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Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 15
Available Online at www.ijcsmc.com
International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
ISSN 2320–088X
IMPACT FACTOR: 7.056
IJCSMC, Vol. 12, Issue. 9, September 2023, pg.15 – 38
Hybrid, Secure and Highly Speed Method
for Color Image Cryptography
Prof. Ziad AlQadi
Albalqa Applied University
Jordan Amman
DOI: https://doi.org/10.47760/ijcsmc.2023.v12i09.003
Abstract
A new method of color image cryptography will be produced, it will be shown the simplicity and easy use of this
method, which can be used to encrypt-decrypt any image with any size without doing any changes in method
operations. The proposed method will provide a high level of image protection by increasing the private key space to
make the hacking process impossible. The proposed method will use two private keys to insure image protection,
the first key will be a resized color image_key, the second key generated using chaotic logistic map model with a
selected secrete parameters. The generated two 3D keys will be used to apply two rounds of XORing to encrypt-
decrypt the color image. The proposed method will be implemented and the results will be analyzed. Several types
of analysis (Quality, speed, correlation, speed, sensitivity, and simplicity) will be performed to prove the
enhancements provided by the proposed method comparing with other existing methods of image cryptography
Keywords: Cryptography, PK, image_key, CLMM key, MSE, PSNR, CC, throughput
Introduction
Digital color image [2130]- is one of the most important types of digital data, it used in various applications, and it
requires high level of protection due the following:
1) The image may be very confidential.
2) The image may be private.
3) The image may be holding secret messages.
For these reasons and others more the image requires protection from being hacked by any third unauthorized party.
One the method of image protection is image cryptography, which means encrypting the image before sending, and
decrypting it after receiving. Cryptography can be done using one or more private keys (PK) and a selected
algorithm as shown in figure 1 [31-40].
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 16
The main contribution of this paper research is to introduce a new method of image cryptography, which will
enhance the process of color images cryptography by providing the following positive features [41-45]:
a) Keep the quality factors: mean square error (MSE), peak signal to noise ratio (PSNR), correlation coefficient (CC)
in the encryption phase (between the input and the encrypted images acceptable (MSE must be very high, PSNR
must very low and CC must be closed to zero), while in the decryption phase MSE must be equal zero, PSNR must
be equal infinite and CC must be equal zero, the quality between two images can be measured using equations 1, 2,
and 3 [46-55].
Figure 1: Image cryptography
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 17
Where:
b) The method must be highly secure: The PK must provide a huge key space so that it cannot be guessed or hacked.
The private key will be hybrid, sensitive to any changes in the parameters values used to generate the key.
c) The proposed method will minimize the encryption-decryption and maximize the throughput, the provided
throughput will be better than the throughputs provided by other existing methods.
d) Simple to use and implement, the method will suit any image with any length, changing the image does not require
any modification in the method sequence of operation.
The image is processed easily and without any difficulties, because the digital image can be represented (as shown
in figure 2) by a three-dimensional matrix (a two-dimensional matrix for each of the three colors: red, green and
blue)[56-63].
Figure 2: 3D matrix to represent color image
Color image includes a huge number of pixels, each pixel as shown in figure 3 requires 3 bytes of memory to be
stored, and the value of the pixel is a result of mixing the three colors as shown in figures 4 and 5 [64-70]. The
pixel’s color values will be an integer value within the range 0 to 255, so it is better to use an integer key to be used
for color image cryptography [71-75].
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 18
Figure 3: Color pixel
Figure 4: Mixing colors to for the pixel color
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 19
Figure 5: Pixel components
Image cryptography is a pixel operation; each pixel in the image to be encrypted-decrypted must be processed.
Digital images usually have a huge size, so we have to seek a method to decrease both the encryption and decryption
times [74-80]. The faster approach of image cryptography is XORing image with a secret private key, if the
cryptography is implemented in burst way by using a key with size equal image size then we will save a lot of time,
here the key size must match any other image (to be encrypted) size [81-88]. Image resizing can be easily
implemented using built in matlab function 'imresize', and it can be used to reduce or expand the image size as
shown in figure 6.
