International Journal of Engineering Trends and Technology (IJETT) – Volume17 Number 8–Nov2014
Enhancement of Image Security Using Random
Permutation
S.Vasu Deva Simha#1, Mr.K.Mallikarjuna*2
#
M.Tech student, Rajeev Gandhi Memorial College of Engineering and Technology (Autonomous),
Nandyal (Kurnool Dist), A.P,India
*
Associate Professor, Rajeev Gandhi Memorial College of Engineering and Technology (Autonomous),
Nandyal (Kurnool Dist), A.P,India
Abstract— in recent days transmitting digital media having large
size data through the internet became simple task but providing
security and security became big issue these days. Using
pseudorandom permutation, image encryption is obtained.
Confidentiality and access control is done by encryption. Image
encryption has to be conducted prior to image compression. In
this paper how to design a pair of image encryption and
compression algorithms such that compressing encrypted images
can still be efficiently performed .This paper introduced a highly
efficient image encryption-then compression (ETC) system. The
proposed image encryption scheme operated in the prediction
error domain is able to provide a reasonably high level of
security. More notably, the proposed compression approach
applied to encrypted images is only slightly worse, unencrypted
images as inputs.
Keywords— encryption-then compression, Arithmetic coding,
and bilateral filter.
I. INTRODUCTION
The transmission and the transfer of images, in free spaces
and on lines, are actually still not well protected. The standard
techniques of encoding are not appropriate for the particular
case of the images. The best would be to be able to apply
asymmetrical systems of encoding so as not to have a key to
transfer. Because of the knowledge of the public key, the
asymmetrical systems are very ex-pensive in calculation, and
thus a protected transfer of images cannot be envisaged. The
symmetrical algorithms impose the transfer of the secret key.
The traditional methods of encoding images impose the
transfer of the secret key by another channel or another means
of Communication. Consider an application scenario in which
a content owner Alice wants to securely and efficiently
transmit an image I to a recipient Bob, via an untrusted
channel provider Charlie. Conventionally, this could be done
as follows. Alice first compresses I into B, and then encrypts
B into Ie using an encryption function EK (·), where K
denotes the secret key as illustrated in Fig.1 (a).
The encrypted data I is then assed to Charlie, who simply
forwards it to Bob upon receiving I Bob sequentially performs
decryption and decompression to get a reconstructed image I.
Even though the above Compression-then-Encryption (CTE)
paradigm meets the requirements in many secure transmission
scenarios, the order of applying the compression and
encryption needs to be reversed in some other situations.
ISSN: 2231-5381
Fig.1 (a). Traditional Compression-then-Encryption (CTE)
system
Fig.1 (b). Encryption-then-Compression (ETC) system
As the content owner, Alice is always interested in protecting
the privacy of the image data through encryption.
Nevertheless, Alice has no incentive to compress her data, and
hence, will not use her limited computational resources to run
a compression algorithm before encrypting the data. This is
especially true when Alice uses a resource-deprived mobile
device. In contrast, the channel provider Charlie has an
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International Journal of Engineering Trends and Technology (IJETT) – Volume17 Number 8–Nov2014
overriding interest in compressing all the network traffic so as
to maximize the network utilization. It is therefore much
desired if the compression task can be delegated by Charlie,
who typically has abundant computational resources. A big
challenge within such Encryption-then-Compression (ETC)
framework is that compression has to be conducted in the
encrypted domain, as Charlie does not access to the secret key
K. This type of ETC system is demonstrated in Fig.1 (b).
II. PROPOSED WORK
The proposed system consists of the three key components
namely, image encryption conducted by Alice, image
compression conducted by Charlie, and the sequential
decryption and decompression conducted by Bob.
A. Image Encryption via Prediction Error Clustering and
Random Permutation
Encryption refers to set of algorithms, which are used to
convert the plain text to code or the unreadable form of text,
and provides privacy. To decrypt the text the receiver uses the
“Secret key” for the encrypted text. The feasibility of lossless
compression of encrypted images has been recently
demonstrated by relying on the analogy with source coding
with side information at the decoder. Upon receiving the
compressed and encrypted bit stream B, Bob aims to recover
the original image I. A multimedia technology for information
hiding which provides the authentication and copyright
protection.
Step 3: Reshape the prediction errors in each Ck into a 2-D
block having four columns and ⌈|Ck|/4⌉ rows, where |Ck|
denotes the number of prediction errors in Ck.
