An Investigation into the Usage of Bounding Boxes in Discriminating
Image Encryption Algorithms
Geetanjli Khambra, Vijay Panse and Shilpa Jackson
Department of Computer Applications, The Bhopal School of Social Sciences, Bhopal, India
Keywords: Bounded Box, Image Encryption, Decryption, Correlation.
Abstract: When it comes to protecting people's privacy and maintaining secrecy, encrypting photographs is the safest
and most effective method. However, the computational expense and treating time required to conduct
complete image encryption prove to be preventive constraints that preclude it from presence employed more
intensively in real time. This is because of the large size and complicated nature of digital images. In order to
solve this issue, many recent studies have turned to selective encryption, a way of encrypting only the most
salient features of an image in an effort to lessen the encryption burden. As a possible contribution, we offer
a selective picture encryption method that uses bounded boxes. The computational repercussions of selective
image encryption have been the subject of experimental studies. It has been determined through testing that
the selective encryption technique is significantly faster than other methods of encryption. Selective
encryption has these features; hence it can be looked at as a viable option. Therefore, this contribution
improves security to an acceptable level during implementation. Experiments with the same have shown
promising results in protecting real-time photos.
1 INTRODUCTION
The best way to ensure privacy and maintain
confidentiality with photographs is to encrypt them.
However, the calculation expense and treating time
required to conduct complete image encryption prove
to be preventive constraints that preclude it from
presence employed more intensively in real time
(Dworak et al., 2016). This is because of the large size
and complicated nature of digital images. In order to
solve this issue, many recent studies have turned to
selective encryption, a way of encrypting only the
most salient features of an image in an effort to lessen
the encryption burden. However, a fair comparison of
its performance to full encryption is needed
(Enayatifara et al., 2017; Yavuz and Yazici, 2016;
Faragallah, 2013).
The most widely used framework for encrypting
images in a chaotic environment is based on a special
set of rules for selective picture encryption (Fister and
Tepeh (2016). The effect of a single pixel can be
correctly diffused to the whole cipher-photo with
numerous conventional rounds of encryption using
the substitution method, in which the pixel values are
modified sequentially, with the alteration completed
to a precise pixel usually depending on the collected
impact of all the preceding pixel values (Wang et al.,
2016; Gu and Ling, 2014; Chen et al., 2017; Chen et
al. 2015). To generate a pseudorandom key stream for
replacement, one can use a logistic map, tent map, or
Lorenz machine, to name just a few of the many
discrete and continuous chaotic structures available
(Shi et al. 2016). The secret password is featured in
the hired chaotic systems' starting parameters and
conditions.
The phases of permutation and substitution are
typically considered neutral. Because the permutation
method best reshuffles the pixel placements while
without pixel price, it is vulnerable in the course of
some of the commonplace attacks, specifically
statistical assaults and seemed/chosen simple text
attacks (Jolfaei and Mirghdri, 2011; Lang, 2015;
Wang et al. 2015; Li, 2016). Pixel charge permutation
and substitution (PS/2) plain-photo Cipher-photo
iterations rounds Using the Permutation Key Chaos-
based image cipher substitution key common form.
The replacement method is secure, but it is
computationally intensive. The permutation method
is much simpler (Su and Gao, 2013).
This is owing to the fact that computations are
carried out over the field of actual numbers, and a
fantastic widespread variety of iterations of a chaotic
Khambra, G., Panse, V. and Jackson, S.
An Investigation into the Usage of Bounding Boxes in Discriminating Image Encryption Algorithms.
DOI: 10.5220/0012491600003739
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics (AI4IoT 2023), pages 397-402
ISBN: 978-989-758-661-3
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
397
map is required for the vital thing movement creation
technique (Sui and Lu, 2014). The computational
precision cannot be a factor in a key sensitivity
analysis of a crypto-instrument. This work introduces
a hybrid model based replacement technique to
enhance the performance of the chaos-based selective
picture cryptosystem (Sui et al., 2015).
