(Sample) image encryption

Running Head: Image Encryption 1
Image Encryption
Name of Student
Name of Professor
Date

Image Encryption 2
Image Encryption Using Chaotic Based Artificial Neural Network
Introduction
In this innovative world, the internet is utilized to transmit valuable and important data of
large volumes. Since the internet also has many points of attack, therefore, it is essential to protect
the valuable information or data from unauthorized access. For this purpose, there are many
techniques to protect the data. Image encryption is one of the valuable techniques to protect
information or data. The definition looks to be very precise and simple; encryption means the
change of bits or data of any specified source in a definite pattern that is known only to the receiver
and sender. Image encryption is considered as the process of encoding a secret image in such a
manner that unknown or unauthorized users cannot approach it. It will be done by the aid of an
encryption algorithm. In addition to this, image encryption is determined as an effective method
to communicate information that is confidential for which uncountable measures are extracted.
Nowadays, it also attracts the researchers to utilize the images in every mode of digital
communication, and the utilization of this process is increasing day by day with the passage of
time (Aarthie & Amirtharajan, 2014).
Encryption commonly deals with the conversion of information or data in any other form
from its original form, which hides the information or data present in it. The original data is known
as the plain data and whereas the data after conversion is known as cipher data. Encryption is the
art to convert the information or data in the coded form, and this encoded data can be decoded by
the envisioned receiver only who knows about the decryption or decoding of the cipher or encoded
data. The safety of image data is essential from unauthorized access. Encryption is basically
working to enhance the security of the data. Image encryption is a secure process than any other
kind of cryptanalysis (V.V.Divya, S.K.Sudha, & V.R.Resmy, 2012).

Image Encryption 3
Image encryption works on the innovative idea of choosing random or consecutive pixel
bits of an image and collaboratively modified and operated by the logic, thereby important to a
comprehensive set of new pixels, that is distinctive from the original bits. So, giving growth to a
new manner of the transfer of information. Furthermore, other problems can be summarized to the
malevolent attacker, by integrating the Cipher Block Chaining method due to which the plain text
is intensely entrenched in the encrypted image. It makes the transfer of data very reliable and as
well as secure. In addition to this, the extra accumulation of a key in the process makes the image
locked from any sort of external agency (Aarthie & Amirtharajan, 2014).
Chaotic cryptography is examined as an application of the chaos theory to the practice of
cryptography, and it deals with the techniques or studies that are utilized to transmit data and
information securely and privately in the presence of adversary or third-party. There are also some
cases in which cryptography algorithms based on the chaos theory is proved to be insecure. Image
encryption is differing from the text encryption just because of some intrinsic characteristics of
images that include high redundancy, durable connections among pixels, bulk capacities of data,
etc. The chaos theory has been utilized in the process of cryptography just because of its intrinsic
features. These features of chaos comprise of sensitivity to control parameters and initial condition,
random like mixing property and behavior (N.F.Elabady, H.M.Abdalkader, M.I.Moussa, &
S.F.Sabbeh).
The algorithms that are used for text encryption are not suitable or reliable for image
encryption. Furthermore, just because of the intrinsic characteristics of visual data, as mentioned
above, can not resist differential, statistical, and other type of attacks and simply fail. Therefore,
to meet such issues or problems, a variety of image encryption algorithms have been announced.
The security of the image can be ensured only if the algorithm for image encryption can meet or

Image Encryption 4
overcome the above-mentioned issues. A variety of evaluation criteria, including the correlation
between adjacent pixels, information entropy, unified average changing intensity, and the number
of pixels change rate must be considered in the image encryption. In addition to this, the algorithm
for image encryption must be rapid in real-time applications (Alireza, Rostami, & Ghavami, 2019).
A large number of deterministic, random, and uncorrelated signals can be created by
varying initial values to the system. One dimensional chaotic mapping is considered as appropriate
for real-time image encryption. The schemes of image encryption have been studied only to ensure
the secure and safe transmission of images by means of communication networks or the internet.
Many schemes of encryption grounded on the classical algorithm have been planned to meet this
challenge. In the late 20th century, the first chaos-based cryptosystem was proposed, in which the
logistic map was used. The chaotic map provides a new direction to creates the schemes of image
encryption, which have some familiar features with the old or conventional ones (Boriga,
Dascalescu, & Diaconu, 2014).
Artificial neural networks are examined as the main tool in machine learning. The name
“neural” suggests that these are the systems inspired by the brain that is planned to repeat the way
that humans learn. The neural networks comprise two layers, i.e., input layer and output layer. In
addition to this, there is also a hidden layer comprising of units which convert the input into
something that can be utilized by the outer layer. They are an exceptional tool for finding shapes
that are so much complicated or abundant to extract for a human programmer and learn the
machine to distinguish. Neural networks are also called perceptrons, and it becomes a significant
part of artificial intelligence in the last few decades. This all is happened due to the technique
named “backpropagation,” that permits networks to settle their hidden layers of neurons in
conditions where the result does not meet what the originator is in suspense for (Dormehl, 2019).

