Image encryption

IAENG International Journal of Computer Science, 35:1, IJCS_35_1_03
______________________________________________________________________________________

Image Encryption Using Block-Based
Transformation Algorithm

Mohammad Ali Bani Younes and Aman Jantan

Abstract—Encryption is used to securely transmit data in The variable secret key of the transformation process
open networks. Each type of data has its own features, therefore determines the seed, which is used to build the secret
different techniques should be used to protect confidential transformation table with a variable number of blocks. If the
image data from unauthorized access. Most of the available
encryption algorithms are mainly used for textual data and may key is changed, another seed will be generated, and then a
not be suitable for multimedia data such as images. In this different secret transformation table is obtained.
paper, we introduce a block-based transformation algorithm The variable secret key of the Blowfish algorithm is used
based on the combination of image transformation and a well to encrypt the transformed image. This encryption process
known encryption and decryption algorithm called Blowfish. decreases the mutual information among the encrypted image
The original image was divided into blocks, which were variables (i.e. high contrast) and thus increasing the entropy
rearranged into a transformed image using a transformation
algorithm presented here, and then the transformed image was
value. In this paper we propose a block-based transformation
encrypted using the Blowfish algorithm. The results showed algorithm in order to increase the security level of the
that the correlation between image elements was significantly encrypted images. The rest of this paper is organized as
decreased by using the proposed technique. The results also follows. Section II gives a background about the current
show that increasing the number of blocks by using smaller image encryption schemes. In Section III, the description of
block sizes resulted in a lower correlation and higher entropy. the proposed block-based transformation algorithm is
presented. Section IV and V present the experimental results
Index Terms—Image correlation, Image encryption, Image
entropy, Permutation. and discussion, while section VI presents a comparison
between the proposed algorithm and those of other systems.
Finally, section VII concludes the paper.
I. INTRODUCTION
The rapid growth of computer networks allowed large files,
II. BACKGROUND
such as digital images, to be easily transmitted over the
internet [1]. Data encryption is widely used to ensure security Encryption is the process of transforming the information
however, most of the available encryption algorithms are to insure its security. With the huge growth of computer
used for text data. Due to large data size and real time networks and the latest advances in digital technologies, a
constrains, algorithms that are good for textual data may not huge amount of digital data is being exchanged over various
be suitable for multimedia data [2]-[4]. types of networks. It is often true that a large part of this
In most of the natural images, the values of the information is either confidential or private. As a result,
neighboring pixels are strongly correlated (i.e. the value of different security techniques have been used to provide the
any given pixel can be reasonably predicted from the values required protection [8].
of its neighbors) [5]-[7]. In order to dissipate the high The security of digital images has become more and more
correlation among pixels and increase the entropy value, we important due to the rapid evolution of the Internet in the
propose a transformation algorithm that divides the image digital world today. The security of digital images has
into blocks and then shuffles their positions before it passes attracted more attention recently, and many different image
them to the Blowfish encryption algorithm. By using the encryption methods have been proposed to enhance the
correlation and entropy as a measure of security, this process security of these images [9].
results in a lower correlation and a higher entropy value when Image encryption techniques try to convert an image to
compared to using the Blowfish algorithm alone, and thus another one that is hard to understand [9]. On the other hand,
improving the security level of the encrypted images. There image decryption retrieves the original image from the
are two main keys to increase the entropy; the variable secret encrypted one. There are various image encryption systems
key of the transformation process and the variable secret key to encrypt and decrypt data, and there is no single encryption
of the Blowfish algorithm. algorithm satisfies the different image types.
Most of the algorithms specifically designed to encrypt
digital images are proposed in the mid-1990s. There are two
Manuscript received 25 Jul 2007. Mohammad Ali Moh'd Bani Younes
major groups of image encryption algorithms: (a) non-chaos
and Aman Jantan are with the Department of Computer Science, Universiti selective methods and (b) Chaos-based selective or
Sains Malaysia, and E-mail: mali.cod04@student.usm.my, non-selective methods. Most of these algorithms are
aman@cs.usm.my.

