Iaetsd implementation of chaotic algorithm for secure image

Implementation of Chaotic Algorithm for Secure Image
Transcoding
PRADEEP K.G.M Dr. Ravikumar M.S.
IV SEM M.Tech Professor and Head
DEC, Dept. of E&C Dept. of E&C
KVGCE, Sullia KVGCE, Sullia
Pkgm77@gmail.com
ABSTRACT
The transcoding refers to a two-step process in which
the original data/file is decoded to an intermediate
uncompressed format which is then encoded into the
target format. Transcoding is the direct digital-to-
digital data conversion of one encoding to another.
This paper proposes a system of secure image
transcoder which mainly focuses on multimedia
applications such as web browsing through mobile
phones, in order to improve their delivery to client
devices with wide range of communication, storage
and display capabilities. This system based on CKBA
encryption ensures end to end security. The
performance of the system has been evaluated for
different images. It is verified that the proposed
system is having less resource complexity with good
performance.
Keywords: CKBA, Encryption, Transcoding
1. INTRODUCTION
Images are generally the collection of pixels. Basically
Image Encryption is a means that convert the image into
unreadable format. Many digital services require reliable
security in storage and transmission of digital images.
Due to the rapid growth of the internet in the digital
world today, the security of digital images has become
more important and attracted much attention. The
prevalence of multimedia technology in our society has
promoted digital images to play a more significant role
than the traditional texts, which demand serious
protection of user’s privacy for all applications.
Encryption techniques of digital images are very
important and should be used to frustrate opponent
attacks from unauthorized access.
Digital images are exchanged over various types of
networks. It is often true that a large part of this
information is either confidential or private. Encryption
is the preferred technique for protecting the transmitting
data. There are various encryption systems to encrypt and
decrypt image data. However, it can be argued that there
is no single encryption algorithm which satisfies the
different image types. In general, most of the available
encryption algorithms are used for text data. However,
due to large data size and real time constants, algorithms
that are good for textual data may not be suitable for
multimedia data. Although we can use the traditional
encryption algorithm to encrypt images directly, this may
not be a good idea for two reasons. First, the image size
is often large than text. Consequently, the traditional
encryption algorithms need a longer time to directly
encrypt the image data. Second, the decrypted text must
be equal to the original text but this requirement is not
necessary for image data. Due to characteristic of human
perception, a decrypted image containing small distortion
is usually acceptable. The intelligible information in an
image is due to the correlation among the image elements
in a given arrangement. This perceptive information can
be reduced by decreasing the correlation among image
elements using certain transformation techniques. In
addition to cryptography, chaotic based image security
techniques are getting significantly more sophisticated
and have widely used. The chaotic image transformation
techniques are perfect supplement for encryption that
allows a user to make some transformation in the image,
and then the image is totally distorted, so nobody could
see that what information could be shown through that
image. Thus, it is often used in conjunction with
cryptography so that the information is doubly protected,
that is, first it is transformed by chaotic map encryption
techniques, and then it is encrypted so that an adversary
has to find the hidden information before the decryption
takes place.
Nowadays, communication networks such as mobile
networks and the internet are well developed. However
they are public networks and are not suitable for the
direct transmission of confidential messages. To make
use of the communication networks already developed
and to keep the secrecy simultaneously, cryptographic
techniques need to be applied. Traditional symmetric
ciphers such as data encryption standard (DES) are
designed with good confusion and diffusion properties.
These two properties can also be found in chaotic
systems which are usually ergodic and are sensitive to
system parameters and initial conditions. In recent years,
a number of chaos based cryptographic schemes have
been proposed. Some of them are based on one
dimensional chaotic logistic maps and are applied to data
sequence or document encryption. This project mainly
focuses on bit rate transcoding. Recently many papers
have been proposed with the idea of stream ciphering.
Generally encryption can be categorized into three viz
complete encryption, selective encryption and joint
encryption. In complete encryption entire data will be
encrypted whereas in selective encryption only a part of
data. Joint encryption refers to the encryption performed
during compression.
The growth in the processing of digital multimedia data
is rapid, by the increasing demands of the content
consumers with a widening variety of digital equipments;
require bit streams to be modified after transmission. A
transcoder can be placed in the channel to reduce the bit
rate prior to retransmission to end user devices. A
simplified view of a typical broadcasting system is
shown in Fig.1.1.
