Chaos Based Image Encryption Techniques: A Review

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
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Chaos Based Image Encryption Techniques: A Review
Bhavana Sharma1, Jaspreet Singh2
1Assistant Professor, Dept. of CSE, The North Cap University, Gurugram, Haryana, India
2Student, Dept. of CSE, The North Cap University, Gurugram, Haryana, India
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Abstract - Digital images are a common part of
multimedia communication, and in the current scenario
they are frequently used to transmit data. Therefore, their
security is a major concern for them. Chaos maps are good
for encrypting images because they are chaotic, ergodic, and
highly sensitive to initial conditions. Many of the past
proposed image encryption methods relied on low-
dimensional chaotic maps, which are the least secure and
have limited resistance to brute force and statistical attacks.
In attempt to overcome this difficulty, researchers have
created a variety of high-dimensional chaotic maps. This
review article gives a scientific evaluation of a variety of
studies conducted over the recent years that have employed
chaotic in various forms to process digital images in the
encryption.
Key Words: Image Encryption, Chaotic Systems,
Logistics Map, Performance Metrics, Arnold Map,
Cryptography
1. INTRODUCTION
The security of images has become a big problem because
we are exchanging more information through the internet
[1]. To protect confidential photographs from
unauthorized users, many encryption techniques have
been developed in recent years [2].
Cryptography is a science and method of keeping data safe
from prying eyes by storing and transferring it in an
unreadable format for attackers [3]. In reality,
cryptographic methods are supposed to be tough to crack.
As a result, encryption is critical to the security of digital
content [4]. Due to the quantity and redundancy of image
data, current encryption algorithms are not suitable for
encrypting image data, and hence cannot guarantee the
data’s security and confidentiality [5]. Over the last few
decades, various approaches for encrypting image data
have also been presented, with chaotic-based encryption
being the most powerful and widely used due to its erratic
and uncertain character [6].
1.1 OVERVIEW OF CHAOTIC MAP THEORY
For chaotic-based picture encryption, one-dimensional
and multi-dimensional chaotic maps are utilised. The use of
multi-Dimension chaotic maps improves the security of
picture encryption by virtue of its complicated structure
and the inclusion of numerous parameters, but also
increases the difficulty of implementing the method [7].
Matthews employed chaotic to encrypt data for the first
time in the late 1980s, while Habutsu et al. published the
first chaotic block cypher algorithm in 1991. In 1998,
Baptista published a chaotic encryption scheme. In
addition, Friedrich stated that to achieve a high level of
security, the picture encryption method should be repeated
in two steps: diffusion and permutation [8]. The
permutation stage is required to minimise the high
correlation between adjacent pixels. Permutation methods
are divided into two categories: pixel and bit level. The
diffusion phase is responsible for modifying pixel values in
order to develop an oscillatory behavior and avoid the
attack [9].
Almost all proposals for chaotic-based picture
encryption are inspired by two factors: I the potential for
reduces the computational effort when compared to
standard encryption; and (ii) purported security issues
when classical cyphers are applied on images [10].
1.2 IMAGE ENCRYPTION USING A CHAOTIC
SYSTEM
Non-chaos techniques and chaos-based or are two types
of picture encryption. In chaos-based approaches, initial
conditions are extremely important. If we modify
something in the starting situation, the entire outcome will
change [11], [12], [13], [14], [15], [16].
Healthcare, military, online communication, photo
messaging applications for smart phones, multimedia
systems, images used in healthcare, Tele-medicine, and
government papers are just few of the applications of the
encryption concept based on chaos [14].
Diffusion and Confusion are the two phases of a chaos-
based encryption method. The chaos-based picture
encryption technique is depicted in block diagram in
Figure 1. The phase of confusion occurs when the position
of pixels is scrambled without changing their value. The
purpose of a diffusion phase is to alter the estimation of
each pixel in the image [11], [17]. Figure 2 illustrates the
connection between chaotic map theory and
cryptography.