The advantages of the digital revolution haven't
been realized without costs, such as restrictions on
copying and sharing digital multimedia system
papers. In order to rise to this challenge, academics
have been working tirelessly to develop novel and
cost-effective document protection strategies for use
in multimedia system documentation. In this setting,
new methods such as coded writing and digital
watermarking are introduced (Wang et al., 2015) .
Another method of protecting the ownership and
authenticity of digital multimedia system material
consists of adding digital watermarks to relevant
documents. As the Internet expands and transmission
technology becomes more commonplace, people will
find it easier than ever to share digital transmission
data like digital images with one another online
(Wangm and Zhang, 2016).
The purpose of this study is to develop a more
secure image encryption technique based on dispersal
and misperception. Image encryption utilizing the
diffusion and confusion techniques, image encryption
using genetic operators, and selective image
encryption using chaos on a Windows machine are all
within the scope of this article.
2 EXISTING WORK DONE
By combining a logistic map with a cellular
automaton, the authors have provided a method for
computing picture encryption. The plan combines
two different types of randomizations, permutation
and diffusion (Liu and Sun, 2016). The pixels are
shuffled using a permutation technique, and then the
dispersion system amplifies the effects of even a
single pixel's alteration throughout the entire image.
The calculation is based on a muddled system, so
even a minor shift in the significant will harvest
drastically different results. The suggested
framework's key feature is its ability to limit attacks
using animal power and to function in noiseless
transmission. The article also suggests that the
framework has strong security based on the results of
key sensitivity analysis, histogram analysis,
differential attack, and other testing (Wu et al., 2015).
This academic study details a quick method of
image encryption. Here, a 2D Sine IMIC Modulation
map is used, and its unstructured capabilities are
analysed using a stage chart, a Lyapunov type range,
and a many-sided quality. At the foundational stage
of encryption, the confusion and dissemination
method are consolidated. The pixels in the image can
be jumbled up very efficiently using a technique
called Chaotic Shift Transform (CST). In addition, a
method of line and segment replacement is used to
reorder the pixels in the image. The scrambling effect
and low-time many-sided quality of CST are
superior. In this instance, we employ the utilization
of several different keys. A 256-bit secret key is
sufficient to withstand a brute-force attack (Zhang et
al. 2013).
Researchers presented an encryption scheme
based on connecting various chaotic maps to provide
secure transmission of therapeutic images. Logistic,
tent, and sine maps are all incorporated into the
suggested procedure. In this method, DICOM picture
pixels are muddled and spread out (Zhang et al.
2012). The manufactured chaos-cryptic arrangement
is submitted to a number of security investigations,
including measurable, differential, key space, key
affectability, deliberate scrambling assault, and select
plaintext attack tests, to validate the severity of the
proposed approach. Measurement analysis,
connection analysis, differential analysis, key space
analysis, key affectability analysis, deliberate
trimming attack analysis, and picked plaintext attack
analysis are just some of the systems used in this
paper to analyse and approve the security and
unpredictability of the proposed plan (Zhang et al.
2014).
In order to optimize for the monarch butterfly, the
authors advocated a greedy approach combined with
a self-adaptive crossover operator (GCMBO).
Movement Administrator and Butterfly Changing
Administrator are combined into one eager system in
GCMBO. Better performance is possible in the future
if more benchmark issues are used, especially real-
world applications (Liu and Li, 2013). Using LTSVR
(Lagrangian twin support vector relapse) and HC
(hereditary calculation) in DCT (discrete Cosine
transform) space, researchers offer a safe and robust
grey scale picture watermarking scheme (Hua and
Zhou, 2016). The key components into which the
watermark will be embedded are determined using
fuzzy entropy. The inability to withstand rotational
and translational attacks is a major shortcoming.
Under the available computational asset, authors
offer an autonomous multi-objective advancement
model that can account for security and quality of
service (Wang et al., 2019).
AI4IoT 2023 - First International Conference on Artificial Intelligence for Internet of things (AI4IOT): Accelerating Innovation in Industry
and Consumer Electronics
398
3 THE PROPOSED WORK
This work introduces a hybrid model based
replacement technique to enhance the performance of
the chaos-based selective picture cryptosystem. The
following details the suggested architecture for a
replacement method for selective picture encryption
with a key generated via chaos-based key generation.