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Figure 1: Artificial Neural Networks
There are various sorts of the neural network, whereas, each of which has its own precise
stages of complexity. From all types, the most prominent kind of neural network is said to be a
feedforward neural network. The information or data travels from input to output only in one
direction in a feedforward neural network. In addition to this, a recurrent neural network is
considered as more extensively used sort of network. The information or data transfers from input
to output in multiple directions in a recurrent neural network. Such neural networks hold greater
abilities of learning and as well as are extensively employed for much more complicated tasks like
that of language recognition and learning handwriting. Furthermore, Hopfield networks,
Boltzmann machine networks, and convolutional neural networks are some other kinds of neural
networks used to transfer the data (Dormehl, 2019).
An artificial neuron is explained as a mathematical function considered as the model of a
biological neuron, a neural network. In an artificial neural network, artificial neurons are
considered as a basic unit. The artificial neurons obtain one or more than one inputs and sum all
of these inputs to generate an output. The information or data transfers in two different ways by
means of a neural network. When the model is being trained (learning) or operating after being
trained, outlines of data or information are being fed into the network from the dataset by means

Image Encryption 6
of input neurons, that activate the layers of hidden neurons and at the end, these hidden neurons
work out at the output neurons. Such a network is known as a feedforward network. Every neuron
takes the input from its left neurons and whereas, the inputs are multiplied by the masses of
networks they foldaway along (Dacombe, 2017).
Figure 2
If one of the outputs of hidden nodes is examined in figure 1, figure 2 will describe it
precisely. A specified node receipt the weighted sum of all of its inputs, and passes it by means of
the non-linear activation function. This is examined as the output of the node, that in return, grows
into the input of the other node in the further layer. Hence, the signals flow from the left side to
the right side, whereas the final output is considered by executing this process for all of the nodes.
Training such a profound neural network means the erudition of the weights linked with all the
edges (Dertat, 2017).
Each node in one layer of artificial neural networks is linked with the other node in the next
further layer. The network becomes deeper by means of increasing the number of hidden layers.
An artificial neural network is utilized as a random function approximation tool. Such types of
tools assist in guessing the most ideal and cost-effective methods for incoming at solutions while
explaining computing distributions and functions. Artificial neural networks take the samples of

Image Encryption 7
the data rather than complete sets of data to reach the solution that saves both money and time.
Furthermore, Artificial neural networks are considered simple and reliable models to upgrade
existing technologies of data analysis (techopedia, 2019).
Literature Review
In 1999, Jong-Wook, Choon-Sik, and their team came up with research on the XOR
operation for image encryption. They explained that these new techniques could be helpful for
proper image encryption. XOR is the abbreviation of exclusive-OR, which is the security system
to provide the secure entrance of the targeted person. The technique of this proposed system is
very simple as it uses the bit planes to divide the image pixel. It divides the gray-level image into
several bit planes so that to ensure the proper transmission of information (Han, Park, Ryu, & Kim,
1999). The use of XOR has very remarkable achievements as it gives the proper well encryption
structure. Key bitstream techniques use during the process as it provides the secure bit planes for
image encryption. Moreover, it divides the targeted image into the eight well-known bit planes
with proper process, and it is used to display on the liquid crystal devices. The purpose of
displaying the information on the LCD to ensure the proper working process is happening.
Similarly, the key data then represent on the LCD device to verify the eight planes easily.
In addition, the XOR operations start during the division of image encryption into the eight-bit
planes by using the polarized encoding method. After the completion of this system, which is
detected by the CCD camera, it converts into the encrypted gray-image, which can be used for the
double chapter abstraction. This technique of image encryption has several remarkable results,
which gives the surety of proper and secure transmission of image information (Han, Park, Ryu,
& Kim, 1999). Numerous researches and experiments on the XOR operation for image encryption