(Advance online publication: 19 February 2008)

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designed for a specific image format compressed or information can be reduced by decreasing the correlation
uncompressed, and some of them are even format compliant. among the image elements using certain transformation
There are methods that offer light encryption (degradation), techniques.
while others offer strong form of encryption. Some of the The secret key of this approach is used to determine the
algorithms are scalable and have different modes ranging seed. The seed plays a main role in building the
from degradation to strong encryption [10]. transformation table, which is then used to generate the
Mitra A et al. [11] have proposed a random combinational transformed image with different random number of block
image encryption approach with bit, pixel and block sizes. The transformation process refers to the operation of
permutations. dividing and replacing an arrangement of the original image.
Zhi-Hong Guan et al. [12] have presented a new image The image can be decomposed into blocks; each one
encryption scheme, in which shuffling the positions and contains a specific number of pixels. The blocks are
changing the grey values of image pixels are combined to transformed into new locations. For better transformation
confuse the relationship between the cipher image and the the block size should be small, because fewer pixels keep
plain image. their neighbors. In this case, the correlation will be
Sinha A. and Singh K. [13] proposed an image encryption decreased and thus it becomes difficult to predict the value
by using Fractional Fourier Transform (FRFT) and JigSaw of any given pixel from the values of its neighbors. At the
Transform (JST) in image bit planes. receiver side, the original image can be obtained by the
Shujun Li et al. [14] have pointed out that all inverse transformation of the blocks. A general block
permutation-only image ciphers were insecure against diagram of the transformation method is shown in Fig. 1.
known/chosen-plaintext attacks. In conclusion, they
suggested that secret permutations have to be combined with
other encryption techniques to design highly secured images.
Maniccam S.S. and Bourbakis N G. [10] proposed image Original
Image
and video encryption using SCAN patterns. The image
encryption is performed by SCAN-based permutation of
pixels and a substitution rule which together form an iterated
Transformation
product cipher. process Key
Ozturk I. and Sogukpinar I. [15] proposed new schemes
which add compression capability to the mirror-like image
encryption MIE and Visual Cryptography VC algorithms to
Encryption
improve these algorithms. Process
Sinha A. and Singh K. [16] proposed a technique to
encrypt an image for secure transmission using the digital
signature of the image. Digital signatures enable the recipient Ciphered
of a message to authenticate the sender of a message and Image
verify that the message is intact.
Droogenbroeck M.V. and Benedett R. [2] have proposed Fig. 1. General block diagram of the transformation
two methods for the encryption of an image; selective algorithm
encryption and multiple selective encryption.
Maniccam S.S., Nikolaos G. and Bourbakis. [17] have The transformation algorithm is presented below. It generates
presented a new methodology, which performs both lossless a transformation table that will be used to build a newly
compression and encryption of binary and gray-scale images. transformed image.
The compression and encryption schemes are based on
ALGORITHM CREATE_TRANSFORMATION_TABLE
SCAN patterns generated by the SCAN methodology.
1: Load Image
The proposed algorithm divides the image into random
2: Input key
number of blocks with predefined maximum and minimum
3: Get ImageWidth and ImageHeight
number of pixels, resulting in a stronger encryption and a
4:
decreased correlation.
4.1: LowerHorizontalNoBlocks = Int(ImageWidth /10)
4.2: LowerVerticalNoBlocks = Int(ImageHeight /10)
5: Randomize ()
III. THE PROPOSED TECHNIQUE
6:
A. Description of the Transformation Algorithm 6.1: HorizontalNoBlocks = RandomNum between
The transformation technique works as follows: the (LowerHorizontalNoBlocks and ImageWidth)
original image is divided into a random number of blocks 6.2: VerticalNoBlocks = RandomNum between
that are then shuffled within the image. The generated (or (LowerVerticalNoBlocks and ImageHeight)
transformed) image is then fed to the Blowfish encryption 7: NoBlocks = HorizontalNoBlocks * VerticalNoBlocks
algorithm. The main idea is that an image can be viewed as an 8: Seed = | Hash value (Key) |
arrangement of blocks. The intelligible information present 9: HashValue1 = |Hash value (first half of the Key)|
in an image is due to the correlation among the image HashValue2 = |Hash value (second half of the Key)|
elements in a given arrangement. This perceivable 10: Randomize using seed
11: If HashValue1 > HashValue2 Then