1.1. The face detector is used to identify the
one-of-a-kind photo. The resulting binary image
will be the same size as the original photograph
but will only include the numbers 1 and 0. A value
of one indicates that the area is in the same
location as the corresponding pixel in the original
image, whereas a value of zero indicates that there
is no area there.
1.2. The original imagery is converted to a
series of zeros and ones, known as binary. The
special photographic archive will be discarded.
Subtracting anything from the past (foreground
detection) is another name for this technique.
1.3. The goal of selective encryption is to
reduce the number of photos that need to be
encrypted without compromising security.
Selective encryption methods require the original
image to be chosen.
1.4. To substitute means to change the location
of a pixel in a picture from one location to another.
In this process, the partial encrypted image is
encrypted using a substitution method based on
Hybrid T models.
Algorithm for the proposed method is as follows:
1. Twitch with your original coloured
medicinal image.
2. Grayscale and binary formats can be
created from the provided image.
3. The multi-chaotic key is used for selective
encryption, which results in the scrambled
images.
4. Images with selective encryption using the
S-Box produce encrypted versions of themselves.
The Inverse hybrid substitution method, which
makes use of both key stream and Inverse image
conversion techniques, is the foundation of secure
and selective Chaos-based image encryption (SSIE).
Figure 2 displays a square chart of the suggested
unscrambling algorithm.
Combining key stream and Inverse image
conversion methods, a decrypted picture can be
derived from the encrypted image using the Inverse
hybrid substitution process.
Figure 1: The Proposed Block Diagram.
New
Image
Image
Alteration
Elimination of
background
area
Choice the
image
encryption area
Encryption
system
Fusion
Replacement
Arrangement
Ultimate
Encrypted
2D –LS collective
key stream
An Investigation into the Usage of Bounding Boxes in Discriminating Image Encryption Algorithms
399
Figure 2: Square Chart of SSIE Decryption Process.
4 RESULT ANALYSIS AND
DISCUSSION
This experiment is performed using MATLAB
R2010a, with key generation based on randomness.
Here, authors have discussed about the connection
coefficients, key space inquiry, key affectability
research, and differential investigation that were run
on the proposed plot.
1.5. Association analysis: The first image's
neighbouring pixels are very closely related along
the horizontal, vertical, and oblique axes. Pixels
in the encrypted image should have relationship
coefficients that are sufficiently small to
withstand quantifiable attacks if the encryption
computation was performed correctly.
(1)
A random comparison of 2000 pairs of nearby
pixels in each direction is made with the encoded
equivalents of the images. This is done so that the
associations between close pixels in the plain and
figure images can be analysed and thought about.
Figure 3 presents the correlation coefficient for both
the unencrypted and encrypted versions of the image.
Images that have been encrypted have a more
consistent grayscale than their plain counterparts.
Figure 3: Association of Input Images and the analogous
Ciphered Images.
1.6. Information Entropy Analysis: In order to
objectively assess risks, one can use data entropy.
The information entropy of a figure picture is
calculated in order to evaluate his eccentric
behaviour. The maximum entropy for a grayscale
image is log (28) = 8 and will be attained if the
pixels appear with equal probability, as there are
28 possible grayscale quality.
1.7. We use two quantitative metrics—the
Number of Pixel Change Rate and the United
Average Changing Intensity—to examine the
effect of a single pixel change in the plain-image
on the cipher-image, ensuring the proposed
encryption system is resistant to differential
attack.
(2)
Any modification to the plaintext picture must
result in a corresponding modification to the cipher
text image. The average pixel-wise intensity
difference between the two images can be determined
with the aid of UACI. Images 'C1' and 'C2' are of the
same size.
(3)
Table 1: Different evaluation parameter comparison.
S.