Figure 6: Image resizing
Related Work
A lot of methods for data cryptography were introduced, some of these methods were based on data cryptography
standards such as DES, 2DES, AES and BF, these methods are good for encrypting-decrypting small is size data,
using these methods for data with big size (digital image) will require big time and the throughput will be very low,
in [1] a comparative analysis of standard methods of data cryptography was provided and it was shown that the
average throughputs of DES, 3DES, AES and BF were 835, 282, 491, and 1038 bytes per second respectively.
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 20
In [2] a performance comparison between chaotic and non-chaotic methods of data cryptography was done and it
was shown that the average throughputs of non-chaotic, chaotic and hyper chaotic methods were 170.3, 141.2 and
636.3 K bytes per second respectively, which are better than standard methods.
Some other methods were introduced to enhance the cryptography throughputs, in [8] the authors introduced a
method, the throughput was enhanced to reach 169.1 K bytes per second, while in [9] the introduced method
enhanced the throughput to reach 710 K bytes per second.
Faster methods were introduced; these methods were used to minimize the encryption-decryption times and to
maximize the throughput of data cryptography, in [3] the authors provided a robust and fast image encryption
scheme based on a mixing technique. In [4] the authors provided cosine-transform-based chaotic system for image
encryption, while in [5] the authors introduced a novel image encryption algorithm based on polynomial
combination of chaotic maps and dynamic function generation. In [6] the authors introduced Multiple-image
Encryption Algorithm Based on DNA Encoding and Chaotic System, while in [7] the authors produced a multiple-
image encryption with bit-plane decomposition and chaotic maps, these methods provided good quality a have
various speeds as shown in table 1.
The Proposed Method
The proposed method will use two rounds of color image cryptography as shown in figure 7.
Figure 7: Proposed diagram of the proposed method
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 21
The first round will use the first secret key which is generated by using chaotic logistic map model (CLMM) using
the selected secret chaotic parameters R, C, r and x, here R and C point to the 3D chaotic key dimensions, r and x
are the growth rate and the initial population of CKMM which can be calculated using equation 4:
The second round will use the output image of the first round, this image will be XORed by the second secret key,
and this key must be generated from a secret image_key by resizing this image to match the sizes of the image to be
encrypted-decrypted [9-12].
Using these two keys will increase the level of security or the following reasons:
- The image-key is to be kept in secret and without transmission, the generated key from this image will have the
image to be encrypted size, so it is impossible to guess or hack this key.
- The second key is generated by applying CLMM parameters, these parameters have a double data type and 64 bits
are reserved for each parameter, so the CLMM parameters will provide a key with a huge key space, hacking or
guessing this space will be a very difficult target.
The first key is sensitive to the selecting image_key, changing this image will totally change the first key. The
second key is very sensitive to any change in one or more of the CLMM parameters, any minor change in one or
more parameters values will lead to change the second secret key.
The second secret key is generated by applying the CLMM using a selected secret values of the chaotic parameters,
applying these values will generate a 3D CLLM key, the obtained key then must be converted to integers and
resized to match the size of the image to be encrypted, figures 8 thru show an example of second key generation:
Figure 8: 3D chaotic key example
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 22
Figure 9: Chaotic key converted to integers
Figure 10: Resized integer key
The proposed method of image cryptography can be implemented applying the following algorithm:
Inputs
Image to be encrypted/decrypted, CLMM parameters values, image_key
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 23
Output:
Encrypted/decrypted image
Process
1) Get the image to be encrypted/decrypted
2) Retrieve the image size
3) First secret key generation
a) Get the image key
b) Resize the image_key to the image size to obtain key 1
4) Second secret key generation:
a) Get the CLLM parameters (R, C, r and x)
b) Apply CLMM model to obtain CLMM key
c) Convert CLMM key to integers
d) Resize the integer key to match the image size.
5) Apply XORing the encrypted/decrypted image to get decrypted/encrypted image (Round 1)
6) Apply XORing the decrypted/encrypted image using the results of step 5 to get the encrypted/decrypted image
(round 2)
For researchers who want to reproduce the methods outputs the following mat lab code can be used:
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 24
Implementation and Results Analysis
Different images with various sizes were selected (figures 11 the selected images), these images were treated using
the proposed method. Below we will provide a detail analysis of the obtained experimental results.
