A New Hybrid Steganography Scheme Employing Time-Varying Delayed.pptx

1. Karim H. Moussa
School of Internet of Things, Xi’an
Jiatong-Liverpool University, Suzhou,
China. karim.moussa@xjjtlu.edu.cn
Marwa H. Elsherif
Electrical Engineering Department,
Alexandria University Alexandria,
Egypt. marwaelsherif2@gmail.com

2. 1. Introduction to Steganography
2. Aim of Proposed Work
3. System Model
4. Experimental Results
5. Conclusions

3. STEGANOGRAPHY
 STEGAN-O-GRAPHY

4.  The goal of steganography is to hide
messages in such a way that no one
apart from the intended recipient
even knows that a message has been
sent.”

5. ADVANTAGE OF STEGANOGRAPHY
OVER CRYPTOGRAPHY
Messages do not attract
attention to themselves
Protects both messages
and communicating
parties

6. COMBINED CRYPTO-
STEGANOGRAPHY
Merge
Steganography Encryption

9.  Embedding secret messages
into digital sound is known
as Audio Steganography.

10.  GOALS OF HIDING TECHNIQUE
EFFECTIVE
ALGORITHM
TRANSPAREN
CY
CAPACITY
ROBUSTNES
S

11.  THE PROPOSED WORK
 To propose a new chaos-based
audio steganography method that
increases the embedding capacity
while maintaining high
imperceptibility and robustness,
employing chaotic neural networks
and wavelet transform.

12. •HAMMING CODES (7, 4)
The Hamming code is one of the
most well-known block code
methods that can do both error
detection and correction on a
block of data. Hamming Codes
are still widely used in computing,
telecommunication, and other
applications.

13. •HAMMING CODES(CONT.)

14. CHAOS AND RANDOM SYSTEM
Chaos is
statistically
indistinguishable
from
randomness,
and yet it is
deterministic
and not random
at all .

15. CHAOS IN NEURAL
NETWORK
 Artificial neural networks are an integral
part of emerging technologies, and
ongoing research has shown that they
can be applied to a variety of applications.
 This paper proposes a
new Steganographic algorithm using
chaotic neural networks, whose function
is enhanced by construction with
polynomials that exhibit chaos, namely,
nonlinear Hermite and Chebyshev
polynomials.

16. CHAOTIC NEURAL NETWORK
 Chaotic neural networks offer greatly
increase memory capacity.Each memory is
encoded by an Unstable Periodic Orbit
(UPO) on the chaotic attractor

17. The delayed feedback method is
considered to be best suited to
the control of chaos in neural
networks
CHAOTIC NEURAL NETWORK (CONT.)

18. .
A HOPFIELD NEURAL NETWORK
WHICH SHOWS CHAOTIC NATURE
IS PERFORMED AS FOLLOWS:

19.  THE PROPOSED STEPS FOR
EMBEDDING PROCESS

20.  EXTRACTION PROCESS

21. •SUBJECTIVE TEST
RESULTS

22. QUALITY TEST
RESULTS
Mean–structure similarity-index-measure (MSSIM) is an estimate for the deformity of
extracted image

23. AVERAGE PSNR VALUES OF
COVER AUDIO WITH
ATTACKS
53.65
40.1
34.12
55.65
60.11
44.6
Average PSNR of Cover Audio (dB)
Gaussian noise attack MP3 compression attack
Cropping attack Jittering Attack
Amp.Scaling Attack Echo Addition Attack

24. AVERAGE MSSIM VALUES
OF HIDDEN IMAGE WITH
ATTACKS
0.69 0.66
1
0.77
1
0.77
AVERAGE MSSIM OF HIDDEN IMAGE
Average MSSIM of Hidden Image
Gaussian noise attack MP3 compression attack Cropping attack
Jittering Attack Amp.Scaling Echo Addition

25.  HIDING CAPACITY AND MEAN PSNR OF THE
PROPOSED ALGORITHM AND SOME OTHER
RELATED WORKS

26. COMPARISON BETWEEN BER OF THE
PROPOSED ALGORITHM AND SOME
OTHER RELATED WORKS
Attack Type BER%
Proposed
algorithm
[1] [2] [6] [8] [9]
Gaussian Noise
(20 dB)
0.21 - 0.23 0.03 0.53 0.31
Amp. scaling
(1.2)
0 0.03 - 1.44 - -
MP3 Comp.
(64 kbps)
0.23 0.74 0.07 - 0.45 0.2

27. CONCLUSION

28. Performance improvement was
achieved by obtaining high hiding
capacity above 3000 kbps and
average quality of more than 60 dB.
The secret message is encoded by
applying Hamming code (7, 4)
before the embedding process to
make the message even more
secure.

29.  Using delayed chaotic neural
network makes it difficult for any
cryptanalyst to determine the actual
parameters of the hiding method as it
is difficult to synchronize the
unknown chaotic neural networks
unless you know the neural network
clearly so it enhanced the security of
the algorithm

30. REFERENCES
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33. [7] Banik BG, Bandyopadhyay S (2018)
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