Fig -1: Chaotic Encryption Algorithms Approach
Fig 2: Chaos Based Cryptography
2. KEY SECURITY EVALUATIONS
2.1 KEY SECURITY ASSESSMENT
In both the encoding and decoding processes, a good
encryption method should concentrate on the encryption
key. The total number of unique keys that can be used to
encrypt data is known as the key space estimate. The text
pictures are sent out in the open, while the security key is
sent out in secret. The security key must have a size that is
difficult to estimate by brute force attackers and be
sensitive to such an attack in order to be valid
2.2 EVALUATION OF DIFFERENTIAL ATTACK
The differentiated attack is a frequent and extremely
successful security strategy. Two metrics that may be used
to measure the resilience of image computation encoding
to differential assault are the unified average change
intensity (UACI) and the formally number of pixels change
rate (NPCR).
Where,
d1(k,l) and d2(k,l) Described as encrypted image before
and after one pixel of original image is changed.
2.3 EVALUATION OF CORRELATION COEFFICIENT
An encrypted image with a constrained connection
between pixel values should be produced by a precise
encrypted image. Using correlation analysis to uncover
correlations between the cipher’s pixel values would be
the most efficient technique to determine the suggested
picture ’s security. In a number of orientations, the pixels
of an original image are always tightly connected to their
neighbours. In this vein, a good picture encryption
algorithm should be able to remove significant
correlations between nearby pixels.
where E(.) is the expectation value and µ & σ are the
standard deviation and mean value respectively.
2.4 HISTOGRAM EVALUATION
The histogram is a technique for determining how
encrypted picture data is distributed. The histogram
should be uniform, keeping in mind the fundamental
purpose of surviving alone and statistical attacks, which is
the goal. A histogram analysis can be used to examine the
uneven distribution of pixel values in an encrypted
picture. It’s reasonable to believe that the pixels in an
encrypted image are scattered equally, making brute force
assaults to extract the plain image challenging.
3. LITERATURE REVIEW
3.1 CHAOTIC ENCRYPTION TECHNIQUE, 2014
[18]
They provide a novel way to creating the brand-new
chaotic map on this examine, as well as a higher photo
encryption scheme based on it. A significantly more
(1)
(2)
(3)

maximal Lyapunov exponent means that this lately built
chaotic map has many higher chaotic capabilities for
encryption. In addition, the Arnold’s Cat Map &
unconventional chaotic map based totally image
encryption technique has been devised and demonstrated
to be solid. The security analysis & simulation out- comes
that this approach not only meets the want of envision
encryption, however, also offers advanced effectiveness
and security, making it suitable for
[21]
The authors of this study present a new image block
encryption technique based on a number of frequently
used chaotic maps. The image blocking strategy in this
algorithm
and specific chaotic maps are used to implement both
substitution and shuffling algorithms.
Paper Cipher-Text
Image
Entropy
Image
Name
NPCR UACI Key Space Correlation of Encryption Image
H V D
[18] 7.9977 Lena 99.54 33.36 1048 0.0063 0.0062 0.0069
[19] 7.9991 Lena 99.63 33.52 2193 -0.0180 0.0035 -0.0020
[20] 7.9991 Peppers - - 1084 -0.0007 0.0006 0.0002
[21] 7.9978 Lena 99.58 33.36 2231 0.0012 0.0013 0.0020
[22] 7.9993 Lena 99.61 33.48 2428 0.0013 0.0008 0.0066
[23] 7.9994 Lena 99.60 33.44 - -0.0043 -0.0007 0.0030
[24] 7.9994 Airplane 99.62 33.90 - 0.0006 -0.0011 0.0029
[25] 7.9975 Lena 99.62 33.41 240+2384+28 2.9903e-04 −3.4722e-
05
−2.7381e-
05
An efficient picture encryption system primarily based on
lookup table-based totally confusion and diffusion is
proposed on this paper. In contrast to conventional chaos-
primarily based block , the proposed method requires a
long way much less chaotic map iteration and no
measuring operation. As a result, their technique is extra
efficient and has a faster encryption speed. Furthermore,
their approach has ideal noise resistance and robustness
towards statistics loss. All of these benefits make the
proposed system ideal for actual-time stable image
transmission in real-international networks.