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ISBN: 378-26-138420-01

Fig. 1.1: Potential broadcasting network
One of the challenges in using such network is protecting
the intellectual property rights of the content owners and
producers when the data is transmitted over a public
channel. In traditional cryptosystem a secret key is used
to encrypt and decrypt the data. There are two main
drawbacks in using traditional cryptosystems to protect
image data.
First one is that the traditional systems are either too
slow or in need of excessive complexity in real time
operations with large volume of data. The second one is
that any modification of the cipher text generated using
on and off-the shelf cipher would render the resulting bit
stream undecipherable. An intuitive approach is to allow
the transcoder to decrypt the bit stream, prior to
transcoding, re-encryption and retransmission, as shown
in fig 1.2. While this approach is very effective in
ensuring efficient content delivery, it does not allow end
to end security.
Fig. 1.2 : Traditional transcoder with encrypted data
To ensure end to end security one of the possible
approaches is shown in Fig.1.3. Here transcoder stage is
made such that no plain text is freely available in the
intermediate stages of transcoding. For this purpose a
decode and re-encode stage is used with different
quantization value
Fig 1.3 secure transcoder using ciphers designed for
transcoding
The proposed secure image transcoder is having too
many multimedia applications such as medical imaging,
mobile web browsing, etc.
3. PROBLEM STATEMENT
There are two main drawbacks in using conventional
crypto-systems to protect image data. First one is that the
traditional systems are either too slow or in need of
excessive complexity in real time operations with large
volume of data. The second one is that any modification
of the cipher text generated using on and off-the shelf
cipher would render the resulting bit stream
undecipherable.
4. MOTIVATION
The motivation for image transcoding research is mainly
due to contemporary developments in the field of
security and compression of many multimedia
applications. In the digital world nowadays, the security
of digital images becomes more and more important
since the communications of digital products over
network occur more and more frequently. Furthermore,
special and reliable security in storage and transmission
of digital images is needed in many applications, such as,
medical imaging systems, military image
database/communications and confidential data.
5. METHODOLOGY
The proposed secure image transcoder system is
basically a modification of the existing jpeg encoder-
decoder algorithm. The main modifications are;
 Encryption
 Transcoding
Fig. 5.1: The proposed frame work
The proposed work consists of the following steps
STEP 1: Considering an input image of size M×N and
Apply 2-DCT to the image.
DCT is mathematical technique used to convert image
from spatial domain to frequency domain. JPEG image
compression standard use DCT. The discrete cosine
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transform is a fast transform. It is a widely used and
robust method for image compression. It has excellent
compaction for highly correlated data.DCT has fixed
basis images. DCT gives good compromise between
information packing ability and computational
complexity.
Fig. 5.2 : The 8×8 sub-image shown in 8-bit grayscale
 Each image is divided into 8 x 8 blocks. The
2D DCT is applied to each block image f(i,j),
with output being the DCT coefficients
F(u,v)/F(i,j) for each block
 If the input matrix is P[x,y] and the
transformed matrix is F[u,v] then the DCT for
the 8×8 block is computed using the
expression:
Forward DCT
 F[i,j]= C(i)C(j)∑ ∑ [ , ]cos
( )
cos
( )
(5.1)
Where C (i) and C (j) =
√
for i, j = 0
= 1 for all other values of i and j
and x, y,i, and j all vary from 0 through 7
STEP 2: CKBA encryption
The proposed chaotic key based algorithm is a complete
encryption technique. The encryption procedure of
CKBA can be briefly depicted as follows. Assume the
size of the plain-image is M × N. Shuffle the DCT matrix
using the generated random key. It can be performed
before or after the quantization stage. Incorporating a
shuffling algorithm in the spatial domain can result an
immense reduction in the compression ratio. Since, in
most of the multimedia applications, the larger
compression is a mandatory requirement, we cannot
implement the shuffling algorithm in the spatial domain.
Hence, performing the shuffling operation in the
transform domain, without affecting the compression
ratio is favorable. Thus, block-wise shuffling of DCT
matrix is performed so that compression remains intact.
Shuffling is performed based on a chaotic map.
