptography is the science of deliberately disguising the signals using typical ciphers so that they assume the form of noise signal for an unauthorized receiver. The noise-like (encrypted) signals can be decrypted or deciphered back to recover the original message signals [1]. Scramblers are a class of substitution ciphers and have been found to be suitable for various security requirements such as those used by cable and satellite TV operators and mobile phone service providers [2,3]. In this paper secure compressed image compression scheme is presented where the input image data is first compressed using Self Organizing Feature Maps (SOFM) based technique. The compressed image data is then scrambled using a typical scrambler so that it appears noise like for an unauthorized receiver [4]. The Simulation results for the compressed image transmission and reception using the proposed techniques have been presented in the paper for standard grayscale images. 2 Proposed technique of secure image transmission using SOFM based Image Compression: Block Diagram of the proposed compressed Image Encryption technique is shown in Fig. 1. The input image data is applied to the image partitioning and vectorization block where the whole image is divided into 4x4 non-overlapping blocks. Each block serves as a vector of 16 elements for the Self Organizing Feature Map (SOFM) network by which the indexes of the codewords are determined. These indexes are coded in a binary stream of 0’s and 1’s using a variable length Entropy Coding Scheme. These long strings 81
  3. 3. Computer Engineering and Intelligent Systems www.iiste.orgISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)Vol 3, No.4, 2012of 0’s and 1’s are scrambled by a typical scrambler shown in Fig. 2 in order to secure the image data fromthe unauthorized receivers.3. Self Organizing Feature Maps:Self Organizing Feature Map (SOFM) has formed a basis for a great deal of research into applying networkmodels to the problem of codebook design in Vector Quantization [5]. The SOFM introduced by Kohonenis an unsupervised learning method which has both clustering and visualization properties and creates acorrespondence between the input space of stimuli and the output space constituted of the codebookelements (the code words or neurons) [6]. The learning algorithm ensures that the most highly activatednode as well as its neighbors move towards a sample presented to the network. The networks are selforganizing in that nodes tend to attain weight vectors that capture characteristics of the input vector space,with the neighborhood relation translating into proximity in Euclidean space, even if the initial values ofweight vectors are arbitrary. In the SOFM algorithm, the vector X is used to update not only the winningclass but also its neighboring classes according to the following rule:For each vector X in the training 1. X is classified according to:X ∈Ci if X −Wi = minX −Wj .......... 1) ..(2. The weights Wj are then updated according to: W j (t ) + lr (X − W j (t ))2 if C j ∈ N (Ci , t ) W j (t + 1) =  ...(2) W j (t )  if C j ∉ N (Ci , t )Where W is the feature vector, lr is the learning parameter in the range of 0-1 and N (Ci, t) is the set ofclasses, which are in the neighborhood of the winning class Ci at time t. The subscript ‘j’ represents theindex of all vectors in the neighborhood of the ith feature vector.4. Binary Arithmetic Coding:In Arithmetic coding (AC) Scheme, a one to one correspondence between source symbols and codewordsdoes not exist; instead, an entire sequence of source symbols (or message) is assigned a single arithmetic 82
  4. 4. Computer Engineering and Intelligent Systems www.iiste.orgISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)Vol 3, No.4, 2012codeword. As the number of symbols in the message increases, the interval used to represent it becomessmaller and the number of information bits required to represent the interval becomes larger [7].5. Scrambler for Data:Fig. 2 shows the circuit diagram of the proposed scrambler. Here tap-gain values are continuously changedwith the help of a PN-sequence. Inputs QA, QB, QC and QD to the AND gates are the outputs of a 15-bit PN-sequence generator. The value of a Qi=1 means that the corresponding shift register stage is effective in thegeneration of the Key ‘K’. Similarly Qi=0 means that ai =1. For unscrambling the received message, thereceiver has to know the value of N, the PN-sequence and its starting point. Thus, the unscramblingbecomes very difficult even for smaller values of N, thus minimizing the problem of error propagation6. Simulation Results:The proposed Algorithm based on SOFM and Arithmetic Coding has been implemented using MATLAB-7.02 and the proposed algorithm has been simulated on various grayscale images of size 256x256 with 8bits per pixel over a PC with Intel Pentium IV, 2.9 GHz and 256MB RAM under Windows-XP operatingsystem. The ‘Lena’ and ‘Couple’ images are used for training the initial set and codebook design. Theperformance of the proposed technique is tested for images ‘Einstein’ and ‘Woman’, which are outside thetraining sequence. The performance is measured for various codebook sizes of 2n where n is an integervarying from 5 to 8, and then compression efficiency is measured in terms of compression ratio (CR) whichis defined as: . … … … 3 . … … 4 !" #The quality of the decoded image is measured in terms of Peak-Signal-to-Noise-Ratio (PSNR) which isdefined as: 2551 %& 10 log!- . ? … … 5 1 =;! ;! ∑ ∑ 45 6, 8 9 51 6, 8 : 1 2 ><- <- ! 83