[20]
They suggested an image encryption technique that is
based on a chaotic inertial neural network & piecewise
linear chaotic map(PWLCM). The data is encrypted using
the chaotic signals generated by the inertial neural
network. A chaotic system (a continuous system) and a
chaotic map(a discrete system) are both included in the
algorithm. They scrambled the image's pixel values at
random before encrypting it with the outputs of an inertial
chaotic neural network.
[22]
This study presents a new and safe image encryption
strategy based on the hyper-chaotic Lorenz system and
hash function. They used the traditional encryption
architecture of diffusion & permutation in their
scheme. The information from the original image and the
first key are used to construct the initial conditions of the
hyper-chaotic Lorenz system, which is used in
permutation and key stream creation methods.
[23]
This study presents an image encryption technique based
on the Chaotic Coupled Sine Map (CCSM), a one-
dimensional chaotic map. A dynamic approach is
introduced to the block- based image scrambling after the
of the new chaotic map and the block size is dynamically
configurable.
Table -1: Comparison of Different Chaotic Map Systems based on Encryption Algorithms Results in Papers
[26] 7.9994 Lena 99.60 33.47 2504 0.0013 -0.0012 -0.0015
[27] 7.9959 Lena 99.62 33.46 2398 0.0019 0.0035 0.0008
[28] 7.9915 Lena 99.60 33.45 - 0.0006 -0.0017 -0.0008
[29] 7.9994 Lena 99.62 33.49 - 0.0008 0.0006 0.0033
[30] 7.9970 Lena 99.63 30.34 2425 0.0023 -0.0016 -0.0013
a wide range of programs.
[19]

[24]
In this study, they presented a novel system that is less
computationally intensive and more secure. A three-
dimensional Lorenz chaotic map and a fractal-key
shuffling mechanism form the basis of the system. The
confusion -
Table -2: Review of Chaotic Maps
Paper Title Author Algorithms Features Parameters Research Gap
[18] A new image
alternate
encryption
algorithm based
on chaotic map
Wang & Guo Shuffle &
Diffusion, 0,1
Sequence
Highly Secure,
High Key
sensitivity, High
key space
Logistic
Map, XOR
Differential attack can
be used to defeat this
strategy. An example
of a well-known
plaintext attack.[31]
[19] An efficient image
encryption
scheme using
lookup table
based on
confusion &
diffusion
Chen, Zhang Confusion &
diffusion,
Latin square,
LUT based
permutation.
Larger key
space, higher
key sensitivity
Lookup
table used
Combination attacks
using both chosen-
plaintext and chosen-
cipher attacks were
utilized to break this
method because of its
inherent fault that the
same key was used in
both permutation stages
as well as diffusion.[32]
[20] Synchronization
of an Inertial
Neural Network
With Time-
Varying Delays
and Its
Application to
Secure
Communication
Shanmugam
Lakshmanan
et al.
S-Box, P-Box,
PRNG,
Shuffling. RK-
4
Robust and
resistant
against
different
security attacks
PWLCM,
chaotic
inertial
neural
network.
The encryption
technique is
completely reliant on
the secret key and has
little to no reliance on
awaiting plain
pictures. It is possible
to decrypt an
encrypted image
using a chosen-
plaintext attack,
which eliminates the
necessity for a secret
key.[33]
[21] Image Block
Encryption
algorithm based
on chaotic map
L.Liu, S.Hao.
et al.
Shuffling &
Substitution
Algorithm
Larger Key
Space, Key
sensitive
Arnold Map,
Baker Map
With a few chosen
plain images, the
Secret Key may be
easily obtained. Every
security metric in use
is unable to verify its
true security
performance.[34]
[22] A novel plaintext
-related image
encryption
scheme using
hyper-chaotic
system
Zhen Li et al. Permutation
Algorithm,
Hash
Functions
Effective at
resisting the
known/selected
plaintext attack,
resist entropy
attacks
Lorenz Map It has two flaws, one
of which may be
exploited by creating
a special picture and
then permuting the
permuted image.[35]
[23] Digital image
scrambling based
on a new-
dimensional
coupled sine map
Behzad Irani
et al.
