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Mohammad Ali Bani Younes，Aman Jantan. Image Encryption Using Block-Based Transformation Algorithm. IAENG International Journal of Computer Science, 2008.2 ### 图像加密技术：基于区块转换算法的图像加密方法 #### 概述随着计算机网络技术的迅速发展，大量的数字图像文件得以通过互联网便捷地传输。为了确保这些数据的安全性，加密技术被广泛应用于数据传输过程中。然而，现有的大多数加密算法主要针对文本数据设计，并不一定适用于多媒体数据如图像。这是因为多媒体数据具有较大的数据量和实时性限制，因此适用于文本数据的算法可能并不适合图像等多媒体数据。鉴于此，本文介绍了一种基于区块转换的加密算法，该算法结合了图像变换技术和一种著名的加密解密算法——Blowfish。 #### 区块转换算法该算法的主要思想是将原始图像划分为多个区块，然后利用特定的转换算法重新排列这些区块的位置，形成一个新的转换后的图像。随后，这个转换后的图像会被使用Blowfish算法进行加密处理。通过这种区块转换的方式，可以显著降低图像元素之间的相关性，从而提高加密后的图像安全性。 #### 影像相关性和熵在自然图像中，相邻像素值之间通常存在很强的相关性，即某个像素的值可以从其邻近像素的值中得到合理的预测。为了打破这种高相关性并增加熵（即随机性），文中提出了一种转换算法，该算法首先将图像分割成多个区块，然后对这些区块的位置进行打乱处理后送入Blowfish加密算法。通过使用相关性和熵作为衡量安全性的指标，这一过程与仅使用Blowfish算法相比，能够实现更低的相关性和更高的熵值，进而提高了加密图像的安全级别。 #### 技术细节 - **区块划分**：原始图像被划分为多个固定大小的区块。 - **区块重排**：利用特定的转换算法重新排列这些区块的位置，以降低相邻像素之间的相关性。 - **Blowfish加密**：将经过区块重排的图像送入Blowfish加密算法进行加密处理。 #### 安全性增强措施 - **可变秘密密钥**：在区块转换过程中使用可变的秘密密钥，这是提高熵的两个关键因素之一。 - **区块大小**：实验结果表明，通过使用更小的区块尺寸来增加区块数量，可以进一步降低相关性并提高熵值。 #### 实验结果 - **相关性减少**：实验结果显示，采用该区块转换算法后的图像，相邻像素间的相关性显著下降。 - **熵值增加**：通过区块转换和Blowfish加密处理后，图像的熵值明显增加。 - **安全性提升**：综合来看，该算法有效地提高了加密图像的安全性。 #### 结论基于区块转换的图像加密算法能够有效解决图像加密领域中关于降低像素相关性和增加熵的问题，从而提高了加密图像的安全级别。通过将图像分割为多个区块并重新排列其位置，再结合Blowfish加密算法，可以实现对图像数据的有效保护。这种方法对于需要在网络上传输大量图像数据的应用场景尤为适用，有助于保障数据的安全性。

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Abstract—Encryption is used to securely transmit data in

open networks. Each type of data has its own features, therefore

different techniques should be used to protect confidential

image data from unauthorized access. Most of the available

encryption algorithms are mainly used for textual data and may

not be suitable for multimedia data such as images. In this

paper, we introduce a block-based transformation algorithm

based on the combination of image transformation and a well

known encryption and decryption algorithm called Blowfish.

The original image was divided into blocks, which were

rearranged into a transformed image using a transformation

algorithm presented here, and then the transformed image was

encrypted using the Blowfish algorithm. The results showed

that the correlation between image elements was significantly

decreased by using the proposed technique. The results also

show that increasing the number of blocks by using smaller

block sizes resulted in a lower correlation and higher entropy.

Index Terms—Image correlation, Image encryption, Image

entropy, Permutation.

I. INTRODUCTION

The rapid growth of computer networks allowed large files,

such as digital images, to be easily transmitted over the

internet [1]. Data encryption is widely used to ensure security

however, most of the available encryption algorithms are

used for text data. Due to large data size and real time

constrains, algorithms that are good for textual data may not

be suitable for multimedia data [2]-[4].

In most of the natural images, the values of the

neighboring pixels are strongly correlated (i.e. the value of

any given pixel can be reasonably predicted from the values

of its neighbors) [5]-[7]. In order to dissipate the high

correlation among pixels and increase the entropy value, we

propose a transformation algorithm that divides the image

into blocks and then shuffles their positions before it passes

them to the Blowfish encryption algorithm. By using the

correlation and entropy as a measure of security, this process

results in a lower correlation and a higher entropy value when

compared to using the Blowfish algorithm alone, and thus

improving the security level of the encrypted images. There

are two main keys to increase the entropy; the variable secret

key of the transformation process and the variable secret key

of the Blowfish algorithm.

Manuscript received 25 Jul 2007. Mohammad Ali Moh'd Bani Younes

and Aman Jantan are with the Department of Computer Science, Universiti

Sains Malaysia, and E-mail: mali.cod04@student.usm.my,

aman@cs.usm.my.

The variable secret key of the transformation process

determines the seed, which is used to build the secret

transformation table with a variable number of blocks. If the

key is changed, another seed will be generated, and then a

different secret transformation table is obtained.