Figure 11: Selected images
1) Quality analysis
The image “sampleMerry_0055_Lasalle.jpg” was selected as an image_key, the following were selected as CLMM
parameters value:
R=40; C=50, H=3; r1=3.9; x1=0.025;
Figure 12 shows the used image_key
The selected images were encrypted-decrypted using the proposed method, MSE, PSNR were calculated in the
encryption and decryption phase. In the decryption phase the values of MSE were always equal zero, while the
values of PSNR were always equal infinite, table 2 shows the calculated values of MSE and PSNR between the
input and the encrypted images:
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 25
Figure 12: Used image_key
Table 2: MSE and PSNR between input and encrypted images
Image
MSE
PSNR
1
13309
15.8634
2
11161
17.6235
3
98012
18.9226
4
97820
18.9423
5
84066
20.4576
6
12345
16.6151
7
11802
17.0651
8
96359
19.0927
9
11898
16.9836
10
69213
22.4018
Figures 13, 14 and 15 show a sample outputs
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 26
Figure 13: Input image (example)
Figure 14: Encrypted image (example)
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 27
500 1000 1500
200
400
600
800
1000
0
1
2
3
4
x 104
0100 200
0
1
2
3
4
x 104
0100 200
0
1
2
3
4
x 104
0100 200
Figure 15: Decrypted image (example)
From table 2 and figures 12 to 14 we can see that the proposed method totally destroyed the input image when
encrypting it, this is proved by the low values PSNR and the high values of MSE. In the decryption phase the images
were totally recovered, this is proved by a zero MSE and infinite PSNR.
The obtained here results prove that the proposed method satisfies the quality requirements of good method of image
cryptography.
2) Sensitivity Analysis
The proposed method is very sensitive in any changes in the image_key and CLMM parameters values, any minor
changes in them in the decryption phase will produce damaged decrypted images, these changes will be co nsidered
as a hacking attempt.
Image 10 was encrypted using the key_image shown in figure 12, and using the CLMM parameters:
(R=40;C=50,H=3;r1=3.9;x1=0.025), the decryption phase use image 9 as an image_key, figures 16, 17 and 18 show
the generated outputs:
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 28
Figure 16: Input image (image 10)
Figure 16: Encrypted image using first image_key
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 29
Figure 17: Decrypted image using second image_key
As we can see from figure 17, the process of decryption is sensitive to changing the image_key, changing the
image_key will produce a corrupted damaged decrypted image (with low values of PSNR and CC and high values
of MSE).
Now we will see how the process of cryptography is very sensitive to any changes in CLMM parameters required to
generate the second secret key.
Image 10 was encrypted using image_key shown in figure 11, the parameters :( CLMM 1=
R=40;C=50,H=3;r1=3.9;x1=0.025) were used in the encryption phase, while the parameters: (CLMM
2=R=40;C=50,H=3;r1=3.9;x1=0.037) were used in the decryption phase, figures 18 and 19 show the obtained
images:
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 30
Figure 18: Encryption using CLMM 1
Figure 19: Decryption using CLMM 2
As we can see from figure 19 the decrypted image is a damage image with unacceptable quality.
3) Statistical Analysis
A good method of image cryptography must provide good values of CC between images colors in the encryption
and decryption phases, the values of CCs in the encryption phase must be closed to zero (very low), while in the
decryption phase the values of CCs must equal 1, table 3 shows the proposed method of encrypting-decrypting the
selected images, the results show that the proposed method gives an excellent values of CCs, and that the proposed
method totally destroys the image in the encryption phase, and totally recover the image in the decryption phase.
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 31
Table 3: Obtained CCs
Image
Between input and encrypted image
Between input and decrypted image
RED CC
Green CC
Blue CC
RED CC
Green CC
Blue CC
1
0.0131
-0.0057
-0.0746
1
1
1
2
0.0033
-0.0261
-0.0309
1
1
1
3
0.0151
-0.0113
-0.0520
1
1
1
4
0.0217
-0.0299
-0.0659
1
1
1
5
0.0075
0.0117
-0.0270
1
1
1
6
0.0172
-0.0223
-0.0380
1
1
1
7
0.0143
-0.0141
-0.0775
1
1
1
8
0.0369
-0.0285
-0.0553
1
1
1
9
0.0097
-0.0082
-0.0484
1
1
1
10
0.0028
-0.0041
-0.0114
1
1
1
4) Speed Analysis
In this section we will prove how the proposed method enhances the cryptography efficiency by decreasing the
encryption/decryption time and increasing the cryptography throughput (encrypted/decrypted byte is a second,
which is equal image size divided by encryption time).