Block-based
image
scrambling
Better
performance,
High security,
Reliable & safe
Logistic
Map, Sine
Map
Secret keys were
extracted, and the
original plain image
was retrieved.[36]

[24] A novel hybrid
Secure Image
Encryption Based
on Julia Set of
Fractals & 3D
Lorentz Map
Fawad
Masood et al.
Confusion,
Diffusion,
Shuffling
Higher Security,
High Sensitivity,
Low peak to
SNR
3D Lorenz
Map, XOR
Encryption keys may
be obtained using
only three plain text
images that have
been chosen.[37]
[25] Chaotic image
encryption
algorithm based
on hybrid multi-
objective
particle swarm
optimization and
DNA sequence
Xingyuan
Wang, Yanpei
Li
Fisher-Yates
shuffling,
Multi-
objective
particle
swarm
optimization
algorithm,
SHA-384
High Key Space,
High Key
Sensitivity.
Logistic
Map, DNA
-
[26] Image Encryption
based on Roulette
Cascaded Chaotic
System &
Alienated Image
Library
X.Wang, P.Liu Roulette
Wheel
Selection,
SHA-512,
Scrambling
Diffusion.
High Key Space,
High Key
Sensitivity
Logistic,
Logistic-
Sine,
Logistic-
Tent, Sine-
Tent.
Very Complex.
[27] A novel color
image encryption
scheme based on
a new dynamic
compound
chaotic map & S-
Box
Tahir S. Ali et
al.
PRNG, S-Box,
SHA-256,
RSA.
High Key Space,
High Key
Sensitivity.
PWLCM,
Tent Map.
It is based on multi-
chaotic maps, which
may necessitate a
huge amount of
memory as well as
increased computing
expenses.
[28] Bit-Level Color
image encryption
using Logistic-
Sine-Tent-
Chebyshev(LSTC)
Map
S.M.Basha et
al.
Diffusion,
Cyclic Shift.
Resistant to
statistics
attacks, High
Key Space.
LSTC Map. Slower Computing
Speed than the
Traditional
Encryption
Algorithms.
[29] A Novel Chaotic
Permutation-
Substitution
Image
Encryption
Scheme Based on
Logistic
Map and Random
Substitution
Khan et al. S-Box, AES,
SHA-256
Robust against
statistical
attacks.
XOR,
Logistic Map
Only grayscale
photographs, not
colour images, may be
utilised with their
proposed system.
[30] RGB Image
Encryption
through Cellular
Automata, S-Box
and the Lorenz
System
Wassim
Alexan et al.
Cellular
Automata, S-
Box, PRNG
Any statistical,
differential, or
brute-force
attacks are
resistant to the
suggested
approach.
Lorenz, XOR Dissipative and poor
ergodicity
characterize the
Lorenz system
utilized in the third
stage, compared to
conservative chaotic
dynamical systems.
property has been applied to the shuffled picture, further
confusing the pixels. A three-dimensional Lorenz chaotic
map was used for the diffusion process, which distorted
each pixel in the picture.

[25]
This study investigates a multi-objective particle swarm
optimization (MOPSO), DNA encoding sequence, and one-
dimensional Logistic map-based image encryption
technique. The key in this research is made up of a sub-key
sequence chosen via particle swarm optimization (PSO),
an image hash value, and a shuffle mark bit. Using a
logistic map and DNA encoding, generate random DNA
mask pictures and then combine it with the DNA encoding
sequence that has been block-shuffled to create an
encryption system. The iterative PSO technique is based
on the information entropy and correlation coefficient,
and the location value of a particle reflects a position of
the picture. Finally, gets the best and returns the current
value of the best particle.
[26]
This research uses the roulette jump selection chaotic
system and alienated image library transformation to
develop an image encryption technique. The fitness of
each system is determined by the algorithm using the
original image. Run different chaotic systems through a
fitness test to see which ones are the most fit and then use
the roulette method to control their parameters in order
to reduce their dynamic degradation. This study presents
a transformation that operates on plain images and many
pictures in the agreed image library, significantly
increasing encryption security. Synchronous scrambling
diffusion is used in scrambling diffusion. An image is
simultaneously scrambled and diffused by a pair of row
and column mapping arrays, and its pseudo-random
sequence is sorted in a row and column order.
