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Creating hidden information, the lesson of presenting the analysis and analysis function of Steganography using wavelet transform image O communication for my composite and discrete alpha

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Parastoo Alaei Roozbahni at Islamic Azad University, Tehran
Parastoo Alaei Roozbahni
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Abstract

Steganography is a technology for secure information exchange. And there is no doubt that the image or sound of the video does not create any doubt Be the carrier is possible. Secretive techniques, hidden information behind Steganography produce the same image cover. Exaggeration of the presence of external observers will cause this matter to be hidden. A proposed alpha parameter is the scaling task. The cargo and the cover of the images and other types of the format are defined in advance Images and low, the web is a live of different images, the dimensions of and have been processed before A Discrete Wavelet Transform (DWT) is applied to each image's load and mask. Image is a production for Stego, the encrypted image payload combines the image cover with and becomes . As the result of PSNR, MSE and Entropy parameters, you measure.
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
Steganography

Parastoo Alaei Roozbahani

Abstract


Steganography

Steganography



DWT
Stego
PSNRMSEEntropy
Introduction
Introduction: SteganographySteganosStego
grafia







SteganographyCI
Steganography

Steganography

SteganographyCI

Stego
•Infographic Style
Introduction
Contribution & Organization:
AB
 :
Steganography


Haar DWT
.

Steganography
 .4
5

6
.
CI: Cover Image
PI: Payload Image
SI: Stego Image
HDWT: Haar Discrete Wavelet Transform
IDWT: Inverse Discrete Wavelet Transform
N1: Entropy of Stego Image
N2: Entropy of Cover Image
Literature Review
Literature Review:

CI



(PSO)

(AEMD )

 .



.




LSB 
.
Tang L et al:
Atta R et al:
Mansoor Fateh et al:
Aya Jaradat et al:

(LSB )
 .Payload

 .
CI 

Literature Review:


LSB

.


PVD.
GA


LSB CI
.
Pratik D Shah and
Rajankumar S Bichkar:
Nabanita Mukherjee
(Ganguly) et al:
J B Eseyin and K A
Gbolagade:
Supriadi Rustad
et al:


RNS
CRT

RSA
CI 
LSB
.





Cover Image:


Playload Image:
Haar DWTSteganography



CIPI



CIPI
αβ

Haar DWTSteganography


•Infographic Style
Proposed
Model

𝛽=(1- α)
SIStego𝘢
𝛽


DWT


DWT

High-Pass (H)
Low-Pass (L)





IDWT



Stego Image
Generation Algorithm:
αββ= (1-α)
01
CIx256PI
x512
02
(NCI) = CI/255
(NPI) = PI/255.
03
(PCI) = (NCI)* α
(PPI) = (NPI* β).
04
DWT HAARCIPI
LL CIPI
05
LL CIPI
Haar
06
CIStego (SI)
LLLHHLHHPI
CI
IDWT SI
07
Secret Image Extraction Algorithm:










β

D-DWT
HAAR SI


SI 
CI.
IDWT 

SI


Peak Signal to Noise Ratio (PSNR):
PSNR SI CI 
SI CI. 

Mean Square Error (MSE):
MSE 
SI CI 
Entropy(N):


αCIPIMSEPSNRN1N2
0.999baboonlena0.140165.20362.91632.9163
0.9baboonlena0.140165.20032.91632.9163
0.8baboonlena0.140265.19692.91672.9163
0.7baboonlena0.140265.19352.91532.9163
0.6baboonlena0.140365.19022.91522.9163
0.5baboonlena0.140365.18692.91662.9163
0.4baboonlena0.140465.18372.91492.9163
0.3baboonlena0.140465.18042.90412.9163
0.2baboonlena0.140565.17722.86792.9163
0.1baboonlena0.140665.17402.78432.9163
PSNRCIPIα
CIPI
x512α

α=0.1MSE
0.1406PSNR 65.1740
α=0.999MSEPSNR

PSNRCIPI
PSNRCI
PI
CI

α
PSNR
MSE 0.1401
Stego

αCI
Dimension
PI
Dimensio
nMSE
PSNR
N1 N2
0.999
peppers.pn
g512x512
zelda.png
512x512
0.7315
50.8465
airplane.pn
g512x512
barbara.pn
g
512x512
0.5447
53.4071
lena.png 512x512
goldhill.png
512x512
0.4163
55.7420
baboon.pn
g512x512
barbara.pn
g
512x512
0.1401
65.2036
PSNRCIPI
PSNR CI
PI