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SEEDALTERNATE = 1 by reducing the correlation among image elements and
Else increasing its entropy value.
SEEDALTERNATE = 2 Image measurements (correlation and entropy) will be
End If carried out on the original image and the encrypted images
12: I = 0 with and without transformation algorithm the results are
Number-of-seed-changes (N) = 1 then analyzed. The overview model of the proposed
13: While I < NoBlocks technique is shown in Fig.2.
R = RandomNum between (zero and NoBlocks -1)
If R is not selected Then
Original
Assign location R to the block I Image
I +=1
Else Transformation
If SEEDALTERNATE = 1 Then process
seed = seed + (HashValue1 Mod I) +1
SEEDALTERNATE = 2
Else Encryption Encryption
Process Key
seed = seed + (HashValue2 Mod I) + 1 Process
SEEDALTERNATE = 1
Randomize (seed) Ciphered Ciphered
Image Image
End If
Else
Number-of-seed-changes += 1 Statistical Statistical
analysis analysis
If Number-of-seed-changes > 500,000 then
For K = 0 to NoBlocks -1
If K not selected then Measuremen Comparison Measuremen
Assign location K to Block I t results Process t results
I=I+1 Proposed
End if Technique
Comparison Blowfish
Next K results
End if
End if Fig. 2. An overview diagram of the proposed technique
End While
END CREATE_TRANSFORMATION_TABLE
IV. EXPERIMENTS
Input: BMP image file, a string Key The method used to evaluate the present technique is
Output: Transformation table described in Fig. 2. The algorithm was applied on a bit
mapped (bmp) image that has the size of 300 pixels x 300
ALGORITHM PERFORM_TRANSFORMATION pixels with 256 colors. In order to evaluate the impact of the
1: For I = 0 to NoBlocks -1 number of blocks on the correlation and entropy, three
1.1: Get the new location of block I from the different cases were tested. The number of blocks and the
transformation table block sizes for each case are shown in Table I.
1.2: Set block I in its new location
END PERFORM_TRANSFORMATION
Table I Different Cases to Test the Impact of the Number of
Blocks on the Correlation and Entropy
Input: Original Image (BMP image file) and
Transformation table Case Number Number of blocks Block size
Output: Transformed Image. 1 30×30 10 pixels × 10 pixels
2 60×60 5 pixels × 5 pixels
B. Description of the combination technique 3 100×100 3 pixels × 3 pixels
The block-based transformation algorithm is based on the
Each case produces three output images; (a) a ciphered
combination of image transformation followed by
image using the Blowfish algorithm, (b) a transformed image
encryption (i.e. transformation algorithm followed by the
using the proposed algorithm, and (c) a ciphered image using
Blowfish algorithm). The transformation algorithm and the
the proposed algorithm followed by the Blowfish algorithm.
Blowfish algorithm use the original image to produce three
For the rest of this paper, we use image A, image B, image C,
output images; (a) a ciphered image using Blowfish, (b) a
and image D to denote the original image, the ciphered image
transformed image using a transformation process and (c) a
using the Blowfish algorithm, the transformed image, and the
transformed image encrypted using Blowfish.
ciphered image using the proposed algorithm followed by the
The correlation and entropy of the three images are
Blowfish algorithm respectively.
computed and compared with each other. This technique
Correlation and entropy are computed for each case
aims at enhancing the security level of the encrypted images
according to equation (1) and equation (2).


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n∑(xy) − ∑x∑y
r=
1.1

2
[
n∑(x ) − (∑x) n∑( y ) − (∑y)
2 2 2
(1)
][ 0.9 ]
Where 0.7

Correlation
r: correlation value
0.5
n: the number of pairs of data
∑xy: sum of the products of paired data 0.3
∑x: sum of x data
0.1
∑y: sum of y data
∑x2: sum of squared x data -0.1
∑y2: sum of squared y data Horizontal Vertical Diagonal Opposite
Diagonal

Entropy defined as follows [18]-[19]. A B C D
G −1
H e = − ∑ P ( k ) log 2 ( P ( k )) ( 2) (a)
k =0
1.1 5.5
Where:
He: entropy. 5
0.9
G: gray value of input image (0... 255).
P(k): is the probability of the occurrence of symbol k. 4.5

Average correlation
0.7
4

Entropy
Case 1: The image is decomposed into 10 pixels × 10 pixels 0.5
blocks. Fig.3. shows the resulted images. 3.5
0.3
3

0.1 2.5

-0.1 2
A B C D

(b)
(a) (b) Fig. 4. Correlation and entropy values of Case 1. (a)
Correlation between directions. (b) Average correlation and
entropy.
Case 2: The image is decomposed into 5 pixels × 5 pixels
blocks. Fig. 5 shows the resulted images.

(c) (d)
Fig. 3. Results of encryption by using 10 pixels × 10 pixels
blocks. (a) Original image. (b) Encrypted image using
Blowfish. (c) Transformed image. (d) Encrypted image
using transformed followed by the Blowfish algorithm.
(a) (b)
The correlation and entropy results of this case are
summarized in Table II and Fig. 4

Table II Results of Correlation and Entropy values of Case 1.