The CLMM key requires time for generation, this time will increase when increasing the CLMM key, so it is
recommended to use a CLMM key with small size, this size can be resized to match the image size, doing this we
can optimize the total time of encryption, to see that various CLLM keys were generated, table 4 shows the expected
key generation time.
Table 4: CLMM key generation
Size
Number of elements
Generation time (second)
10x10x3
300
0.000001
40x50x3
6000
0.001000
60x80x3
14400
0.003000
80x80x3
19200
0.004000
100x100x3
30000
0.005000
200x200x3
120000
0.046000
300x200x3
180000
0.104000
500x500x3
750000
0.995000
500x1000x3
1500000
2.152000
From table 4 we can see that CLLM key generation time will rapidly increase when increasing the key size (see
figure 20), so it is better to select a key with small size.
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 32
Figure 20: CLMM key generation time vs key length
The selected images were treated using the proposed method, the encryption and decryption phases used image_key
shown in figure 12, the CLMM parameters were :( R=40; C=50, H=3; r1=3.9; x1=0.025), the encryption time was
measured; table 5 shows the obtained results (the decryption time was equal the encryption time):
Table 5: Speed results
Image
Encryption time
Throughput(K byte per second)
1
0.0430
3425.9
2
0.0440
11506
3
0.0530
94723
4
0.0520
81246
5
0.0430
2776.7
6
0.0420
12054
7
0.0430
3428.8
8
0.0420
3510.4
9
0.0460
40124
10
0.0570
104840
Average
0.0465
35763
From table 5 we can see that the proposed method enhanced the throughput of the image cryptography, the proposed
method has a significant speed up comparing with method mentioned in the related work part and as shown in table
6:
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 33
Table 6: Throughput (speed) comparisons
Method
Throughput
Speedup of the proposed method
Proposed
35763
1
In [3]
888.8867
40.23347407492991
In [4]
668.4082
53.50472959487930
In [5]
911.0352
39.25534381108436
In [6]
360.4102
99.22860118831265
In [7]
384.9609
92.90034390505633
Increasing the CLMM key length will keep the method quality high, but the encryption time will be increased, thus
the throughput will decrease and it will remain better than the throughputs of the introduced methods in [3 -7] (see
table 8).
The selected images were treated using other CLMM parameters: (R=240; C=250, H=3; r1=3.9; x1=0.025), these
images were treated using the proposed method; table 7 shows the obtained results:
Table 7: Speed results using another CLMM key
Image
Encryption time
Throughput(K byte per second)
1
0.1370
1075.3
2
0.1300
3894.2
3
0.1370
36645
4
0.1910
22119
5
0.1260
947.6144
6
0.1580
3204.1
7
0.1270
1160.9
8
0.1290
1142.9
9
0.1600
11536
10
0.1410
42382
Average
0.1436
12411
Table 8: Throughput (speed) comparisons (using bigger CLMM key)
Method
Throughput
Speedup of the proposed method
Proposed
12411
1
In [3]
888.8867
13.9624
In [4]
668.4082
18.5680
In [5]
911.0352
13.6230
In [6]
360.4102
34.4358
In [7]
384.9609
32.2396
5) Security Analysis
The proposed method uses two keys to apply image cryptography. The first key is to be generated from a secret
image_key, which is to be kept in secret between the sender and receiver without transmission, the generated key
from this image is a 3D matrix, this matrix cannot be guessed or hacked because it has a huge key space.
The second key is to generated by running the CLMM with a secrete parameters, each parameter has a double data
type which requires 64 bits for representation , thus the number of combinations will be high, and a high number of
combinations will provide a big key space which is difficult to guess or to hack.
