Memristive Coupled Neural Network Based Audio Signal Encryption

This paper proposes a multi-layer audio encryption scheme based on the Memristive Coupled Neural Network (MCNN), S-box, and the Fibonacci Q-matrix. Initially, a pseudo-random key is generated using the MCNN system and XORed with the original audio data. Subsequently, an S-box, created using the Open...

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Published in2024 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) pp. 149 - 154
Main Authors Hamed, Farah, Gabr, Mohamed, Mamdouh, Eyad, Aboshousha, Amr, Alexan, Wassim, El-Damak, Dina, Fathy, Abdallah, Mansour, Marvy Badr Monir
Format Conference Proceeding
LanguageEnglish
Published Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT) 25.09.2024
Subjects
Audio signal encryption
chaos theory
Conductivity
Correlation coefficient
Encryption
Generators
Information entropy
Measurement
neural network
Neural networks
PSNR
Resistance
Signal processing algorithms
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Summary:This paper proposes a multi-layer audio encryption scheme based on the Memristive Coupled Neural Network (MCNN), S-box, and the Fibonacci Q-matrix. Initially, a pseudo-random key is generated using the MCNN system and XORed with the original audio data. Subsequently, an S-box, created using the OpenSSL Pseudo-random Number Generator (PRNG), is applied to the cipher. Finally, the Fibonacci Q-matrix is used to produce the final encrypted audio. The proposed scheme was evaluated using various metrics, including Peak Signal-to-Noise Ratio (PSNR), Number of Sample Change Rate (NSCR), correlation coefficient, and information entropy. The results demonstrate excellent performance and robust resistance to multiple types of attacks. Additionally, the scheme features a vast key space of 2 2126 , showcasing significant resistivity to brute-force attacks.
ISSN:2326-0319
DOI:10.23919/SPA61993.2024.10715600