Measurement A B C D
Horizontal 0.933 0.035 0.843 0.034 (c) (d)
Vertical 0.936 0.107 0.844 0.038 Fig. 5. Results of encryption by using 5 pixels × 5 pixels
Correlation

Diagonal 0.919 0.032 0.748 0.013 Blowfish. (c) Transformed image. (d) Encrypted image
Opposite using transformed followed by the Blowfish algorithm.
0.916 0.034 0.745 0.009
Diagonal
Average 0.926 0.052 0.795 0.023 The correlation and entropy results of this case are
summarized in Table III and Fig. 6.
Entropy value 2.431 4.799 2.431 5.231


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Table III Results of Correlation and Entropy values of Case 2. Fig. 7. Results of encryption by using 3 pixels × 3 pixels
Measurement A B C D Blowfish. (c) Transformed image. (d) Encrypted image
Horizontal 0.9325 0.0346 0.7469 0.0245 using transformed followed by the Blowfish algorithm.
The results of this case are summarized in Table IV and Fig. 8.
Vertical 0.9362 0.1073 0.7497 0.0067
Correlation

Table IV Results of Correlation and Entropy values of Case 3.
Diagonal 0.9186 0.0321 0.5881 0
Opposite Measurement A B C D
0.9156 0.0337 0.5877 0.0056
Diagonal
Horizontal 0.9325 0.0346 0.6289 0.0129
Average 0.9257 0.0519 0.6681 0.0092
Entropy value 0.9362 0.1073 0.6265 0.0034
2.431 2.4305 4.799 2.4305 Vertical

Correlation
Diagonal 0.9186 0.0321 0.4145 0.0014
1.1

0.9 Opposite 0.9156 0.0337 0.4146 0.0049
Diagonal
0.7
0.9257 0.0519 0.5211 0.0056
Correlation

Average
0.5
Entropy value 2.4305 4.799 2.4305 5.5281
0.3

0.1
1.1
-0.1
Horizontal Vertical Diagonal Opposite 0.9
Diagonal 0.7
Correlation

A B C D 0.5

(a) 0.3

0.1
1 5
0.9 -0.1
Average correlation

4.5 Horizontal Vertical Diagonal Opposite
0.8
0.7 Diagonal
4
Entropy

0.6
0.5 3.5 A B C D
0.4
0.3
3 (a)
0.2
2.5
0.1 1 6
0 2 0.9 5.5
Average correlation

A B C D 0.8
5
0.7
4.5

Entropy
0.6
(b) 0.5 4
Fig. 6. Correlation and entropy values of Case 2. (a) 0.4
3.5
Correlation between directions. (b) Average correlation and 0.3
3
entropy. 0.2
0.1 2.5
Case 3: The image is decomposed into 3 pixels × 3 pixels 0 2
blocks. Fig. 7 shows the resulted images. A B C D

(b)
Fig. 8. Correlation and entropy values of Case 3. (a)
Correlations between directions. (b) Average correlation and
entropy.

(a) (b)

(c) (d)


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0.06 1.1

0.05 0.9
Percentage

0.04 0.7
0.03 0.5
0.02 0.3
0.01 0.1
0 -0.1
30 × 30 60 × 60 100 ×100 30 × 30 60 × 60 100 ×100
Number of blocks Number of blocks
B D A B C D

(a)
(a)
6
6 5
5
Percentage

4
4
3
3
2 2
1 1
0 30 × 30 60 × 60 100 ×100
30 × 30 60 × 60 1 00 ×1 00
Number of blocks
Number of blocks
A B C D
B D

(b) (b)
Fig. 9 Average correlation and entropy for image B and Fig. 10. Correlation and entropy values versus the number of
image D. (a) Correlation. (b) Entropy blocks. (a) Correlation. (b) Entropy.

As shown in Fig. 9 above, the three different cases showed VI. COMPARISON BETWEEN THE PROPOSED
that the proposed algorithm resulted in lower correlation and ALGORITHM AND OTHER ALGORITHMS
higher entropy than the Blowfish algorithm alone. The best
performance occurs for the smallest image block size. In this section, two experiments were carried out using
the image of Fig. 11. In the first experiment, the image was
divided into different blocks that are shuffled according to
the proposed algorithm and then followed by one of four
V. RESULTS AND DISCUSSION
commonly used encryption algorithms; Blowfish, Twofish,
The above cases show that using the proposed algorithm RijnDael, or RC4. These algorithms are commercially
followed by the Blowfish algorithm resulted in a lower available, so we applied them on the ciphered image that
correlation and a higher entropy compared to using the resulted from applying the proposed algorithm on the
Blowfish alone. Dividing the image into a larger number of different block sizes of the original image. The correlation
blocks made the performance even better. The results and entropy of each one was compared with applying the
showed that the correlation was reduced even further and the corresponding algorithm alone. The results of this
entropy was increased as the number of blocks is increased. experiment are shown in Table VI and Fig. 12.
Fig. 12 shows that using the proposed algorithm along with
Table V summarizes the results of the different cases, while the other algorithms resulted in a better performance
Fig. 10 shows the effect of block size on the correlation and compared to using the other algorithms alone.
entropy value. In the second experiment, commonly used algorithms in
literature were used to compare with the proposed
Table V Results of Correlation and Entropy values of image algorithm. Since those algorithms are not available for us,
(a) in Fig. 3. we compared the ciphered images generated by applying
these algorithms with the ciphered images generated by
Number applying the proposed algorithm on the original image. The
Measurement A B C D
of blocks correlation and entropy of the ciphered images were
30 × 30 0.7952 0.0234 recorded for each algorithm. The results of this application
Average
60 × 60 0.9257 0.0519 0.6681 0.0092 are shown in Table VII and plotted in Fig. 13.
correlation
100 ×100 0.5211 0.0056
30 × 30 5.2305
Entropy 60 × 60 2.4305 4.799 2.4305 5.4737
100 ×100 5.5281


