A hybrid chaos and neural network cipher encryption algorithm for compressed video signal transmission over wireless channel

Abstract

In this paper, a hybrid framework of compressed and encrypted video signal transmission over wireless channel has been designed and implemented. Chaos theory property has been combined with artificial neural network to construct a cipher algorithm called a Chaotic Neural Network (CNN). This algorithm has been embedded and integrated inside MPEG-2 video codec standard to transform the plaintext (compressed video data) into an unintelligible form. The resultant compressed and encrypted bitstream has been transmitted from source to destination by using Orthogonal Frequency Division Multiplexing (OFDM) modulation technique. The effect of wireless channel condition has been investigated for both AWGN and Rayleigh fading channel. A video signal sample of size 176 × 144 (QCIF standard format) with rate of 30 frame per second has been used for test and simulate the overall system model framework performance. MATLAB software package has been used for system model framework implementation. The proposed framework is flexible and has ability to control output video quality, bit rate, and group of picture (GOP) number and their arrangement. Subjective and objective measurements have been used for overall system model performance evaluation. Results indicate high sensitivity behavior for both key and plaintext modification with high entropy result value.