Assume that the size of the plain-image is M × N. Select
an initial condition x (0) of a one-dimensional chaotic
system as the secret key of the encryption system defined
by the following logistic map (1).
x (i+1) = µ*x(i)(1 - x(i)) (5.2)
It has been proved that the system behaves chaotically if
the value of μ > 3.5699.
This chaotic system is run to make a chaotic sequence x
(i) for i varying from 0 to ((M×N)/8) −1. It is then
grouped into 8 bits to form an integer so that a pseudo
random array of (M×N)/64 integers are formed. By
avoiding the repeating elements, it is possible to form an
array of length 256. This array can be taken as an index
to shuffle the columns of input DCT matrix. This system
is a well encrypted system providing good compression
by suitably selecting the quantization matrix.
STEP 3: Applying quantization to the CKBA encrypted
image.
Quantization process aims to reduce the size of the size
of the DC and AC coefficient so that less bandwidth is
required for their transmission. The human eye responds
primarily to the DC coefficient and the lower spatial
frequency coefficients. Thus if the magnitude of a higher
frequency coefficient is below a certain threshold, the
eye will not detect it. This property is exploited in the
quantization phase by dropping –in practice, setting to
zero-those spatial frequency coefficients in the
transformed matrix whose amplitudes are less than a
defined threshold value.
The sensitivity of the human eyes varies with spatial
frequency, which implies that the amplitude threshold
below which the eye will detect a particular spatial
frequency also varies. In practice, therefore, the threshold
values used vary for each of the 64 DCT coefficients.
These are held in a two dimensional matrix known as
quantization table with the threshold value to be used
with a particular DCT coefficient in the corresponding
position in the matrix.
A common quantization matrix is
Quantized coefficient matrix values,
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This is an example of DCT coefficient matrix:
For example, using −415 (the DC coefficient)
and rounding to the nearest integer Round (-415/16)
=Round (-25.9375) = - 26
STEP 4: Applying entropy encoding to the quantized
coefficient matrix as
 Zigzag scanning
 Differential encoding
 Run length encoding
 Huffman encoding
Zigzag scanning
The output of typical quantization is a 2-D matrix of
values/ coefficients which are mainly zeros except for a
number of non-zero values in the top left hand corner of
the matrix. Clearly, if we simply scanned the matrix
using a line-by-line approach, then the resulting (1×64)
vector would contain a mix of non-zero and zero values.
In general, however, this type of information structure
does not lend itself to compression. In order to exploit
the presence of large number of zeros in the quantized
matrix, a ZIG-ZAG SCAN is used.
Fig. 5.4: Zigzag Scanning Pattern
DIFERENTIAL ENCODING
Differential encoding is used when the
amplitude of the values that make up the source
information cover a large range but the difference
between successive values is relatively small. Instead of
using a set of relatively large codeword’s to represents
the actual amplitude of each value, a set of smaller of
smaller codeword’s is used, each of which indicates only
the difference in amplitude between the present value
being encoded and the immediately preceding value.
RUNLENGTH ENCODING TECHNIQUE
It is used when the source information contains long
strings of the same symbols such as character, a bit or a
byte. Instead of sending the source information in the
form of independent codeword’s, it is sent by simply
indicating the particular symbol in each string together
with an indication of the number of symbols in the string.
For ex: 000000060040009 this string is represented as (7,
6) (2, 4) (3, 9)
HUFFMAN COMPRESSION TECHNIQUE
It is a method for the construction of minimum
redundancy Codes.
Huffman code procedure is based on the two
observations.
a. More frequently occurred symbols will have shorter
code words than symbol that occur less frequently.
b. The two symbols that occur least frequently will
have the same length.
Huffman coding algorithm working steps are as follows.
 Convert the given color image into grey level
image.
 Find the frequency of occurrence of symbols
(i.e. pixel value which is non-repeated).
 Calculate the probability of each symbol.
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 Probability of symbols are arranged in
decreasing order and lower probabilities are
merged and this step is continued until only
two probabilities are left and codes are
assigned according to rule that the highest
probable symbol will have a shorter length
code.
 Further Huffman encoding is performed i.e.
mapping of the code words to the
corresponding symbols will result in a
compressed data.
 The original image is reconstructed i.e.
decompression is done by using Huffman
decoding.
