Integrating Convolutional Neural Networks with Phase Coding for Robust Audio Steganography

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 10

Year of Publication : 2025

Authors : Thuraka Srinivasa Padmaja, Shaik Mahaboob Basha

10.14445/23488549/IJECE-V12I10P102

How to Cite?

Thuraka Srinivasa Padmaja, Shaik Mahaboob Basha, "Integrating Convolutional Neural Networks with Phase Coding for Robust Audio Steganography," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 10, pp. 9-14, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I10P102

Abstract:

Audio steganography is a crucial technique for secure communication, as it enables the covert insertion of data into audio waves. In order to increase robustness and imperceptibility, this study looks at two important approaches: the conventional phase coding method and a novel approach that combines phase coding with Convolutional Neural Networks (CNN). The study assesses several techniques for conversational, musical, and instrumental audio signals. Among the key performance metrics used are embedding latency, Bit Error Rate (BER), payload capacity, Mean Opinion Score (MOS), and PSNR. Architecture diagrams for both approaches are presented along with comprehensive experimental results, a comparative analysis, and an explanation of the practical implications. The significant improvements in robustness and imperceptibility demonstrate CNN-enhanced phase coding’s potential in modern secure audio communications.

Keywords:

Phase coding, Audio steganography, Convolutional Neural Networks, Secure communication, Signal processing.

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