[27]
This paper presents a new technique for encrypting and
colour images using the S-box and chaotic system. For this,
a hybrid technique is used. The software creates
encrypted versions of images based on a master key. After
encrypting with the receiver's public key, the master key is
delivered to him using the asymmetric technique RSA. The
symmetric technique is used in encrypting and data.
During the encryption phase, the created S-box by PWLCM
substitutes picture pixel values, resulting in a visual
confusion and diffusion. For generating random chaotic
sequence values, the tent logistic system is used as a
PRNG. The sequence was mixed with substituted picture
pixels values and their previous values using a mixing
technique. To create a noise-like effect in the encrypted
image, a self-mixing technique is used on the image
components.
3.11 Chaotic Encryption Technique, 2022 [28]
Based on the Logistic-Sine-Tent-Chebyshev (LSTC) map,
the research presents a bit-level chaos based image
encryption scheme. The image is first disassembled into
its component of red, blue, and green. Each component of
a picture is converted to an 8-bit binary plane. The LSTC
map, XOR operation, and cyclic shifts are used to scramble
two binary elements by mutual diffusion of two sequences.
A chaos map is used to perform binary element confusion,
which is subsequently translated into binary bit planes.
The binary element is swapped and turned into another
binary bit plane using the chaos map. Combining binary
bit planes creates individual text image components.
Finally, the encrypted image component and the text
picture are joined to create the text picture.
[29]
This work offers a new chaos-based image encryption
solution based on permutation and substitution a single
Substitution Box to tackle challenges in current picture
encryption approaches (S-Box). The recommended
technique employs chaotic permutation, substitution, and
the XOR operator. AES S-Box is used to obtain the lowest
amount of correlation between the image pixels. In their
suggested method, encryption is done in two phases. The
first stage generates encryption keys from the picture
using the SHA-2 256 hashing. After then, the hash is
divided into multiple portions, each of which is mapped
between 0 and 1, making it excellent for logistic mapping.
The image pixels are permuted in columns and rows in the
second stage using numbers produced using a chaotic
logistic map. The logistic map's beginning population is
made up of the hash values created in the first step. The
XOR operation is done to the permuted picture pixels and
the logistic map's integers after permutation. Following
that, the picture is subjected to a replacement method.
This research looks at logistic maps and employs AES and
reverse S-Boxes to replace pixel values. The encryption
quality worsens when a large number of less secure S-
Boxes are for picture encryption.
[30]
This paper proposes an image encryption strategy to help
in the solving of such a problem. The planned strategy's
execution is divided into three parts. The first stage uses
Rule 30 cellular automata to generate the initial
encryption key. A tried-and-true S-box with a
transformation, modular inverses, and permutation is
used in the second step. Finally, the third stage employs a
Lorenz system solution to produce the second encryption

key. This three-stage method's combined impact assures
that Shannon's confusion and diffusion aspects of a system
are used, enhancing the security and resilience of the
encrypted pictures. Combining the S-box with the PRNG
from both the cellular automata and the Lorenz system as
keys results in well-encrypted pictures with the needed
non-linearity and complexity.
4. CONCLUSION
The chaotic image encryption method is one of the best
ways to encrypt an image. In this work, different chaotic
picture encryption algorithms are looked at, discussed,
and assessed.
The primary purpose of this work is to give an integrated
look at how the chaotic map has been used in encrypting
over the last ten years, along with other techniques that
we have talked about in our research based on the years
and techniques that support the chaotic map. We shared
our research so that other researchers could build on it by
choosing the right chaotic map strategies. Because of this,
we gave each academic researcher a summary of the many
studies that used chaotic maps.
When we send information over a medium that isn't
secure, information security becomes more and more
important. A safe way to send data can be done in a
number of ways. One is to encrypt the data, which is set up
to be sent in a mixed way and decoded when the data is
needed. The article talks about different chaotic maps that
are used to encrypt pictures, as well as their pros and
cons.
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