The document summarizes an efficient image steganography method using multiobjective differential evolution. It begins with an introduction to steganography, watermarking and cryptography. It then discusses the least significant bit substitution steganography method and differential evolution optimization technique. The proposed method embeds a secret image into a cover image by using differential evolution to optimize the mask assignment process in the least significant bit substitution. Experimental results show the proposed method achieves better peak signal-to-noise ratio, structural similarity index and bit error rate than existing steganography methods, demonstrating better stegoimage quality and robustness against noise attacks.

1. An efficient image steganography
method using multiobjective
differential evolution
Digital media steganography(Fourth Season)
Providers :
Sima Abolhasani &
AydaMohammadi

2. Outline
Introduction
Literature review
Background
LSB substitution method
Differential evolution
The proposed method
Embedding process
Extraction process
Experimental results
Peak signal-to-noise ratio
Structural similarity index measure
Bit error rate
Conclusion
Refrences
2

3. Information hiding methods are decomposed into two
categories such as watermarking and steganography.
Introduction
Digital information includes text documents, digital
images, videos, and audio signals. The most popular
among them are cryptography, steganography, and
watermarking.
Cryptography protects themeaningful information
from attackers by converting it into unreadable form.
Cryptographic methods involve encryption, decryption,
and keys to make secure communication.
Watermarking is used to protect the integrity of
private information.
steganography is an another most popular method to
protect or hide the information.
It is also known as cover writing since it conceals the
presence of potential information inside an image,
audio, video, and text file.
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4. Introduction
1. General model of cryptography
1 2
3
Encryption algorithms are
used to convert the plain
image into a cipher image
using secret keys.
The decryption algorithms
are used to retrieve the plain
image using the same secret
keys.
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5. 2.General model of image steganography.
The image that hides thesecret message is known as a “cover image”.
The procedure used to hide the secret message inside the cover image is known as an
“embedding method”.
The use of stego-key is optional and depends on the embedding method.
The final output of embedding process is a “stegoimage”, which hides the secret message.
3
4
1
2
5

6. In this chapter:
The reason to choose
differential evolution is its
good convergence speed and
lesser sticking in local optima
as compared to other
metaheuristic algorithms.
Propose a steganography
method based on the least
significant substitution(LSB)
method and differential
evolution.
Use differential evolution to
optimize the mask
assignment process.
1 3
2 4
In the LSB method the
process of mask assignment
for embedding a secret image
into a cover image is a
tedious task.
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7. Literature review
3.Types of image steganography methods.
Image steganography
Secret data Format
Image Coded Format
RAw ( BMP , PNG )
Compressed
( JPEG 2000)
Encrypted
(AES based image)
Plain/RAW
Compressed
Encrypted
Compressed+Encrypted
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8. Literature review
Zhang et al
Main Context Year
Auther
Zhou et al
Brandao and
Jorge
Wu et al
Zhang et al
Sarreshtedari
and Akhaee
Proposed a steganography method based on joint distortion capacity for binary images. 2016
Studied the effect of noise on optical steganography. 2016
2016
2016
2016
2016
Proposed an attacking method to extract the secret information and detect the stegoimages.
Presented an image steganography method using artificial neural networks.
Proposed a coverless image steganography to resist the steganalysis.
Proposed a method known as one–third least significant bits embedding steganography
8

9. Zhang et al
Main Context Year
Auther
Rajput et al
Guo et al
Hu et al
Wu and
Wang
Zhang et al
Proposed a steganography method based on joint distortion capacity for binary images. 2016
Used generative adversarial networks to propose the steganography method without embedding. 2016
2016
2016
2016
2016
Implemented the data-hiding algorithm that uses secret image-sharing and
steganography methods.
Proposed a steganography method using a modification of uniform embedding.
Used reversible texture synthesis to implement the steganography.
Studied nonadditive distortion steganography using joint distortion.
Literature review
9

10. Background
LSB substitution method
Differential evolution:
1.
2.
Population initialization
Mutation
Recombination
Selection
Stopping criteria:
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11. LSB substitution method
Least significant bit (LSB): is a very easy and simple method to hide the
secret information in a cover image.
In LSB steganography the least significant bits of cover imageare used to
hide the secret image.
4.Framework of least significant bit method
1
2
11