CI
α=0.999
PSNRMSE
0.2140Stego

αCI
Dimensi
on PI
Dimensi
on
MSE
PSNR
N1 N2
0.999
zelda.pn
g
512x51
2
peppers.
png
512x512
0.3035
58.487
8
3.109
7
3.10
97
goldhill.
png
512x512
peppers.
png
512x51
2
0.2179
61.365
9
3.055
0
3.05
61
goldhill.
png
512x512
lena.pn
g
512x51
2
0.2140
61.522
4
3.055
3
3.05
62
PSNRCIPI
PSNR
CIPI
rice.png
CItestpat1.png
PI
α=0.999
PSNR
MSE 0.1634
Stego

αImage
Dimension
Image
Dimensio
nMSE
PSNR
N1 N2
0.999
lena.jpg 256x256
baboon.jpg
128x128
0.2685
59.5527
3.0845
3.0845
circuit.tif
272x280
cameraman.
tif
256x256
0.2412
60.4819
3.1037
3.1003
goldhill.pn
g512x512 mri.tif
128x128
0.2179
61.3659
3.0554
3.0561
goldhill.pn
g512x512
baboon.jpg
256x256
0.2140
61.5224
3.0555
3.0562
rice.png
256x256
testpat1.pn
g
256x256
0.1634
63.8647
3.0765
3.0713
PSNRCIPI
PSNR
CIPI
peppers.pngCI
onion.pngPI
α=0.999
PSNR
MSE 0.1206Stego


αImage
Dimension
Image
Dimensi
on
MSE
PSNR
N1 N2
0.999
lena.png
512x512
Gantrycrane.
png
400x264
0.4163
55.742
03.07
13 3.07
13
kids.tif 318x400 onion.png
198x135
0.2218
61.212
23.15
55 3.15
13
fruits.png
512x512
peppers.png
512x384
0.1907
62.525
82.87
29 2.87
32
baboon.p
ng 512x512 trees.tif
350x258
0.1556
64.288
52.91
52 2.91
52
peppers.
png 512x384
onion.png
198x135
0.1206
66.502
52.50
15 2.50
15
PSNRCIPIWC
PSNR
CIPI
Webcam Image3 (wc3)
CIWebcam Image4
(wc4)PI
α=0.999PSNR
MSE 0.1479
Stego

𝛼CI PI MSE PSNR N1 N2
0.999
wc1
wc2
0.5914
52.6928
3.1806
3.1806
wc3
wc4
0.1479
64.7340
2.9867
2.9868
Simulation Results
Simulation result


 

MATLAB



Comparison of related work
PSNR


Authors PSNR
JinHa Hwang et al. [27] 48.40
Asmaa A E,et al [12] 40.46
Atta R,et al [7] 52.07
M Hassaballah,et al [14] 53.23
Aya Jaradat,et al [1] 63.25
Proposed 66.50

Conclusion
Steganography
Haar DWT
MATLAB
CI
Stego


PSNRStego



PSNRCIPI
BaboonCIPIBarbaraPSNR
MSE 0.1401PSNR CI
PIGoldhillCI
LenaPSNRMSE 0.2140
PSNRCIPICI
testpat1PIPSNRMSE 0.1632
PSNRCIPICI
PIPSNRMSE 0.1206PSNR
CIPIwc3 CI
wc4PIPSNRMSE 0.1479