Step 4: Perform two key-driven cyclical shift operations to
each resulting prediction error block, and read out the data in
raster-scan order to obtain the permuted cluster ˜C k.
Step 5: The assembler concatenates all the permuted
clusters Ck, for 0 ≤ k ≤ L−1, and generates the final encrypted
image.
in which each prediction error is represented by 8 bits. As
the number of prediction errors equals that of the pixels, the
file size before and after the encryption preserves.
Step6: Pass Ie to Charlie, together with the
length of
each cluster |˜C k|, for 0 ≤ k ≤ L − 2. The values of |˜C k|
enable Charlie to divide Ie into L clusters correctly. In
comparison with the file size of the encrypted data, the
overhead induced by sending the length |˜C k| is negligible.
B. Compression of Encrypted Image via Arithmetic
Coding
Fig. 3. Schematic diagram of compressing the encrypted
data.
Fig: 2 Schematic diagram of image encryption.
The algorithmic procedure of performing the image
encryption is then given as follows:
Step 1: Compute all the mapped prediction errors ˜ei,j of
the whole image I.
Step 2: Divide all the prediction errors into L clusters Ck,
for 0 ≤ k ≤ L − 1, where k is determined by (5), and each Ck is
formed by concatenating the mapped prediction errors in a
raster-scan order.
ISSN: 2231-5381
Arithmetic coding
Arithmetic coding is especially suitable for small alphabet
(binary sources) with highly skewed probabilities. Arithmetic
coding is very popular in the image and video compression
applications.
Consider a half open interval [low, high).Initially, interval is
set as [0, 1) and range=high -low = 1-0 = 1.Interval is divided
into cumulative probabilities of n symbols. For this example,
n=3; p (a) =1/2, p (b) =1/4 and p(c) =1/4.We propose an
image encryption scheme operated over the prediction and
permutation based image encryption method and the
efficiency of compressing the encrypted data.
The compression of the encrypted file Ie needs to be
performed in the encrypted domain, as Charlie does not have
access to the secret key K. In Fig. 3, we show the diagram of
compression of Ie. Assisted by the side information|˜C k|, for
0 ≤ k ≤ L − 2, a de-assembler can be utilized to parse Ie into L
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International Journal of Engineering Trends and Technology (IJETT) – Volume17 Number 8–Nov2014
segments ˜C 0, ˜C 1,・・, ˜C L−1 in the exactly same way as
that done at the encryption stage. An AC is then employed to
encode each prediction error sequence ˜C k into a binary bit
stream Bk. Note that the generation of all Bk can be carried
out in a parallel manner to improve the throughput. An
assembler concatenates all Bk to produce the final compressed
and encrypted bit stream B, namely,
B = B0B1 ・ ・ ・BL−1
prediction error ei, j, which implies ˆIi,j = Ii,j , i.e., error-free
decoding is achieved.
C. Sequential Decompression and Decryption
Fig. 6 Sample Results of (a) original Lena; (b) encrypted
Lena; (c) original Baboon; (d) encrypted Baboon.
III. PERFORMANCE MEASURES
To study the relative performance of cluster based
segmentation methods the following quality measures are
calculated.
Fig. 5. Schematic diagram of sequential
decompression.
decryption and
After receiving the compressed and encrypted bit stream B,
Bob aims to recover the original image I. The schematic
diagram demonstrating the procedure of sequential decryption
and decompression is provided in Fig. 4. According to the side
information |Bk|, Bob divides B into L segments Bk, for 0 ≤ k
≤ L−1, each of which is associated with a cluster of prediction
errors. For each Bk, an adaptive arithmetic decoding can be
applied to obtain the corresponding permuted prediction error
sequence C k. As Bob knows the secret key K, the
corresponding de-permutation operation can be employed to
get back the original Ck. With all the Ck, the decoding of the
pixel values can be performed in a raster-scan order. For each
location (i, j), the associated error energy estimator i,j and the
predicted value ˜Ii,j can be calculated from the causal
surroundings that have already been decoded. Given _i,j , the
corresponding cluster index k can be determined by using (5).
The first ‘unused’ prediction error in the kth cluster is selected
as ˜ei,j , which will be used to derive ei,j according to ˜Ii,j and
the mapping rule described in Section II-A. Afterwards, a
‘used’ flag will be attached to the processed prediction error.
