A Novel Audio Forgery Detection Method Based on Short-time Power Spectral Density

The popularity of portable recording equipment has made the acquisition of digital audio convenient, and the development of multimedia editing software has made audio editing and modification progressively easier. Therefore, it is essential to ensure the authenticity and integrity of digital media files. Copy-move forgery is a prevalent form of audio tampering, but existing detection methods suffer from low accuracy and poor robustness. To address this issue, we propose a detection method for audio copy-move forgery based on short-time power spectral density (STPSD). Specifically, we first separate the syllables in the voiced part of the speech signal. Then, the STPSD features are extracted from the suspect byte pair. Finally, the content matching algorithm is used for detection. The experimental results show that the proposed algorithm can resist all kinds of common attacks reliably, and can accurately locate copied and moved forged syllables, with high efficiency.

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Publication Details

Published
2024-10-19
DOI
https://doi.org/10.1109/iccsnt62291.2024.10776625
Primary Topic
Digital Media Forensic Detection
Type
article
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article

A Novel Audio Forgery Detection Method Based on Short-time Power Spectral Density

Xiaolong Li, Jiayi Cheng
Digital Media Forensic Detection
article

A Novel Audio Forgery Detection Method Based on Short-time Power Spectral Density

Xiaolong Li, Jiayi Cheng
article en

Abstract

The popularity of portable recording equipment has made the acquisition of digital audio convenient, and the development of multimedia editing software has made audio editing and modification progressively easier. Therefore, it is essential to ensure the authenticity and integrity of digital media files. Copy-move forgery is a prevalent form of audio tampering, but existing detection methods suffer from low accuracy and poor robustness. To address this issue, we propose a detection method for audio copy-move forgery based on short-time power spectral density (STPSD). Specifically, we first separate the syllables in the voiced part of the speech signal. Then, the STPSD features are extracted from the suspect byte pair. Finally, the content matching algorithm is used for detection. The experimental results show that the proposed algorithm can resist all kinds of common attacks reliably, and can accurately locate copied and moved forged syllables, with high efficiency.

Beijing Jiaotong University (CN)
Openalex Percentile: Top 78%
Digital Media Forensic Detection
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