Image Encryption 8
have given proof that this can be a beneficial and useful system for the proper and secure image
transmission.
In 2004, Guanrong Chen and Yaobin Mao came up with research on the image encryption
by using the 3D Chaotic maps. They explained that this system is totally different from the
traditional procedures and techniques, which requires more high severance and qualities. The
addition of some essential edifices and intrinsic abilities such as the addition of data capacity
enhances the worth of 3D chaotic cat maps. This system provides unique techniques to complete
the encryption of image fast with the high-security surety during the process. Reduction in
problems and an increase in process time enhances the worth of the proposed structure.
The researcher of the proposed paper used the two-dimensional chaotic system to update
into the 3D dimensional to provide a more efficient and generalized system. The 3D chaotic system
changes the position of image pixel so that no one can understand the sequence of image pixel
distribution. In addition, by changing the position of image pixel (Chen, Mao, & Chui, 2004), it
complicates the relation between two image qualities such as basic image, and the second is
cryptograph image, which increases the security of the process. Moreover, it diminishes numerous
spells, such as arithmetic and disparity, which can change the image information completely.
The purpose of upgrading the chaotic cat maps is to deliver the more effective and locked
system aimed at image broadcast. Moreover, the development and inventions of numerous
methods of image encryption have a single purpose of providing a more secure, legal way for the
transmission of information. Likewise, the threats of data detrimental and damaging causes the
invention of various techniques, so that a suitable system can prevent the threats of image leaking
information. The main objective of 3D disordered charts is to hobble the duplicate quality from
the right position just to puzzle the sequence of information because it produces difficulties in

Image Encryption 9
understanding the period of information (Chen, Mao, & Chui, 2004). Various researches and
experiments on 3D image encryption have given proof that this can be more beneficial and helpful
for the proper image encryption process.
In 2006, Pareek and Vinod Patidar came up with research on image encryption. They
explained that encryption is the process through which information transmits in a very secure way.
The chaotic logistics map is the structure that is used to convey the image data in code wording.
For the last many years, the chaotic map system has developed many new innovative techniques
for the encryption of image information. They explored that the development of new innovative
techniques in this system has increased the security level of image transmission. The purpose of
image encryption is to transmit the information in a very secure way so that hackers or viruses
couldn’t interrupt in the broadcast. Sometimes, one organization transmits some secret data of
incoming events to the other organization. They approach the encryption system for satisfactory
communication. In a chaotic map system (Pareek, Patidar, & Sud, 2006), a source of peripheral
80-bit data and two logistics maps are used for the completion.
The use of peripheral bits is to provide the secret outdoor key to the chaotic maps. These
bits help the chaotic maps for the fortunate transmission as it provides a secure path for the
encryption. Similarly, several operations are required for the proper pixel of an image because
when pixels of image disturb, it will create the problem to clarify the information properly. So,
several dissimilar categories of procedures remain castoff in the direction of scramble the image
evidence to the required destination. In addition, a secret key involvement in the chaotic structure
enhances the security level of the system. It makes the structure more robust and healthier in which
the key secret is changed after encrypting the image block. Various researches and experiments on

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the chaotic logistics maps have reported that this structure has remarkable service for the secure
transmission of image information.
In 2008, Bani and Jantan came up with research on the image encryption system. They
explained that every new system is generating for the proper transmission of image consumers. It
considers the biggest challenge for companies to deliver the image to the right path. For this
purpose, numerous companies and organizations spend a lot of money on image encryption. For
example, techniques are available for security, such as encryption, watermarking, and
steganography.
The researcher of the respective paper used the encryption technique for secure
transmission. This encryption process shows the basic three stages for the completion of the
procedure. In the start, the pixel of image changes by using the XOR system, which encrypts the
image completely. In steganography, the pixels of the image becomes much low so that no one
can judge the original pixel of the image, which is done by the system of Least Significant Bits
(LSBs), in result a stego image from the steganography process. In the third step, the watermark
process is used on the encrypting image to ensure the right ownership of the image. The watermark
procedure is done by a time domain as well as a frequency domain. Two structures give a
remarkable result for image encryption (Bani & Jantan, 2008). It gives the surety of the appropriate
broadcast of information during the process.
Various alphanumeric amenities are available in the market, which can be used for image
encryption. The question is, which is more applicable and efficient for a company. The data of
images and pictures are increasing on the internet fast, which needs a suitable decision for security.
The world is moving towards innovation; everything is getting innovation in its process. In
addition, the world prefers image information than traditional text, which also saves the time of