The reconstructed pixel value can then be computed by ˆIi,j =
˜Ii,j + ei,j (9) As the predicted value ˜Ii,j and the error energy
estimator _i,j are both based on the causal surroundings, the
decoder can get the exactly same prediction ˜Ii,j . In addition,
in the case of lossless compression, no distortion occurs on the
ISSN: 2231-5381
A. Peak Signal to Noise Ratio (PSNR)
PSNR is most commonly used to measure the quality of
for image compression. The signal in this case is the original
data, and the noise is the error introduced by compression.
When comparing compression, PSNR is a human perception
of reconstruction quality. The PSNR is calculated based on
color texture based image segmentation. The PSNR range
between [0, 1), the higher is better.
PSNR = 20 * log10 (255 / sqrt (MSE))
B. Mean Square Error (MSE)
Mean Square Error (MSE) is calculated pixel-by pixel by
adding up the squared difference of all the pixels and dividing
by the total pixel count. MSE of the segmented image can be
calculated by using the Equation. The MSE range between [0,
1], the lower is better.
MSE =
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International Journal of Engineering Trends and Technology (IJETT) – Volume17 Number 8–Nov2014
IV. RESULTS
(a) Original Image
(b)Encrypted Image
Fig. 6 Result1
V. CONCLUSIONS
In this paper, we have designed an efficient image Encryptionthen Compression (ETC) system. Within the proposed work,
the image encryption has been achieved via prediction error
clustering and random permutation. Efficient compression of
the encrypted data has then been done by arithmetic coding
approach. By Arithematic Coding based, Coding can’t be
cracked. Both theoretical and experimental results have shown
that reasonably high level of security has been retained. The
coding efficiency of our proposed compression method on
encrypted images is very close to that of the image codecs,
which receive original, unencrypted images as inputs. The
Compressed image is measured in terms of Quality measures
like MSE and PSNR.
REFERENCES
[1]
[2]
(a)Encrypted Image (b) Reconstructed Image
Fig. 7 Result2
Table.1 Quality Measures of Image
Image
[3]
[4]
Lena
[5]
Quality Measures
PSNR
MSE
[6]
Original Image
23.86 dB
267.12
Encrypted Image
26.55 dB
144.00
J. Zhou, X. Liu, and O. C. Au,“ On the design of an efficient
encryption then-compression system,”[2] T. Bianchi, A. Piva, and M.
Barni, “On the implementation of the discrete Fourier transform in the
encrypted domain,” IEEE Trans. on Inf. Forensics Security, vol. 4, no.
1, pp. 86-97, March 2009.
M. Johnson, P. Ishwar, V. M. Prabhakaran, D. Schonberg, and
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D. Schonberg, S. C. Draper, and K. Ramchandran, “On compression
of encrypted images,” in Proc. IEEE Int. Conf. Image Process., pp.
269 -272, 2006.
W. Liu, W.J. Zeng, L. Dong, and Q.M. Yao, “Efficient compression of
encrypted grayscale images,” IEEE Trans. on Image. Process. vol. 19,
no. 4, pp. 1097–1102, April 2010.
A. Kumar and A. Makur ,“Lossy compression of encrypted image by
compressing sensing technique,” in Proc. IEEE Region 10 Conf., pp.16, 2009.
X. Wu and N. Memon, “Context-based, adaptive, lossless image
codec,”IEEE Trans. On Commun., vol. 45, no. 4, pp. 437–444, April
1997.
BIODATA
Author
Reconstructed Image
32.11 dB
39.98
S.Vasu Deva Simha presently pursuing
M.Tech DSCE (Digital Systems and
Computer Electronics) Electronics and
Communication (ECE) Department,
Rajeev Gandhi Memorial College of
Engineering
and
Technology
(Autonomous) Nandyal (Kurnool Dist)
A.P, India. He completed his B.Tech in
ECE (Electronics and Communication)
Annamacharya Institute of Science and Technology
(Autonomous) Rajampet (Kadapa Dist) A.P, India.
Co-Author
Mr.K.Mallikarjuna working as Associate Professor in ECE
Department , Rajeev Gandhi Memorial College of
Engineering and Technology(Autonomous) Nandyal (Kurnool
Dist), A.P,India.
ISSN: 2231-5381
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