  5. 5. Computer Engineering and Intelligent Systems www.iiste.orgISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)Vol 3, No.4, 2012Where NxM is the image size, f1 (i, j) is the random field of the original field intensities and f2 (i, j) denotesthe random field of the reconstructed image intensities. Fig. 3 outlines the simulation results of the imagetransmission over the proposed secure image transmission systems. Variation of CR with PSNR andCodebook size has been plotted. Standard grayscale image including Lena, Couple, Einstein were firstcompressed using SOFM based image compression technique. The compressed image data has beensubsequently scrambled using the modified scrambler as shown in Fig. 2. The Scrambled imagecorresponding to the given standard image ‘woman’ is shown in Fig. 4 along with the original and thereconstructed images.The waveforms of the Key bits, Arithmetic Coded bits and the scrambled image data has been presented inFig. 5. Further, the histogram of the original and encrypted image is shown in Fig. 6, and the Correlationanalysis of the plain image and encrypted image has been presented in Fig. 7.7. Comparison with JPEG:The performance of the proposed technique for image compression has been compared for the standardimage ‘Woman’ with the performance obtained by using JPEG standard using 8x8 default quantization forcompression. The results of the comparison have been presented in Fig. 8. It can be observed from thefigure that the proposed method outperforms the DCT based JPEG by up to 1 dB.8. Conclusion:The paper presents an interesting technique for fast mage data transmission with data security against eavesdropping. An SOFM technique has been used for data compression while as a typical scrambler has beenused for data encryption for message security. The proposed technique is shown to be very efficient forsecure image data compression with reduced bit error propagation. The transmission and reception ofencrypted image using the proposed technique has been tested experimentally on standard grayscale images.The proposed technique provides a compression ratio of about 38:1 which outperforms the conventionalJPEG standard for image compression. The simulation of the proposed system has been carried out using 84
  6. 6. Computer Engineering and Intelligent Systems www.iiste.orgISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)Vol 3, No.4, 2012MATLAB 7.02 and the performance results have been presented in the paper. The performance of theproposed secure image transmission system has been found to be satisfactory.References:1. Shaaban, E., “Data Compression Basics,”, 2002, Retrieved from- http://meseec.ce.rit.edu/eecc694-spring2000/694-5-9-2000.pdf2. M. Buer and J. Wallace, “Integrated Security for digital video broadcast”, IEEE Trans. Cons. Elect., Aug. 1996, vol. 42, No. 3, pp. 500-503.3. Dorothy Elizabeth and Robling Denning, “Cryptography and Data Security”, Addison Wesley, 1982.4. H. Feistel, “Cryptography and Computer Privacy”, Scientific America, May 1973, vol. 228, pp. 15-23,5. Wu, Chung-Ping & Kuo, C.-C.J., “Design of integrated multimedia compression and encryption systems,” IEEE Transactions on Multimedia, 2005, Vol. 7, No. 5, pp 828 – 839.6. P. Wayner, “A redundancy reducing Cipher”, Cryptologia, April 1988, vol. XII, pp. 107.7. J. B. Kam & G. I. Davida, “Structured design of substitutional permutation encryption networks”, IEEE Trans. Computers, Oct. 1979, vol. C-28, pp. 747-753. Input SOFM based Arithmetic Image Image Vector coding partition and Scrambler Coded quantization Bit stream Code book Fig. 1 Proposed Image Encryption using SOFM based Image Compression 85
  7. 7. Computer Engineering and Intelligent Systems www.iiste.orgISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)Vol 3, No.4, 2012 QA QA QB QB SHIFT SHIFT QC QC QD QD Compressed image data Cryptogram Decrypted Compressed Image data Fig. 2 Proposed Image Data ScramblerFig. 3 (a) Variation of Compression ratio with PSNR (b) Variation of Compression ratio withCodebook Size 86
  8. 8. Computer Engineering and Intelligent Systems www.iiste.orgISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)Vol 3, No.4, 2012 (a) (b) (c) Fig. 4 SOFM based Secure Image transmission using standard Image Woman (a) Original Image (b) Scrambled Image (c) Reconstructed Image (a) (b) (c) Fig. 5 Various Waveforms generated: (a) Key bits (b) Arithmetic Code (c) Scrambled Image data 87
  9. 9. Computer Engineering and Intelligent Systems www.iiste.orgISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)Vol 3, No.4, 2012 Fig. 6 Histograms of Original and Encrypted Image ‘Woman’ Fig. 7 Correlation analysis of original image and encrypted image Fig. 8 Performance comparison of the proposed method with JPEG for image woman 88
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