The variable secret key of the Blowfish algorithm is used

to encrypt the transformed image. This encryption process

decreases the mutual information among the encrypted image

variables (i.e. high contrast) and thus increasing the entropy

value. In this paper we propose a block-based transformation

algorithm in order to increase the security level of the

encrypted images. The rest of this paper is organized as

follows. Section II gives a background about the current

image encryption schemes. In Section III, the description of

the proposed block-based transformation algorithm is

presented. Section IV and V present the experimental results

and discussion, while section VI presents a comparison

between the proposed algorithm and those of other systems.

Finally, section VII concludes the paper.

II. BACKGROUND

Encryption is the process of transforming the information

to insure its security. With the huge growth of computer

networks and the latest advances in digital technologies, a

huge amount of digital data is being exchanged over various

types of networks. It is often true that a large part of this

information is either confidential or private. As a result,

different security techniques have been used to provide the

required protection [8].

The security of digital images has become more and more

important due to the rapid evolution of the Internet in the

digital world today. The security of digital images has

attracted more attention recently, and many different image

encryption methods have been proposed to enhance the

security of these images [9].

Image encryption techniques try to convert an image to

another one that is hard to understand [9]. On the other hand,

image decryption retrieves the original image from the

encrypted one. There are various image encryption systems

to encrypt and decrypt data, and there is no single encryption

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

major groups of image encryption algorithms: (a) non-chaos

selective methods and (b) Chaos-based selective or

non-selective methods. Most of these algorithms are

Image Encryption Using Block-Based

Transformation Algorithm

Mohammad Ali Bani Younes and Aman Jantan

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

______________________________________________________________________________________

(Advance online publication: 19 February 2008)

designed for a specific image format compressed or

uncompressed, and some of them are even format compliant.

There are methods that offer light encryption (degradation),

while others offer strong form of encryption. Some of the

algorithms are scalable and have different modes ranging

from degradation to strong encryption [10].

Mitra A et al. [11] have proposed a random combinational

image encryption approach with bit, pixel and block

permutations.

Zhi-Hong Guan et al. [12] have presented a new image

encryption scheme, in which shuffling the positions and

changing the grey values of image pixels are combined to

confuse the relationship between the cipher image and the

plain image.

Sinha A. and Singh K. [13] proposed an image encryption

by using Fractional Fourier Transform (FRFT) and JigSaw

Transform (JST) in image bit planes.

Shujun Li et al. [14] have pointed out that all

permutation-only image ciphers were insecure against

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

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

product cipher.

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

improve these algorithms.

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

of a message to authenticate the sender of a message and

verify that the message is intact.

Droogenbroeck M.V. and Benedett R. [2] have proposed

two methods for the encryption of an image; selective

encryption and multiple selective encryption.

Maniccam S.S., Nikolaos G. and Bourbakis. [17] have

presented a new methodology, which performs both lossless

compression and encryption of binary and gray-scale images.

The compression and encryption schemes are based on

SCAN patterns generated by the SCAN methodology.

The proposed algorithm divides the image into random

number of blocks with predefined maximum and minimum

number of pixels, resulting in a stronger encryption and a

decreased correlation.

III. THE

PROPOSED TECHNIQUE

A. Description of the Transformation Algorithm

The transformation technique works as follows: the

original image is divided into a random number of blocks

that are then shuffled within the image. The generated (or

transformed) image is then fed to the Blowfish encryption

algorithm. The main idea is that an image can be viewed as an

arrangement of blocks. The intelligible information present

in an image is due to the correlation among the image

elements in a given arrangement. This perceivable

information can be reduced by decreasing the correlation

among the image elements using certain transformation

techniques.

The secret key of this approach is used to determine the

seed. The seed plays a main role in building the

transformation table, which is then used to generate the

transformed image with different random number of block

sizes. The transformation process refers to the operation of

dividing and replacing an arrangement of the original image.

The image can be decomposed into blocks; each one

contains a specific number of pixels. The blocks are

transformed into new locations. For better transformation

the block size should be small, because fewer pixels keep

their neighbors. In this case, the correlation will be

decreased and thus it becomes difficult to predict the value

of any given pixel from the values of its neighbors. At the

receiver side, the original image can be obtained by the

inverse transformation of the blocks. A general block

diagram of the transformation method is shown in Fig. 1.

Fig. 1. General block diagram of the transformation

algorithm

The transformation algorithm is presented below. It generates

a transformation table that will be used to build a newly

transformed image.

ALGORITHM CREATE_TRANSFORMATION_TABLE

1: Load Image

2: Input key

3: Get ImageWidth and ImageHeight

4.1: LowerHorizontalNoBlocks = Int(ImageWidth /10)

4.2: LowerVerticalNoBlocks = Int(ImageHeight /10)

5: Randomize ()

6.1: HorizontalNoBlocks = RandomNum between

(LowerHorizontalNoBlocks and ImageWidth)

6.2: VerticalNoBlocks = RandomNum between

(LowerVerticalNoBlocks and ImageHeight)

7: NoBlocks = HorizontalNoBlocks * VerticalNoBlocks

8: Seed = | Hash value (Key) |

9: HashValue1 = |Hash value (first half of the Key)|

HashValue2 = |Hash value (second half of the Key)|

10: Randomize using seed

11: If HashValue1 > HashValue2 Then

Key

Ciphered

Image

Transformation

process

Encryption

Process

Original

Image

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

______________________________________________________________________________________

(Advance online publication: 19 February 2008)

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