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 34
The used two keys are very sensitive to any change, any changes in one or in the two keys in the decryption phase
will be considered as a hacking attempt and a damaged, corrupted decrypted image will be produced.
6) Simplicity Analysis
The proposed method can be implemented in a simple sequence of operations, and it can be implemented using any
programming language. The proposed sequence can be used to encrypt-decrypt any image with any size (color or
gray images), it is very easy to change the image to be encrypted, the image_key and the CLMM parameters,
changing one or more of these input does not require any changes in the sequence of operation.
Conclusion
A simple and easy to implement method of image cryptography was introduced, the method can use any image, an
image_key and any CLMM parameters, changing one or more input does not require any changes in the method
algorithm. The proposed method used two secret keys to secure the image, the first key is huge key generated from
a secret image_key, the second is CLMM key generated by a selected secret chaotic parameters, the two key
provided a huge key space, this key space will provide a high degree of image security and protection.
The proposed method provided an excellent quality, the obtained MSE, PSNR, and CC values in the encryption and
decryption phases satisfied the quality requirements, the proposed method total destroyed the image in the
decryption phase, and totally recovers the original image in the decryption phase.
The proposed method gave on optimal encryption-decryption time and it enhanced the throughput of cryptography,
the obtained results were compared with other existing method, and it was shown that the proposed method has a
significant speed up.
The obtained results were analyzed, the results of analysis proved the proposed method provided enhancements.
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[33]. Rashad J. Rasras1, Mutaz Rasmi Abu Sara2, Ziad A. AlQadi3, Rushdi Abu zneit, Comparative Analysis of
LSB, LSB2, PVD Methods of Data Steganography, International Journal of Advanced Trends in Computer
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[37]. Musbah J. Aqel, Ziad ALQadi, Ammar Ahmed Abdullah, RGB Color Image Encryption-Decryption Using
Image Segmentation and Matrix Multiplication, International Journal of Engineering & Technology,
7(3.13) (2018) 104-107.https://doi.org/10.14419/ijet.v7i3.13.16334
[38]. Bilal Zahran, Ziad Alqadi, Jihad Nader, Ashraf Abu Ein,A COMPARISON BETWEEN PARALLEL
ANDSEGMENTATIONMETHODS USED FOR IMAGE ENCRYPTION-DECRYPTION International
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[39]. Khaled Matrouk, Abdullah Al- Hasanat, HaithamAlasha'ary, Ziad Al-Qadi, Hasan Al-Shalabi, Analysis of
Matrix Multiplication Computational Methods, European Journal of Scientific Research, ISSN 1450-216X
/ 1450-202X Vol.121 No.3, 2014, pp.258-266.
[40]. Ziad A.A. Alqadi, Musbah Aqel, and Ibrahiem M. M. ElEmary, Performance Analysis and Evaluation of
Parallel Matrix Multiplication Algorithms, World Applied Sciences Journal 5 (2): 211-214, 2008.
[41]. Z Alqadi, A Abu-Jazzar, Analysis of program methods used in optimizing matrix multiplication, Journal of
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[43]. J. Al-Azzeh, B. Zahran, Z. Alqadi, B. Ayyoub, M. Abu-Zaher, A Novel zero-error method to create a secret
tag for an image, Journal of Theoretical and Applied Information Technology, Vol. 96. No. 13, pp. 4081 -
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Color Image Steganography, JCSMC, Vol.5, Issue. 11, November 2016, pg.37–43.
[45]. M. Jose, “Hiding Image in Image Using LSB Insertion Method with Improved Security and Quality”,
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[52]. M. Abu-Faraj, and Z. Alqadi, “Image Encryption using Variable Length Blocks and Variable Length
Private Key,” International Journal of Computer Science and Mobile Computing (IJCSMC), vol. 11, Iss. 3,
pp. 138-151, 2022.
[53]. M. Abu-Faraj, A. Al-Hyari, and Z. Alqadi, “A Dual Approach for Audio Cryptography,” Journal of
Southwest Jiaotong University, vol. 57, no. 1, pp. 24-33, 2022.
