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Mel spectrogram-based audio forgery detection using CNN

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Abstract

In this time of technology, digital speech can be created and falsified by a very diverse of hardware and software technologies. Audio copy-move forgery is an audio forgery technique that goals to create forged audio by hiding undesirable words or repeating wanted words in identical speech. Therefore, audio authentication has been a necessary requisition. In this study, an effective approach to spectral images based on audio copy-move forgery detection using convolutional neural networks (CNN) with data augmentation is proposed. There are only a few handcrafted methods conducted for the detection of audio copy-move forgery. None of the existing works on audio copy-move forgery detection has proposed deep feature learning from speech recording with Mel spectrogram. This is the first method to employ deep learning with Mel spectrogram of audio for the detection of audio copy-move forgery. The proposed CNN architecture classifies the suspicious Mel spectrogram images into two classes: original and forged. The proposed CNN system is successfully trained on these Mel spectrogram image feature extraction. The proposed algorithm has been tested on our datasets generated from Arabic Speech Corpus and TIMIT speech database. The results show the effectiveness, robustness of post-processing operations, and high accuracy of the proposed approach compared to other studies.

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Acknowledgements

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Funding

This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with Project No: 122E013.

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Authors and Affiliations

  1. Department of Computer Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

    Beste Ustubioglu & Guzin Ulutas

  2. Department of Management Information Systems, Trabzon University, Trabzon, Turkey

    Arda Ustubioglu

Authors
  1. Arda Ustubioglu
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  2. Beste Ustubioglu
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  3. Guzin Ulutas
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Contributions

AU contributed to conceptualization, methodology, and software. BU contributed to data curation, writing—original draft preparation, and software. GU contributed to visualization, investigation, and methodology.

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Correspondence to Beste Ustubioglu.

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The authors declare that this article is original, has not been published before, and is not currently being considered for publication elsewhere. The authors confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. The authors further confirm that the order of authors listed in the manuscript has been approved by all of them.

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Ustubioglu, A., Ustubioglu, B. & Ulutas, G. Mel spectrogram-based audio forgery detection using CNN. SIViP 17, 2211–2219 (2023). https://doi.org/10.1007/s11760-022-02436-4

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