Image Encryption 11
the user. The image information demands a secure platform so that no one can damage the right
information (Bani & Jantan, 2008). A block-based system for image encryption is a unique
technique that uses three remarkable steps for proper encryption.
In 2009, Jun Peng, Du Zhang, and their team explained that utilizing chaotic features of a
dynamic system is an auspicious way to design or create cryptosystems that play a vital role in
engineering applications like data or information security and assurance. But, algorithms for
encryption based on one-dimensional chaotic map tackle a latent danger of a keystream being
reconstructed by the technique or method of return map or method of neural network. They also
proposed a new algorithm for image encryption that works as a hyper-chaotic cellular neural
network. In addition to this, their team also make the security analysis of planned algorithm to
validate its security. These analyses include sensitivity analysis, keyspace analysis, correlation
coefficients analysis, and information entropy analysis. The outcome shows that the algorithm for
planned encryption has necessary security features and can be organized in a comprehensive
security cryptosystem as a cornerstone. They further concluded that a comparison of a planned
algorithm with the other five algorithms for chaos-based image encryption specifies that their
algorithm has reliable and better performance in the sense of security (Peng, Zhang, & Liao, 2009).
In 2010, Yang Tang and Zidong Wang came up with research on image encryption. They
explained that numerous techniques had been developed to deliver the image information in a very
secure way. Chaotic Coupled Map Lattice is a remarkable system for image encryption. This
system works on the time duration as it increases the transmission of image information so that no
one can understand the sequence. First of all, shuffle the image pixels by providing the tent map
and then add the DCML structure for time delaying. DCML is the abbreviation of Delayed Coupled
Map Lattices. This system provides the puzzle situation during the process as it enhances project

Image Encryption 12
completion (Tang, Wang, & Fang, 2010). CML and DCML are the two systems that are used in
this technique. Coupled Map Lattice (CML) provides the tent map for image shuffling, while
DCML provides the puzzle situation to identify the difference between the two main image
qualities, which can increase confusion for any type of hackers.
Similarly, time-delaying procedure plays a crucial rule for the transmission of information.
The purpose of the delaying procedure is to increase the refuge close of the erection, which creates
the multiple puzzle situation during the process. Numerous researches and experiments on CML
systems give evidence that the use of the respective scheme can be beneficial for the security
program. In addition, various techniques have been derived for the improvement of the CML
system (Tang, Wang, & Fang, 2010). Multiple-Watermarking increases the worth of CML as it
prints the label or company name on an image that gives surety of proper process.
In 2014, Adelaide Nicole Kengnou Telem, Colince Meli Segning, and their team
researched the image encryption scheme using a chaotic logistic map and artificial neural network.
They utilized an external secret key to arise the initial conditions only for the chaotic one-
dimensional logistic maps that are active to create biases and weight matrices of MLP (multilayer
perceptron). They explained that the artificial neural network regulates the weight matrix of
networks during the period of the learning process by the backpropagation algorithm. They further
researched that the plain image before encoding is alienated into four sub-images that are utilized
for the first diffusion stage. The sub-images gained earlier are further alienated into square sub-
images blocks. In addition to this, in the further stage, various initial conditions are worked to
create a keystream that will be utilized for diffusion and permutation of sub-image blocks.
Statistical analysis, sensitivity analysis, and entropy analysis are some security analyses to show
the keyspace of a planned algorithm that is bulky sufficient to make a physical strength outbreaks

Image Encryption 13
infeasible. They carried out computing validation utilizing experimental data with images with
comprehensive numerical analysis, only to authenticate the high safety of the scheme of planned
encryption (Telem, Segning, Kenne, & Fotsin, 2014).
In 2014, Minal Chauhan and Rashmin Prajapati came up with research on the image
encryption by the ANN method. They explained that different process is available for the proper
transmission of image information. Cryptography is a technique that is used to transmit the
information in a very secure way. Encryption has many useful objectives, which gives a very
secure way for the spread and reduces the chances of involvement of a third party or third person.
ANN is the abbreviation of the Artificial Neural Network, which has a relation with the brain
system. It has designed to behave like the brain to achieve numeric purposes. Images have much
secure information on the organization, which needs a proper way to transmit the right destination
(Chauhan & Prajapati, 2014). Various processes have their own efficiency, worth, and work
capability, which gives several performances during the progress.
The proposed paper used the neural network for encryption as it provided effective
outcomes. The chaotic system gives the same outcomes as the input occurs so that no big change
can be seen during the process. First, the chaotic system generates the encrypted image is a very
secure way and then transmits to the ANN system. The ANN system derives the key bits into the
encryption, which further enhances the security of the process. After the completion of the process,
the encrypted image can be implanted on the software such as MATLAB (Chauhan & Prajapati,
2014), where information of the image can be gotten straightforwardly. This new and innovative
technique of encryption is getting fame all over the world, and most companies are preferring this
proposed procedure.

