Chaotic neural network based MPEG-2 video encryption framework over wireless channel

The increasing demand for retrieving secure and high quality of multimedia service applications corresponding to available bandwidth channel proposes new challenges for system engineering designers to implement efficient and optimum solution ideas. In this thesis, chaos theory property is combine...

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Main Author:	Tariq Adnan, Fadil
Format:	Thesis
Language:	English
Subjects:	Wireless network Chaotic Neural Network (CNN) High quality Multimedia service Cipher cryptography algorithm Video encryption Video compression Chaos
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59420/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59420/2/Full%20text.pdf
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id	my-unimap-59420
record_format	uketd_dc
spelling	my-unimap-594202019-04-10T03:47:24Z Chaotic neural network based MPEG-2 video encryption framework over wireless channel Tariq Adnan, Fadil Dr. Shahrul Nizam Yaakob The increasing demand for retrieving secure and high quality of multimedia service applications corresponding to available bandwidth channel proposes new challenges for system engineering designers to implement efficient and optimum solution ideas. In this thesis, chaos theory property is combined with artificial neural network to construct a cipher cryptography algorithm called a Chaotic Neural Network (CNN). The proposed system model framework is developed and modelled by embedding CNN inside video codec model to produce a secure and a compress bitstream. The proposed video codec model is designed and implemented based on MPEG-2 standard. The resultant video signal bitstream is transmitted from source to destination by using Orthogonal Frequency Division Multiplexing (OFDM) modulation technique. The size of tested input video signal is 176 × 144 (QCIF standard format). The video sequence frames is divided into sets of 30, 15, 10, and 5 frames which are fed to the framework model. The first frame (I-Frame) for each Group of Pictures (GOP) is compressed as still image (i.e. by using DCT transform, Quantization, Zig-Zag scan, and Huffman entropy coding), while other frames are compressed by using motion estimation and compensation algorithm then encoded like (I-Frame). Three Step Search algorithm (TSS) is used as motion estimation and compensation algorithm in this thesis. Weights and biases of CNN algorithm are set based on binary sequence generated from the chaotic logistic map for each iterate. Control parameter and initial value of chaotic logistic map are used as secret keys of the cipher algorithm. CNN is used to encrypt/decrypt both of motion and quantized data vectors of video codec model. CNN algorithm shows high sensitivity behavior for both key and plaintext modification with low PSNR value of -18.363 dB and high entropy value of 7.833. OFDM model performance is investigated and simulated over AWGN and 2-path frequency selective Rayleigh fading channel. Mathematical formulation expression is given and software programming code implementation is written by using MATLAB to simulate and test the overall system model framework. The proposed system model framework has the ability to control the required video quality value factor, bit rate, frames arrangement, and GOP number. Results indicate that the transmitted bitstream has been protected from known plaintext attack. Perceptual encryption feature was satisfied and applied successfully. Finally, subjective and objective measurement metrics are used to verify the performance of overall system model framework. Universiti Malaysia Perlis (UniMAP) 2014 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/59420 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59420/1/Page%201-24.pdf 652df6d7f2decbbabe06d517ef61ba2a http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59420/2/Full%20text.pdf bb57777da4db56c69dd4f9084f4d6bee http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59420/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Wireless network Chaotic Neural Network (CNN) High quality Multimedia service Cipher cryptography algorithm Video encryption Video compression Chaos School of Computer and Communication Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Dr. Shahrul Nizam Yaakob
topic	Wireless network Chaotic Neural Network (CNN) High quality Multimedia service Cipher cryptography algorithm Video encryption Video compression Chaos
spellingShingle	Wireless network Chaotic Neural Network (CNN) High quality Multimedia service Cipher cryptography algorithm Video encryption Video compression Chaos Tariq Adnan, Fadil Chaotic neural network based MPEG-2 video encryption framework over wireless channel
description	The increasing demand for retrieving secure and high quality of multimedia service applications corresponding to available bandwidth channel proposes new challenges for system engineering designers to implement efficient and optimum solution ideas. In this thesis, chaos theory property is combined with artificial neural network to construct a cipher cryptography algorithm called a Chaotic Neural Network (CNN). The proposed system model framework is developed and modelled by embedding CNN inside video codec model to produce a secure and a compress bitstream. The proposed video codec model is designed and implemented based on MPEG-2 standard. The resultant video signal bitstream is transmitted from source to destination by using Orthogonal Frequency Division Multiplexing (OFDM) modulation technique. The size of tested input video signal is 176 × 144 (QCIF standard format). The video sequence frames is divided into sets of 30, 15, 10, and 5 frames which are fed to the framework model. The first frame (I-Frame) for each Group of Pictures (GOP) is compressed as still image (i.e. by using DCT transform, Quantization, Zig-Zag scan, and Huffman entropy coding), while other frames are compressed by using motion estimation and compensation algorithm then encoded like (I-Frame). Three Step Search algorithm (TSS) is used as motion estimation and compensation algorithm in this thesis. Weights and biases of CNN algorithm are set based on binary sequence generated from the chaotic logistic map for each iterate. Control parameter and initial value of chaotic logistic map are used as secret keys of the cipher algorithm. CNN is used to encrypt/decrypt both of motion and quantized data vectors of video codec model. CNN algorithm shows high sensitivity behavior for both key and plaintext modification with low PSNR value of -18.363 dB and high entropy value of 7.833. OFDM model performance is investigated and simulated over AWGN and 2-path frequency selective Rayleigh fading channel. Mathematical formulation expression is given and software programming code implementation is written by using MATLAB to simulate and test the overall system model framework. The proposed system model framework has the ability to control the required video quality value factor, bit rate, frames arrangement, and GOP number. Results indicate that the transmitted bitstream has been protected from known plaintext attack. Perceptual encryption feature was satisfied and applied successfully. Finally, subjective and objective measurement metrics are used to verify the performance of overall system model framework.
format	Thesis
author	Tariq Adnan, Fadil
author_facet	Tariq Adnan, Fadil
author_sort	Tariq Adnan, Fadil
title	Chaotic neural network based MPEG-2 video encryption framework over wireless channel
title_short	Chaotic neural network based MPEG-2 video encryption framework over wireless channel
title_full	Chaotic neural network based MPEG-2 video encryption framework over wireless channel
title_fullStr	Chaotic neural network based MPEG-2 video encryption framework over wireless channel
title_full_unstemmed	Chaotic neural network based MPEG-2 video encryption framework over wireless channel
title_sort	chaotic neural network based mpeg-2 video encryption framework over wireless channel
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Computer and Communication Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59420/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59420/2/Full%20text.pdf
_version_	1747836835428564992

Chaotic neural network based MPEG-2 video encryption framework over wireless channel

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