An improved speech steganalysis based on VoIP using deep learning approach

Document Type : -

Authors

1 PhD student, Malek Ashtar University of Technology, Tehran, Iran

2 Associate Professor, Malek Ashtar University of Technology, Tehran, Iran

Abstract

Today, Voice over Internet Protocol (VoIP) is widely used in real-time communication and social networks and has become a suitable carrier for steganography methods. To confronting these threats, many steganalysis methods have been invented, among the proposed solutions, the combination of signal processing and machine learning methods has made it possible to create steganalysis methods with high accuracy. In this paper, a combined approach of speech signal processing methods and artificial intelligence algorithms is used. In this research, first, data pre-processing is done on compressed audio signal with G.729 codec, which extracts intra-frame features and inter-frame correlations with good resolution. Then the obtained results are given to a deep learning network to train cover data from stego data. The results of the implementation include the improvement in both the detection accuracy and the computation time. This method has been analyzed for two important steganography families, QIM and PMS, and the proposed method has been tested and implemented for different embedding rates. Another important point is the real-time test of the presented method, which for 1000 millisecond files, the response time was less than 5 millisecond, which shows the high speed of the proposed model in the execution phase.

Keywords

Main Subjects

Smiley face

References

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Journal of Advanced Defense Science & Technology
Volume 14, Issue 2 - Serial Number 52
September 2024
Pages 101-111

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History

  • Receive Date: 31 May 2023
  • Revise Date: 03 July 2023
  • Accept Date: 14 August 2023

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