STEP 5: Designing of Transcoder block
The string of zeros and ones from the Huffman encoding
is applied to the transcoder block. This block converts
digital-to-digital data conversion of one encoding to
another format. Transcoder can be placed in the channel
to reduce bit rate prior to retransmission to target client
devices according to bandwidth availability of end users.
Fig. 5.5: Schematic of Transcoder block
The output of Huffman encoding is applied to decoding
block of Huffman to inverse quantiser block. Then the
output of inverse quantiser is applied from quantiser of
transcoder block to Huffman encoding block. This
transcoder block achieves good compression ratio.
STEP 6: In the receiver side the output of Huffman
encoding is applied from Huffman decoding block to
inverse quantiser.
STEP 7: CKBA decryption is performed as follows by
applying 8-bit key to the inverse quantiser.
STEP 8: Each resulting block of 8×8 blocks spatial
frequency coefficients is passed in turn to the inverse
DCT which transform them back into their spatial form
using the following expression:
P[x, y] = ∑ ∑ C(i)C(j) [ , ]cos
( )
cos
( )
5.3
Where C (i) and C (j) =
√
for i, j=0
=1 for all other values of i and j
Finally image is reconstructed by applying IDCT to the
decrypted image as shown in the flow chart fig.5.2.
Fig .5.6: CKBA decryption flow chart
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6. SIMULATION RESULTS AND ANALYSIS
Simulation Results
Example 1
Fig. 6.1 : Cameraman original image of size (256×256)
Fig. 6.2: CKBA encrypted image
Fig. 6.3 : CKBA decrypted output image
The performance of the proposed system is
evaluated for image of size 256×256 with quantization
factor 5 before transcoding and then increasing
quantization factor to 85 in the transcoder block. After
transcoding we will get the compression ratio of 18.0870
and average bit per image of 0.4423.
7. CONCLUSION
Conclusion
Several Transcoding algorithms are already
been examined for the effective utilization of bandwidth
and user satisfaction. The proposed image transcoder
gives the idea of how a high quality data like image can
be securely transmitted to different environments with
effective utilization of available bandwidth. From the
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ISBN: 378-26-138420-01

results it is clear that, for transcoding we exploit the full
efficiency of existing jpeg algorithm and gives better
compression for different quantization values. Here we
presented security in terms of CKBA.
REFERENCES
[1]. Nithin Thomas, David Red mill, David Bull,
“Secure transcoders for single layer video
data”, Signal processing: image
communication, pp, 196-207, and 2010.
[2]. Huafei Zhu, “Adaptive and Composable Multi-
media transcoders”, Proceedings of the 3rd
IEEE International Conference on Ubi-media
computing (U-media).
10.1109/UMEDIA.2010.5543914, pp, 113 –
117, 2010.
[3]. Samit Desai, Usha B, “Medical image
transcoder for telemedicine based on wireless
communication devices”, Proceedings of the
3rd IEEE International Conference on
Electronics Computer Technology (ICECT).
Vol.01, pp, 389 – 393, 2011.
[4]. John r. Smith, Rakesh Mohan, Chung-Sheng li,
“Content based transcoding of images in the
internet”. Proceedings of the IEEE
International Conference on Image processing
(ICIP98), Vol.03, pp, 7 – 11, 1998.
[5]. Richard han, Pravin Bhagwat, Richard
Lamaire, “Dynamic adaptation in an image
transcoding proxy for mobile web browsing”,
IEEE Personal communications.Vol.05, issue:
6, pp, 8 – 17, 1998.
[6]. Jui-Cheng Yen and Jiun-In Guo, “A new
chaotic key -based design for image encryption
and decryption”, Proceedings of the IEEE
International Conference on Circuits and
Systems, vol. 4, pp. 49-52, 2000.
[7]. M. Sahithi, B. MuraliKrishna, M. Jyothi, K.
Purnima, A. Jhansi Rani, N. Naga Sudha.
“Implementation of Random Number
Generator Using LFSR for High Secured
Multi-Purpose Applications”, M. Sahithi et al, /
(IJCSIT) International Journal of Computer
Science and Information Technologies, Vol. 3
(1), pp, 3287-3290,2012.
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ISBN: 378-26-138420-01

Iaetsd implementation of chaotic algorithm for secure image

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Iaetsd implementation of chaotic algorithm for secure image