Image Encryption 14
In 2015, Hung and Jungshi Lee came up with research on the chaotic system of non-linear.
They explained that the use of a chaotic non-linear system has amazing outcomes in image
encryption. APFM is the abbreviation of the Amplitude Phase Frequency Model, which is
commonly used in the non-linear process. In this paper, the researcher adopted the nine several
parameters to alter the original color of the image so that it could become difficult to identify the
tangible duplicate (Hsiao & Lee, 2015). This system changes the plain structure of the image and
then converts it into the cryptograph image in which these two situations coincide and develop the
critical condition to understand the difference.
The main purpose of these techniques is to alter the color of the image to enhance the
strength of security. The protection of the color of an image is also the foremost target to secure
the image quality. The different procedures can be used for the fortification and shield of image
information so that a precise path should be available to transmit the right destination. The color
of an image can be endangered by the non-linear filter. It alters the plain condition of the image
and panders into the cipher image to secure the image information. The researches and experiments
on the proposed system have given proof that this technique can be helpful for the secure
transmission of image information.

Image Encryption 15
References
Aarthie, N., & Amirtharajan, R. (2014). Image Encryption: An Information Security Perceptive.
Journal of Artificial, 123-135.
Alireza, Rostami, M. J., & Ghavami, B. (2019). An image encryption method based on chaos
system and AES algorithm. SpringerLink, 6663-6682.
Bani, & Jantan. (2008). Image encryption using block-based transformation algorithm. IJCSNS
International Journal of Computer Science and Network Security, 191-197.
Boriga, R., Dascalescu, A. C., & Diaconu, A.-V. (2014). A New One-Dimensional Chaotic Map
and Its Use in a Novel Real-Time Image Encryption Scheme. Hindawi, 15.
Chauhan, & Prajapati. (2014). Image encryption using chaotic based artificial neural network. Int.
J. Sci. Eng. Res.
Chen, Mao, & Chui. (2004). A symmetric image encryption scheme based on 3D chaotic cat maps.
Chaos, Solitons & Fractals, 749-761.
Dacombe, J. (2017, october 24). An introduction to Artificial Neural Networks (with example).
Retrieved from medium: https://medium.com/@jamesdacombe/an-introduction-to-
artificial-neural-networks-with-example-ad459bb6941b
Dertat, A. (2017, august 8). Applied Deep Learning - Part 1: Artificial Neural Networks.Retrieved
from towardsdatascience: https://towardsdatascience.com/applied-deep-learning-part-1-
artificial-neural-networks-d7834f67a4f6

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Dormehl, L. (2019, January 6). What is an artificial neural network? Here’s everything you need
to know. Retrieved from digitaltrends: https://www.digitaltrends.com/cool-tech/what- is-
an-artificial-neural-network/
Han, Park, Ryu, & Kim. (1999). Optical image encryption based on XOR operations. OptEn, 47-
54.
Hsiao, & Lee. (2015). Color image encryption using chaotic nonlinear adaptive filter. Signal
Processing, 281-309.
N.F.Elabady, H.M.Abdalkader, M.I.Moussa, & S.F.Sabbeh. (n.d.). Image Encryption Based on
New One-Dimensional Chaotic Map.
Pareek, Patidar, & Sud. (2006). Image encryption using chaotic logistic map. Image and vision
computing, 926-934.
Peng, J., Zhang, D., & Liao, X. (2009). A Digital Image Encryption Algorithm Based on Hyper-
chaotic Cellular Neural Network. IOS Press , 269-282.
Tang, Wang, & Fang. (2010). Image encryption using chaotic coupled map lattices with time-
varying delays. Communications in Nonlinear Science and Numerical Simulation, 2456-
2468.
techopedia. (2019, august 22). Artificial Neural Network (ANN). Retrieved from techopedia:
https://www.techopedia.com/definition/5967/artificial-neural-network-ann
Telem, A. N., Segning, C. M., Kenne, G., & Fotsin, H. B. (2014). A Simple and Robust Gray
Image Encryption Scheme Using Chaotic Logistic Map and Artificial Neural Network.
Hindawi, 13.

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V.V.Divya, S.K.Sudha, & V.R.Resmy. (2012). Simple and Secure Image Encryption . IJCSI
International Journal of Computer Science Issues.

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