No.
data
Evaluation parameters
Entropy
NPCR
UACI
1
Lungs
7.97
99.76
33.34
2
Eye
7.96
99.45
33.57
3
Heart
7.95
99.32
33.61
4
Bone
7.96
99.84
33.71
Collective
Key stream
Encrypted
image
Inverse
Fusion
Replaceme
nt system
Inverse
Image
transforma
tion
Decrypted
Image
AI4IoT 2023 - First International Conference on Artificial Intelligence for Internet of things (AI4IOT): Accelerating Innovation in Industry
and Consumer Electronics
400
The entropy of several example photos is
displayed in Figure 4. The ability of the suggested
cryptosystem to withstand entropy attacks has been
widely established. The lungs are the most entropic
organ in the body. This can be checked by comparing
the cypher images derived from a standard photo to
one derived from a picture with a single altered pixel.
Adjusted pixel count is the foundation of NPCR. The
NPCR value of several test photos is displayed in
Figure 4. Here, the NPCR and UACI values in bone
are the highest.
Figure 4: Different evaluation parameter comparison.
5 CONCLUSION
Substitution systems were used in the selective image
encryption. The majority of critiques in this field rely
on confusing pixel-based substitution schemes and
other forms of random substitution. While effective,
such encryption methods are unlikely to provide as
much protection as do conventional numbers, which
are less vulnerable to attacks. Therefore, specific
encryption of restorative images based on
substitution computation is a better trade-off between
security and efficiency.
This work presents a hybrid confusion-based
strategy for selective picture encryption that makes
use of bounded boxes. There was a significant
decrease in required processing time and a noticeable
improvement in security. The proposed approach
promises a universally applicable selective
encryption algorithm that may be implemented across
a wide variety of computer distributed systems for
safe, scalable cloud data storage.
REFERENCES
Dworak, K, Nalepa, J, Boryczka, U & Kawulok, M (2016),
‘Cryptanalysis of SDES using genetic and memetic
algorithms’, Springer Computational Intelligence, vol.
642, no. 18, pp. 3-14.
Enayatifara, R, Abdullahb, AH, Isninb, IF, Altameemc, A
& Leed, M (2017), ‘Image encryption using a
synchronous permutation-diffusion technique’, Optics
and Laser in Engineering, vol. 90, no. 2, pp. 146-154.
Erdem Yavuz & Rifat Yazici (2016), ‘A chaos-based image
encryption algorithm with simple logical functions’,
Computers and Electrical Engineering, vol. 54, no. 1,
pp. 471 - 483.
Faragallah, OS (2013), ‘Efficient video watermarking
based on singular value decomposition in the discrete
wavelet transform domain’, International Journal of
Electronics and Communication, vol. 67, no. 3, pp.
189-196.
Fister, I & Tepeh (2016), ‘Epistatic arithmetic crossover
based on Cartesian graph product in ensemble
differential evolution’, Applied Mathematics
Computation, vol. 283, no. 18, pp. 181-194.
Gai-Ge Wang, Suash Deb & Xinchao Zhao (2016), ‘A new
monarch butterfly optimization with an improved
crossover operator’, Springer, vol. 18, no. 3, pp. 731-
755.
Guosheng Gu & Jie Ling (2014), ‘A fast image encryption
method by using chaotic 3D cat maps’, Elsevier-Optik,
vol. 125, no. 17, pp. 4700-4705.
Hang Chen, Camel Tanougast, Zhengjun Liu & Loic Sieler
(2017), ‘Asymmetric optical cryptosystem for color
image based equal modulus decomposition in gyrator
transform domains’, Optics and Laser Engineering, vol.
93, no. 1, pp. 1-8.
Hang Chen, Jiguang Zhao, Zhengjun Liu & Xiaoping Du
(2015), ‘Optodigital spectrum encryption by using
Baker mapping and gyrator transform’, Optics and
Lasers in Engineering, vol. 66, no. 1, pp. 285-293.
Jinhua Shi, Hui Lu & Kefei Mao (2016), ‘Solving the Test
Task Scheduling Problem with a Genetic Algorithm
Based on the Scheme Choice Rule’, Springer, vol.
9713, no. 1, pp. 19-27.
Jolfaei, A & Mirghdri, A (2011), ‘Image encryption using
chaos and block cipher’, Computer and Information
Science’, vol. 4, no. 1, pp. 172-185.