Future Scope

Steganography





References:
[1] Aya Jaradat, Eyad Taqieddin, Moad Mowafi, "A High-Capacity Image Steganography Method Using Chaotic Particle Swarm
Optimization," Security and Communication Networks, vol 2021, Article ID 6679284, 2021.
[2] Mansoor Fateh, Mohsen Rezvani, Yasser Irani, "A New Method of Coding for Steganography Based on LSB Matching Revisited," Security
and Communication Networks, vol. 2021, Article ID 6610678, 2021.
[3] J B Eseyin and K A Gbolagade, "Data Hiding in Digital Image for Efficient Information Safety Based on Residue Number System,"
AJRCoS, vol. 8, no. 4, pp. 35-44, May 2021.
[4] Parameshachari, B. D., Panduranga, H. T., & liberata Ullo, S. (2020, September). Analysis and computation of encryption technique to
enhance security of medical images. In IOP Conference Series: Materials Science and Engineering (Vol. 925, No. 1, p. 012028). IOP Publishing.
[5] Supriadi Rustad, De Rosal Ignatius Moses Setiadi, Abdul Syukur, Pulung Nurtantio Andono, "Inverted LSB image steganography using
adaptive pattern to improve imperceptibility", Journal of King Saud University - Computer and Information Sciences, ISSN 1319-1578, 2021.
[6] Subramani, P., & BD, P. (2021). Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique
for COVID-19 and post-COVID-19 patients. Personal and ubiquitous computing, 1-14.
[7] Nabanita Mukherjee (Ganguly), Goutam Paul, Sanjoy Kumar Saha, "Two-point FFT-based high capacity image steganography using
calendar based message encoding," Information Sciences, Volume 552, ISSN 0020-0255, pp. 278-290,2021.
[8] Yu, K., Lin, L., Alazab, M., Tan, L., & Gu, B. (2020). Deep learning-based traffic safety solution for a mixture of autonomous and manual
vehicles in a 5G-enabled intelligent transportation system. IEEE transactions on intelligent transportation systems, 22(7), 4337-4347.
[9] Pratik D Shah, Rajankumar S Bichkar, "Secret data modification based image steganography technique using genetic algorithm having a
flexible chromosome structure," Engineering Science and Technology, an International Journal, Volume 24, Issue 3, pp. 782-794, ISSN 2215-
0986, 2021.
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A High-Capacity Image Steganography Method Using Chaotic Particle Swarm Optimization
Article
Full-text available
  • Jun 2021
Image steganography has been widely adopted to protect confidential data. Researchers have been seeking to improve the steganographic techniques in order to increase the embedding capacity while preserving the stego-image quality. In this paper, we propose a steganography method using particle swarm optimization and chaos theory aiming at finding the best pixel locations in the cover image to hide the secret data while maintaining the quality of the resultant stego-image. To enhance the embedding capacity, the host and secret images are divided into blocks and each block stores an appropriate amount of secret bits. Experimental results show that the proposed scheme outperforms existing methods in terms of the PSNR and SSIM image quality metrics.
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Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients
Article
Full-text available
  • Mar 2021
  • PERS UBIQUIT COMPUT
Many Coronavirus disease 2019 (COVID-19) and post-COVID-19 patients experience muscle fatigues. Early detection of muscle fatigue and muscular paralysis helps in the diagnosis, prediction, and prevention of COVID-19 and post-COVID-19 patients. Nowadays, the biomedical and clinical domains widely used the electromyography (EMG) signal due to its ability to differentiate various neuromuscular diseases. In general, nerves or muscles and the spinal cord influence numerous neuromuscular disorders. The clinical examination plays a major role in early finding and diagnosis of these diseases; this research study focused on the prediction of muscular paralysis using EMG signals. Machine learning–based diagnosis of the diseases has been widely used due to its efficiency and the hybrid feature extraction (FE) methods with deep learning classifier are used for the muscular paralysis disease prediction. The discrete wavelet transform (DWT) method is applied to decompose the EMG signal and reduce feature degradation. The proposed hybrid FE method consists of Yule-Walker, Burg’s method, Renyi entropy, mean absolute value, min-max voltage FE, and other 17 conventional features for prediction of muscular paralysis disease. The hybrid FE method has the advantage of extract the relevant features from the signals and the Relief-F feature selection (FS) method is applied to select the optimal relevant feature for the deep learning classifier. The University of California, Irvine (UCI), EMG-Lower Limb Dataset is used to determine the performance of the proposed classifier. The evaluation shows that the proposed hybrid FE method achieved 88% of precision, while the existing neural network (NN) achieved 65% of precision and the support vector machine (SVM) achieved 35% of precision on whole EMG signal.
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A New Method of Coding for Steganography Based on LSB Matching Revisited
Article
Full-text available
  • Feb 2021
LSB matching revisited is an LSB-based approach for image steganography. This method is a type of coding to increase the capacity of steganography. In this method, two bits of the secret message are hidden in two pixels with only one change. But this method provides no idea for hiding a message with a large number of bits. In other words, this method works only for n=2, where n is the number of bits in a block of the secret message. In this paper, we propose an improved version of the LSB matching revisited approach, which works for n>2. The proposed scheme contains two phases including embedding and extracting the message. In the embedding phase, we first convert the secret message into a bit-stream, and then the bit-stream is divided into a set of blocks including n bits in each block. Then we choose 2n−1 pixels for hiding such n bits of the secret message. In the next step, we choose the operations needed to generate such a message. Finally, we perform the obtained operations over the coefficients to hide the secret message. The proposed approach needs fewer changes than LSB MR when n>2. The capacity of the proposed approach is 2n−1/2n−1−1×100% higher than the F5 method where this value for n>2 is bigger than 75%. For example, the capacity of our scheme is 75% higher than the capacity of F5 for n=3. The proposed method can be used in the first step of every steganography method to reduce the change in the stego image. Therefore, this method is a new coding method for steganography. Our experimental results using steganalysis show that using our method provides around 10% higher detection error for SRNet over two steganography schemes.