[54]. M. Abu-Faraj, A. Al-Hyari, and Z. Alqadi, “Complex Matrix Private Key to Enhance the Security Level of
Image Cryptography,” Symmetry, vol. 14, Iss. 4, pp. 664-678, 2022.
[55]. M. Abu-Faraj, K. Aldebei, and Z. Alqadi, “Simple, Efficient, Highly Secure, and Multiple Pur- posed
Method on Data Cryptography,” Traitement du Signal, vol. 39, no. 1, pp. 173-178, 2022.
Prof. Ziad AlQadi, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.9, September- 2023, pg. 15-38
© 2023, IJCSMC All Rights Reserved 37
[56]. M. Abu-Faraj, Khaled Aldebe, and Z. Alqadi, “Deep Machine Learning to Enhance ANN Performance:
Fingerprint Classifier Case Study,” Journal of Southwest Jiaotong University, vol. 56, no. 6 , pp. 685-694,
2021.
[57]. M. Abu-Faraj, Z. Alqadi, and K. Aldebei, “Comparative Analysis of Fingerprint Features Ex- Traction
Methods,” Journal of Hunan University Natural Sciences, vol. 48, iss. 12, pp. 177-182, 2021.
[58]. M. Abu-Faraj, and Z. Alqadi, “Improving the Efficiency and Scalability of Standard Meth- ods for Data
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pp. 451-458, 2021.
[59]. Abdullah N. Olimat, Ali F. Al-Shawabkeh, Ziad A. Al-Qadi, Nijad A. Al-Najdawi, Forecasting the
influence of the guided flame on the combustibility of timber species using artificial intelligence, Case
Studies in Thermal Engineering, Volume 38, 2022, 102379, ISSN 2214-157X,
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[61]. M. Abu-Faraj, A. Al-Hyari, and Z. Alqadi, “A Dual Approach for Audio Cryptography,” Journal of
Southwest Jiaotong University, vol. 57, no. 1, pp. 24-33, 2022.
[62]. M. Abu-Faraj, A. Al-Hyari, and Z. Alqadi, “Complex Matrix Private Key to Enhance the Security Level of
Image Cryptography,” Symmetry, vol. 14, Iss. 4, pp. 664-678, 2022.
[63]. M. Abu-Faraj, K. Aldebei, and Z. Alqadi, “Simple, Efficient, Highly Secure, and Multiple Purr- posed
Method on Data Cryptography,” Traitement du Signal, vol. 39, no. 1, pp. 173-178, 2022.
[64]. M. Abu-Faraj, Khaled Aldebe, and Z. Alqadi, “Deep Machine Learning to Enhance ANN Performance:
Fingerprint Classifier Case Study,” Journal of Southwest Jiaotong University, vol. 56, no. 6 , pp. 685-694,
2021.
[65]. M. Abu-Faraj, and Z. Alqadi, “Improving the Efficiency and Scalability of Standard Meth- odds for Data
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pp. 451-458, 2021.
[66]. J. Vilkamo and T. Bäckström, “Time-Frequency Processing: Methods and Tools,” in Parametric Time-
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vol. 2, issue 4, pp. 288-298, 2007.
[69]. Jamil Al-Azzeh, Bilal Zahran, Ziad Alqadi, Belal Ayyoub, Muhammed Mesleh, A Novel Based On Image
Blocking Method to Encrypt-Decrypt Color, International Journal on Informatics Visualization, vol. 3,
issue 1, pp. 86-93, 2019.
[70]. Musbah J Aqel, Ziad ALQadi, Ammar Ahmed Abdullah, RGB Color Image Encryption-Decryption Using
Image Segmentation and Matrix Multiplication, International Journal of Engineering and Technology, vol.
7. Issue 3.13, pp. 104-107. 2018.
[71]. Jihad Nadir, Ashraf Abu Ein, Ziad Alqadi, A Technique to Encrypt-decrypt Stereo Wave File, International
Journal of Computer and Information Technology, vol. 5, issue 5, pp. 465-470, 2016.
[72]. Saleh Khawatreh, Belal Ayyoub, Ashraf Abu-Ein, Ziad Alqadi, A Novel Methodology to Extract Voice
Signal Features, International Journal of Computer Applications, vol. 975, pp. 8887, 2018.