Jun Lang (2015), ‘Color image encryption based on color
blend and chaos permutation in the reality-preserving
multiple-parameter fractional Fourier transform
domain’, Optics Communications, vol. 338, no. 1, pp.
181-192.
Kai Wang, WJ, Pei, LH, Zou, Aiguo Song & Zhenya He
(2015), ‘On the security of 3D Cat map based
symmetric image encryption scheme’, Physics Letter
A, vol. 343, no. 6, pp. 432-439.
Li, C (2016), ‘Cracking a hierarchical chaotic image
encryption algorithm based on permutation’, Signal
Processing, vol. 118, no. 2, pp. 203-210.
Liansheng Su & Bo Gao (2013), ‘Single-channel color
image encryption based on iterative fractional Fourier
0
50
100
Lungs
Eye
Heart
Bone
Evaluation parameters Entropy
Evaluation parameters NPCR
Evaluation parameters UACI
An Investigation into the Usage of Bounding Boxes in Discriminating Image Encryption Algorithms
401
transform and chaos’, Optics & Laser Technology, vol.
48, no. 1, pp. 117-127.
Liansheng Sui & Haiwei Lu (2014), ‘Double-image
encryption using discrete fractional random transform
and logistic maps’, Optics and Lasers in Engineering,
vol. 56, no. 1, pp. 1-12.
Liansheng Sui, Kuaikuai Duan & Junli Liang (2015),
‘Double-image encryption based on discrete multiple-
parameter fractional angular transform and two-
coupled logistic maps’, Optics Communications, vol.
343, no. 1, pp. 140-149.
Wang, XY, Zhang, YQ & Bao, XM (2015), ‘A novel
chaotic image encryption scheme using DNA sequence
operations’, Optics and Lasers in Engineering, vol. 73,
no. 1, pp. 53-61.
Wangm, X & Zhang H (2016), ‘A novel image encryption
algorithm based on genetic recombination and hyper
chaotic systems’, Nonlinear Dynamics, vol. 83, no. 2,
pp. 333–346.
Wenhao Liu & Kehui Sun (2016), ‘A fast image encryption
algorithm based on chaotic map’, Optics and Lasers in
Engineering, vol. 84, no. 1, pp. 26 - 36.
Wu, X, Kan, H & Kurths, J (2015), ‘A new color image
encryption scheme based on DNA sequences and
multiple improved 1D chaotic maps’, Applied Soft
Computing, vol. 37, no. 1, pp. 24-39.
Zhang, Q, Guo, L & Wei X (2013), ‘A novel image fusion
encryption algorithm based on DNA sequence
operation and hyper-chaotic system’, Optik -
International Journal for Light and Electron Optics, vol.
124, no. 18, pp. 3596-3600.
Zhang, Y, Li, C, Li, Q, Zhang, D & Shu, S (2012),
‘Breaking a chaotic image encryption algorithm based
on perceptron model’, Nonlinear Dynamics, vol. 69,
no. 3, pp. 1091-1096.
Zhang, Y, Wen, W, Su, M & Li, M (2014), ‘Cryptanalyzing
a novel image fusion encryption algorithm based on
DNA sequence operation and hyper-chaotic system’,
Optik - International Journal for Light and Electron
Optics, vol. 125, no. 4, pp. 1562-1564.
Zhengjun Liu & She Li (2013), ‘Image encryption
algorithm by using fractional Fourier transform and
pixel scrambling operation based on double random
phase encoding’, Optics and Lasers in Engineering, vol.
51, no.1, pp. 8-14.
Zhongyun Hua & Yicong Zhou (2016), ‘Image encryption
using 2D Logistic-adjusted-Sine map’, Information
Sciences, vol. 339, no. 20, pp. 237 - 253.
Wang, C.P.; Wang, X.Y.; Xia, Z.Q.; Zhang, C., (2019),
‘Ternary radial harmonic Fourier moments based
robust stereo image zero water marking algorithm’. Inf.
Sci. 2019, 470, 109–120.
AI4IoT 2023 - First International Conference on Artificial Intelligence for Internet of things (AI4IOT): Accelerating Innovation in Industry
and Consumer Electronics
402