12. Differential evolution process
5.Differential evolution process.
Differential evolution (DE) provides
optimized solutions of the problems.
provides better convergence as compared
to other evolutionary algorithms.
can be implemented parallelly to
manage the computationally intensive
objective functions.
DE consists of the following steps to
obtain the optimal solution.
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13. Differential
evolution
02 Mutation
03 Recombination
04 Selection
05 Stopping criteria
The initial population is randomly generated.
A donor solution is developed from three randomly
selected solutions.
Elements of solutions are combined to generate a trial
solution. It combines the elements of donor solution
obtained through mutation and target solution.
The best solution is selected on the basis of the fitness
function.
Population
initialization
01
The process of differential evolution is stopped on the basis of
some criteria such as the maximum number of generations,
acceptance error, number of fitness function evaluation.
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14. The proposed method
Embedding process
Extraction process
1.
2.
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15. Embedding process
05
04
02
A mask
assignment
number
presented reveals
the outcome.
01 03
This outcome is
represented with
a solution in DE.
Each aspect in a
solution presents
one assignment
of secret image to
be inserted into
the cover image.
For the n-mask of the
secret image, every
solution includes n
proportions
equivalent to n
masks.
Every is seen
just one time in
an assignment
list.
6. 8-Mask secret image with 16-mask cover image.
15

16. Embedding process
Decompose H and S into various s of size L.
Therefore the various s for H and S are
computed as:
7.Construction of stegoimage from the mask
assignment list.
1
Get full LSBs of the cover image and keep
them in array H.
2 Change the secret image to binary sequence
and keep in array S.
3
4 DE is then utilized to tune the allocation
list for embedding a secret image into the
cover image.
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17. Embedding process
8.Optimization of mask assignment list using differential evolution.
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18. In greedy selection, based on the comparison of the
sum of different bitsD, a new position is produced by
selecting each dimension from the assignment list of
old and neighboring solutions.
9.Exchanging information using greedy selection method.
The difference is evaluated as:
Embedding
process
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19. Extraction process
Extraction of the embedded image is performed.
E
I
T
F
Initially, embedded image and hyperparameters obtained using the
differential evolution process are taken for extraction process.
Thereafter full LSB of the embedded image is obtained. A binary
sequence from the LSB is then obtained.
Finally, this binary sequence is converted to its actual form, that is, a
secret image.
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20. Experimental
results
1.Peak signal-to-noise ratio
2.Structural similarity index measure
3.Bit error rate
20

21. Experimental
results
Various experiments are carried out to test the effectiveness of
the proposed method.
The proposed method is implemented in simulation
environment using MATLAB® 2017a.
To test the proposed method, well-known benchmark images
are used with size of 256 ×256.
The proposed method is compared with the competitive
steganography methods:
Stirling transform-based image steganography (STS)
genetic algorithm-based image steganography (GAS)
modified logistic chaotic map-based image steganography (MLCM)
particle swarm optimization-based image steganography (PSOS)
1.
2.
3.
4.
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22. 10.Visual analysis of the proposed method. Visual analysis of benchmark gray and color images: (A) input
gray image, (B) embedded gray image, (C) stego input, (D) extracted image, (E) cover color image, (F)
embedded color image, (G) stego input, and (H) extracted imag
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23. 5.1 Peak signal-to-noise ratio
a.To quantitatively evaluate the visual quality of cover images of the proposedmethod,
peak signal-to-noise ratio (PSNR) [38] metric is evaluated.
b.PSNR is evaluated between stegoimage and cover image to assess the image quality as
follows:
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24. 24

25. 5.2 Structural
similarity
index measure
11.Structural similarity index analysis for gray images.
Structural similarity index measure (SSIM) is used
evaluate the perceptual difference between cover
and stego images.
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26. 12. Structural similarity index analysis for color images.FIGURE
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27. During transmission, the stegoimages may get infected from some type of noises
Thereforeit is necessary to check robustness of the proposed method against
distortion tolerance.
5.3 Bit error rate
13.Distortion analysis. Comparative analysis of extracted
images from noisy stegoimages using methods
(A) STS, (B) GAS, (C) MLCM, (D) PSOS, and (E) Proposed.
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28. 6.Conclusion
Steganography method using LSB
method and differential evolution.
Differential evolution is used to
optimize the mask assignment of LSB
method.
Using an optimized mask assignment
to hide the secret image into cover
image.
The experimental result analysis
shows that the proposed method has
better PSNR, SSIM, and BER than the
existing steganography methods.
It implies that it has a better
stegoimage quality, robustness against
noise attacks, and payload capacity.
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Refrences
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30. THANK
you