[73]. Majed O. Al-Dwairi, Amjad Y. Hendi, Mohamed S. Soliman, Ziad A.A. Alqadi, A new method for voice
signal features creation, International Journal of Electrical and Computer Engineering (IJECE), vol. 9. Issue
9, pp. 4092-4098, 2019.
[74]. Aws Al-Qaisi, Saleh A Khawatreh, Ahmad A Sharadqah, Ziad A Alqadi, Wave File Features Extraction
Using Reduced LBP, International Journal of Electrical and Computer Engineering, vol. 8. Issue 5, pp.
2780-2787, 2018.
[75]. Ayman Al-Rawashdeh, Ziad Al-Qadi, using wave equation to extract digital signal features, Engineering,
Technology & Applied Science Research, vol. 8, issue 4, pp. 1356-1359, 2018.
[76]. Ashraf Abu-Ein, Ziad AA Alqadi, Jihad Nader, A TECHNIQUE OF HIDING SECRETE TEXT IN WAVE
FILE, International Journal of Computer Applications, 2016.
[77]. Ismail Shayeb, Ziad Alqadi, Jihad Nader, Analysis of digital voice features extraction methods,
International Journal of Educational Research and Development, vol. 1, issue 4, pp. 49-55, 2019.
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© 2023, IJCSMC All Rights Reserved 38
[78]. Jihad Nader Ahmad Sharadqh, Ziad Al-Qadi, Bilal Zahran, Experimental Investigation of Wave File
Compression-Decompression, International Journal of Computer Science and Information Security, vol.
14m issue 10, pp. 774-780, 2016.
[79]. Ziad A AlQadi Amjad Y Hindi, O Dwairi Majed, PROCEDURES FOR SPEECH RECOGNITION USING
LPC AND ANN, International Journal of Engineering Technology Research & Management, vol. 4, issue
2, pp. 48-55, 2020.
[80]. Majed O Al-Dwairi, A Hendi, Z AlQadi, an efficient and highly secure technique to encrypt-decrypt color
images, Engineering, Technology &Applied Science Research, vol. 9, issue 3, pp. 4165-4168, 2019.
[81]. Amjad Y Hendi, Majed O Dwairi, Ziad A Al-Qadi, Mohamed S Soliman, a novel simple and highly secure
method for data encryption-decryption, International Journal of Communication Networks and Information
Security, vol. 11, issue 1, pp, 232-238, 2019
[82]. Prof. Ziad Alqadi, Dr. Mohammad S. Khrisat, Dr. Amjad Hindi, Dr. Majed Omar Dwairi, USING SPEECH
SIGNAL HISTOGRAM TOCREATE SIGNAL FEATURES, International Journal of Engineering
Technology Research & Management, vol. 4, issue 3, pp. 144-153, 2020.
[83]. M. Abu-Faraj, Z. Alqadi, and K. Aldebei, “Comparative Analysis of Fingerprint Features Ex- Traction
Methods,” Journal of Hunan University Natural Sciences, vol. 48, iss. 12, pp. 177-182, 2021.
[84]. Alqadi, Z. (2019). A new method for voice signal features creation. International Journal of Electrical and
Computer Engineering (IJECE), 9(5): 4092-4098.https://doi.org/10.11591/ijece.v9i5.pp4092-4098.
[85]. Alqadi, Z. (2009). A practical approach of selecting the edge detector parameters to achieve a good edge
map of the gray image. Journal of Computer Science, 5(5): 355-362.
[86]. Zaini, H., Alqadi, Z.A. (2021). Efficient WPT based speech signal protection. IJCSMC, 10(9): 53-
65.https://doi.org/10.47760/ijcsmc.2021.v10i09.006.
[87]. Zneit, R.A., Khrisat, M.S., Khawatreh, S.A., Alqadi, Z.(2020). Two ways to improve WPT decomposition
used for image features extraction. European Journal of Scientific Research, 157(2): 195-205.
[88]. Hindi, A., Qaryouti, G.M., Eltous, Y., Abuzalata, M.,Alqadi, Z. (2020). Color image compression using
linear prediction coding. International Journal of Computer Science and Mobile Computing, 9(2): 13-20.