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Secret data modification based image steganography technique using genetic algorithm having a flexible chromosome structure
Article
Full-text available
  • Jan 2021
In all forms of confidential communication, the most significant element is security. Cryptography can be used to secure the information, but it discloses the presence of covert communication. Hence, steganography was invented; steganography is an art of concealed communication. Steganography is performed by inserting the secret information in a cover media to camouflage the presence of covert communication. In image steganography, an image is used as the cover media for secure communication. There are various image steganography techniques invented by many researchers, but a small number of them focus on improving visual quality and increasing payload capacity simultaneously. This paper presents a secret data modification based high capacity image steganography technique using genetic algorithm (GA). This new technique uses the least significant bit (LSB) replacement steganography, to embed the secret data. However, the secret data is rearranged and modified before embedding it in the LSBs of the cover image. The parameters used to rearrange and modify the secret data are controlled by GA. A unique concept called flexible chromosome is introduced which allows GA to interpret the chromosome value in different ways. GA attempts to find the best possible parameter value that yields high visual quality of the stego images. The stego images produced by the proposed technique have an average PSNR value of 46.41 dB and 40.83 dB for 2 bit per pixel (bpp) and 3 bpp data hiding capacity respectively.
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Inverted LSB Image Steganography using Adaptive Pattern to Improve Imperceptibility
Article
Full-text available
  • Jan 2021
Various image steganography methods have been developed to improve the stego image quality, i.e. imperceptibility, capacity, and security. This research proposes an adaptive method that can select the most optimal pattern to minimize the error ratio due to message embedding. This adaptive pattern can optimize the performance of the inverted LSB substitution method, based on the two-bit + least-significant-bit (LSB) pattern in the container image. Before embedding, the message and container image bits are tested and the error ratio is calculated using the inverted LSB substitution method for various possible patterns. The pattern that has the least error rate is selected to embed the message. The use of this adaptive pattern in inverted LSB image steganography significantly increases imperceptibility. Based on the test results and by comparing it to previous studies, it is found increased results, the PSNR value ranges from 52.49 to 57.45, and the SSIM ranges from 0.9991 to 0.9999 with 1 BPP capacity.
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Data Hiding in Digital Image for Efficient Information Safety Based on Residue Number System
Article
  • May 2021
The mass dispersal of digital communication requires the special measures of safety. The need for safe communication is greater than ever before, with computer networks now managing almost all of our business and personal affairs. Information security has become a major concern in our digital lives. The creation of new transmission technologies forces a specific protection mechanisms strategy particularly in data communication state. We proposed a steganography method in this paper, which reads the message, converting it into its Residue Number System equivalent using the Chinese Remainder Theorem (CRT), encrypting it using the Rivest Shamir Adleman (RSA) algorithm before embedding it in a digital image using the Least Significant Bit algorithm of steganography and then transmitting it through to the appropriate destination and from which the information required to reconstruct the original message is extracted. These techniques will enhance the ability to hide data and the hiding of ciphers in steganographic image and the implementation of CRT will make the device more efficient and stronger. It reduces complexity problems and improved execution speed and reduced the time taken for processing the encryption and embedding competencies.
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Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System
Article
  • Dec 2020
It is expected that a mixture of autonomous and manual vehicles will persist as a part of the intelligent transportation system (ITS) for many decades. Thus, addressing the safety issues arising from this mix of autonomous and manual vehicles before autonomous vehicles are entirely popularized is crucial. As the ITS system has increased in complexity, autonomous vehicles exhibit problems such as a low intention recognition rate and poor real-time performance when predicting the driving direction; these problems seriously affect the safety and comfort of mixed traffic systems. Therefore, the ability of autonomous vehicles to predict the driving direction in real time according to the surrounding traffic environment must be improved and researchers must work to create a more mature ITS. In this paper, we propose a deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5G-enabled ITS. In this scheme, a driving trajectory dataset and a natural-driving dataset are employed as the network inputs to long-term memory networks in the 5G-enabled ITS: the probability matrix of each intention is calculated by the softmax function. Then, the final intention probability is obtained by fusing the mean rule in the decision layer. Experimental results show that the proposed scheme achieves intention recognition rates of 91.58% and 90.88% for left and right lane changes, respectively, effectively improving both accuracy and real-time intention recognition and improving the lane change problem in a mixed traffic environment.
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Analysis and computation of encryption technique to enhance security of medical images
  • Sep 2020
  • 12028
  • B D Parameshachari
  • H T Panduranga
  • S Ullo
Parameshachari, B. D., Panduranga, H. T., & liberata Ullo, S. (2020, September). Analysis and computation of encryption technique to enhance security of medical images. In IOP Conference Series: Materials Science and Engineering (Vol. 925, No. 1, p. 012028). IOP Publishing.