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Fig. 11. Image used to compare the proposed algorithm with the commonly used ones in industry and literature

Table VI Correlation and Entropy when Appling. (a) Commercially available algorithms alone. (b) Commercially available
algorithms preceded by the proposed algorithm when applied to the image of Fig. 11

Commercially available algorithms alone
System Correlation Entropy
BLOWFISH(448) 0.0189 5.2703
TWOFISH (256) 0.0054 5.5437
RijnDael (AES256) 0.0051 5.5436
RC4(2048) 0.0038 5.5437
(a)

Commercially available algorithms preceded by the proposed algorithm
System Number of blocks Correlation Entropy
30×30 0.0063 5.4402
60×60 0.0049 5.5286
BLOWFISH(448)
100×100 0.0044 5.5407
300×300 0.0028 5.5438
30×30 0.0026 5.5437
TWOFISH (256) 60×60 0.0040 5.5439
100×100 0.0041 5.5438
300×300 0.0029 5.5438
30×30 0.0034 5.5440
60×60 0.0024 5.5439
RijnDael (AES256)
100×100 0.0049 5.5438
300×300 0.0016 5.5439
30×30 0.0024 5.5438
60×60 0.0026 5.5437
RC4(2048)
100×100 0.0034 5.5439
300×300 0.0034 5.5438
(b)

0.04 5.6
0.035 5.5
0.03
0.025 5.4
0.02 5.3
0.015
5.2
0.01
0.005 5.1
0 5
30×30
60×60
100×100
300×300
30×30
60×60
100×100
300×300
30×30
60×60
100×100
300×300
30×30
60×60
100×100
300×300

30×30
60×60
100×100
300×300
30×30
60×60
100×100
300×300
30×30
60×60
100×100
300×300
30×30
60×60
100×100
300×300

BLOWFISH TWOFISH RijnDael RC4 BLOWFISH TWOFISH RijnDael RC4

Other systems Proposed technique Other systems Proposed technique

(a) (b)
Fig. 12. Correlation and entropy values of the algorithms shown in Table VI. (a) Correlation. (b) Entropy.


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Table VII Correlation and Entropy values of commonly used Encryption Algorithms in Literature when applied to the image of
Fig. 11.
Measurement
Number Image encryption algorithm
Correlation Entropy
1 Proposed technique 30 × 30 0.0063 5.4402
4 Block Permutation 8×8 0.7977 1.9216
5 Pixel Permutation 0.7418 1.9017
6 Bit Permutation 0.0479 1.9769
7 [Block, bit, pixel] Combination 0.0812 2.0631
8 3D Jigsaw transform 0.6096 2.3735
9 Arnold cat map 0.2882 1.9208
10 Chen’s chaotic system 0.2569 2.3440
11 A naive selective encryption (3 bits encrypted) 0.8078 2.0635
12 A naive selective encryption (7 bits encrypted) 0.1037 2.0182

0.9 6
0.8
5
0.7
0.6 4
0.5
0.4 3
0.3
2
0.2
0.1 1
0.0
-0.1 0
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

(a) (b)
Fig. 13. Correlation and entropy values of the algorithms shown in Table VII. (a) Correlation. (b) Entropy.

Fig. 12 and Fig. 13 show that the proposed algorithm resulted Journal of Information and Technology. Vol. 2,
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college of the atlantic 105 eden street, bar harbor, me
04609, 2002, http://hornacek.coa.edu/

Mohammad. A. B. Younes received BSc in computer science from Yarmouk
University in Irbid Jordan, MSc from Sudan University of science and


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