11.secure compressed image transmission using self organizing feature maps

Computer Engineering and Intelligent Systems www.iiste.org
ISSN 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.com

Abstract: Due to the widespread use of multimedia applications, the data communication channels feel

short of bandwidth with regard to channel capacity requirements. As such, the need for improved image

compression techniques, together with image security, is increasing day by day. In this paper, the concept of

compressed image security has been explored. 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 of 0’s and 1’s are scrambled by a typical scrambler in

order to secure the image data from the unauthorized receiver. The simulation results demonstrate the

improved coding efficiency of the proposed method, when compared with JPEG, in addition to providing

the message security. The proposed scheme achieves a compression ratio upto 38:1.

Keywords: SOFM, Entropy Coding, Codewords, Image Security, Scrambler, encryption, JPEG, Arithmetic

Coding..

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1. 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

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of 0’s and 1’s are scrambled by a typical scrambler shown in Fig. 2 in order to secure the image data from

the unauthorized receivers.

3. Self Organizing Feature Maps:

Self Organizing Feature Map (SOFM) has formed a basis for a great deal of research into applying network

models to the problem of codebook design in Vector Quantization [5]. The SOFM introduced by Kohonen

is an unsupervised learning method which has both clustering and visualization properties and creates a

correspondence between the input space of stimuli and the output space constituted of the codebook

elements (the code words or neurons) [6]. The learning algorithm ensures that the most highly activated

node as well as its neighbors move towards a sample presented to the network. The networks are self

organizing 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 of

weight vectors are arbitrary. In the SOFM algorithm, the vector X is used to update not only the winning

class 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 of

classes, which are in the neighborhood of the winning class Ci at time t. The subscript ‘j’ represents the

index 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 codewords

does not exist; instead, an entire sequence of source symbols (or message) is assigned a single arithmetic

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codeword. As the number of symbols in the message increases, the interval used to represent it becomes

smaller 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 changed

with 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 the

generation of the Key ‘K’. Similarly Qi=0 means that ai =1. For unscrambling the received message, the

receiver has to know the value of N, the PN-sequence and its starting point. Thus, the unscrambling

becomes very difficult even for smaller values of N, thus minimizing the problem of error propagation

6. 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 8

bits per pixel over a PC with Intel Pentium IV, 2.9 GHz and 256MB RAM under Windows-XP operating

system. The ‘Lena’ and ‘Couple’ images are used for training the initial set and codebook design. The

performance of the proposed technique is tested for images ‘Einstein’ and ‘Woman’, which are outside the

training sequence. The performance is measured for various codebook sizes of 2n where n is an integer

varying from 5 to 8, and then compression efficiency is measured in terms of compression ratio (CR) which

is defined as:
.

… … … 3
.

… … 4
!" #

The quality of the decoded image is measured in terms of Peak-Signal-to-Noise-Ratio (PSNR) which is

defined as:

2551
%&' 10 log!- . ? … … 5
1 =;! ;!
∑ ∑ 45 6, 8 9 51 6, 8 :
1
2' ><- <- !

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Vol 3, No.4, 2012

Where NxM is the image size, f1 (i, j) is the random field of the original field intensities and f2 (i, j) denotes

the random field of the reconstructed image intensities. Fig. 3 outlines the simulation results of the image

transmission over the proposed secure image transmission systems. Variation of CR with PSNR and

Codebook size has been plotted. Standard grayscale image including Lena, Couple, Einstein were first

compressed using SOFM based image compression technique. The compressed image data has been

subsequently scrambled using the modified scrambler as shown in Fig. 2. The Scrambled image

corresponding to the given standard image ‘woman’ is shown in Fig. 4 along with the original and the

reconstructed images.

The waveforms of the Key bits, Arithmetic Coded bits and the scrambled image data has been presented in

Fig. 5. Further, the histogram of the original and encrypted image is shown in Fig. 6, and the Correlation

analysis 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 standard

image ‘Woman’ with the performance obtained by using JPEG standard using 8x8 default quantization for

compression. The results of the comparison have been presented in Fig. 8. It can be observed from the

figure 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 eaves

dropping. An SOFM technique has been used for data compression while as a typical scrambler has been

used for data encryption for message security. The proposed technique is shown to be very efficient for

secure image data compression with reduced bit error propagation. The transmission and reception of

encrypted 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 conventional

JPEG standard for image compression. The simulation of the proposed system has been carried out using

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Vol 3, No.4, 2012

MATLAB 7.02 and the performance results have been presented in the paper. The performance of the

proposed 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.pdf

2. 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

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QA QA

QB QB
SHIFT

SHIFT
QC QC

QD QD

Compressed image data Cryptogram Decrypted Compressed
Image data

Fig. 2 Proposed Image Data Scrambler

Fig. 3 (a) Variation of Compression ratio with PSNR (b) Variation of Compression ratio with

Codebook Size

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(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

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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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