TY - GEN
T1 - A New Hybrid Steganography Scheme Employing A Time-Varying Delayed Chaotic Neural Network
AU - Moussa, Karim H.
AU - Elsherif, Marwa H.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - A secure hybrid audio steganography algorithm using Discrete Wavelet Transform (DWT) and Hopfield Chaotic Neural Network is presented in this paper. An uncompressed audio file is used as a cover medium and a greyscale image is used as secret data. The pixels of the secret image are reordered using cyclic shifting to increase the system security, then the permutated pixels are encoded by applying Hamming code (7,4) before embedding them in the DWT coefficients of the stereo audio signal. The chaotic neural network is applied here to generate a random sequence to choose the embedding locations of hidden image pixels. Regarding the system's quality, the Peak Signal to Noise Ratio of stego-audio files is above 60 dB, which is close to the original audio quality. Furthermore, the algorithm has an improved embedding payload than previously proposed algorithms and high-security performance, as proved by the results obtained.
AB - A secure hybrid audio steganography algorithm using Discrete Wavelet Transform (DWT) and Hopfield Chaotic Neural Network is presented in this paper. An uncompressed audio file is used as a cover medium and a greyscale image is used as secret data. The pixels of the secret image are reordered using cyclic shifting to increase the system security, then the permutated pixels are encoded by applying Hamming code (7,4) before embedding them in the DWT coefficients of the stereo audio signal. The chaotic neural network is applied here to generate a random sequence to choose the embedding locations of hidden image pixels. Regarding the system's quality, the Peak Signal to Noise Ratio of stego-audio files is above 60 dB, which is close to the original audio quality. Furthermore, the algorithm has an improved embedding payload than previously proposed algorithms and high-security performance, as proved by the results obtained.
KW - Audio Steganography
KW - Cryptography
KW - Discrete Wavelet Transform
KW - Hamming Code
KW - Hopfield Chaotic Neural Network
KW - Human Auditory System
UR - http://www.scopus.com/inward/record.url?scp=85153672625&partnerID=8YFLogxK
U2 - 10.1109/CyberC55534.2022.00032
DO - 10.1109/CyberC55534.2022.00032
M3 - Conference Proceeding
AN - SCOPUS:85153672625
T3 - Proceedings - 2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2022
SP - 152
EP - 157
BT - Proceedings - 2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2022
Y2 - 15 December 2022 through 16 December 2022
ER -