Al-Qadisiyah Journal for Engineering Sciences

Detecting fake audio using convolutional neural networks for reducing misinformation

Document Type : Research Paper

Authors

Esraa Y. Tarkan 1
Mohammed M. Mallam 2
Sarah K. Salim 3

1 Ministry of Higher Education and Scientific Research, Baghdad, Iraq.

2 College of Artificial Intelligence Engineering, University of Technology, Baghdad, Iraq.

3 Electrical Engineering Department, University of Misan, Misan, Iraq.

https://doi.org/10.30772/qjes.2025.161951.1608
Abstract
Recently, a proliferation of techniques capable of replicating sounds has emerged, encompassing manipulated recordings, synthesized audio, and deepfake technologies. These advanced methods for generating artificial sounds have inadvertently given rise to a multitude of issues, prominently including the dissemination of misinformation, propaganda, and significant reputational damage. This article addresses this critical problem by proposing the application of a Convolutional Neural Network (CNN) model designed to predict the authenticity of a given sound. For feature extraction from the audio, Mel Frequency Cepstral Coefficients (MFCC) are utilized. To rigorously assess the robustness of the proposed model, the intricate combinations of these extracted features were further analyzed using derivatives of the MFCC features. The Fake-or-Real (FoR) dataset was used to train and test the proposed model. The Accuracy, F1-score, Precision, Recall, and Loss are used as metrics to evaluate the model's performance. The model performed well, with an accuracy of 99.64%. The proposed model was comprehensively evaluated by systematically varying the extracted features through different derivative orders, where the accuracy decreased to 94.34%. The experimental results demonstrate the effectiveness of the model in accurately detecting fabricated audio.

Keywords

Convolutional Neural Network
Mel Frequency Cepstral Coefficient
Derivative
Fake Audio
Misinformation
Crossmark
Subjects
  • S. C. Venkatesh, S. Shaji, and B. M. Sundaram, “A fake profile detection model using multistage stacked ensemble classification,” Proceedings of Engineering
    and Technology Innovation, vol. 26, pp. 18–32, 2024. [Online]. Available: https://doi.org/10.46604/peti.2024.13200
  • E. A¨ımeur, S. Amri, and G. Brassard, “Fake news, disinformation and misinformation in social media: A review,” Social Network Analysis and Mining,
    vol. 13, p. 30, 2023. [Online]. Available: https://doi.org/10.1007/s13278-023-01028-5
  • Z. Khanjani, G. Watson, and V. P. Janeja, “Audio deepfakes: A survey,” Frontiers in Big Data, vol. 5, p. 1001063, 2023. [Online]. Available:
    https://doi.org/10.3389/fdata.2022.1001063
  • A. Alali and G. Theodorakopoulos, “Review of existing methods for generating and detecting fake and partially fake audio,” in Proceedings of the 10th ACM
    International Workshop on Security and Privacy Analytics, 2024. [Online]. Available: https://doi.org/10.1145/3643651.3659894
  • A. Jellali, I. Ben Fredj, and K. Ouni, “Pushing the boundaries of deepfake audio detection with a hybrid mfcc and spectral contrast approach,” Multimedia
    Tools and Applications, pp. 1–20, 2024. [Online]. Available: https://doi.org/10.1007/s11042-024-19819-z
  • R. Reimao and V. Tzerpos, “FoR: A dataset for synthetic speech detection,” in 10th International Conference on Speech Technology and Human-Computer
    Dialogue, 2019. [Online]. Available: https://doi.org/10.1109/SPED.2019.8906599
  • B. Vimal, M. Surya, Darshan, V. S. Sridhar, and A. Ashok, “MFCC based audio classification using machine learning,” in 12th International Conference on
    Computing Communication and Networking Technologies, 2021. [Online]. Available: https://doi.org/10.1007/s43926-023-00049-y
  •  J. Khochare, C. Joshi, B. Yenarkar, S. Suratkar, and F. Kazi, “A deep learning framework for audio deepfake detection,” Arabian Journal for Science and
    Engineering, vol. 47, no. 3, pp. 3447–3458, 2021. [Online]. Available: https://doi.org/10.1007/s13369-021-06297-w
  • A. Hamza, A. R. R. Javed, F. Iqbal, N. Kryvinska, A. S. Almadhor, and Z. Jalil, “Deepfake audio detection via mfcc features using machine learning,” IEEE
    Access, vol. 10, pp. 134 018–134 028, 2022. [Online]. Available: https://doi.org/10.1109/ACCESS.2022.3231480
  • T. P. Rahul, P. R. Aravind, C. Ranjith, U. Nechiyil, and N. Paramparambath, “Audio spoofing verification using deep convolutional neural networks by transfer
    learning,” 2020, arXiv preprint arXiv:2008.03464. [Online]. Available: https://doi.org/10.48550/arXiv.2008.03464
  • C. A. Hern´andez-Nava, E. A. Rinc´on-Garc´ıa, P. Lara-Vel´azquez, S. Gerardo de-los Cobos-Silva, M. A. Guti´errez-Andrade, and R. A. Mora-Guti´errez, “Voice
    spoofing detection using a neural networks assembly considering spectrograms and mel frequency cepstral coefficients,” PeerJ Computer Science, vol. 9, p.
    e1740, 2023. [Online]. Available: https://doi.org/10.7717/peerj-cs.1740
  • T. B. Patel and H. A. Patil, “Combining evidences from mel cepstral, cochlear filter cepstral and instantaneous frequency features for detection of natural vs.
    spoofed speech,” in Interspeech, 2015, pp. 2062–2066.
    [13] D. M. Ballesteros, Y. Rodriguez-Ortega, D. Renza, and G. Arce, “Deep4snet: Deep learning for fake speech classification,” Expert Systems with Applications,
    vol. 184, p. 115465, 2021. [Online]. Available: https://doi.org/10.1016/j.eswa.2021.115465
  • T. Liu, D. Yan, R. Wang, N. Yan, and G. Chen, “Identification of fake stereo audio using SVM and CNN,” Information, vol. 12, no. 7, p. 263, 2021. [Online].
    Available: https://doi.org/10.3390/info12070263
  • K. M. Rezaul, M. Jewel, M. S. Islam, K. N. A. Siddiquee, N. Barua, M. A. Rahman, M. Shan-A-Khuda, R. B. Sulaiman, M. S. I. Shaikh, M. A. Hamim, F. M.
    Tanmoy, A. U. Haque, M. S. Nipun, N. Dorudian, A. Kareem, A. K. Farid, A. Mubarak, T. Jannat, and U. F. T. Asha, “Enhancing audio classification through
    mfcc feature extraction and data augmentation with cnn and rnn models,” International Journal of Advanced Computer Science and Applications, vol. 15,
    no. 7, pp. 37–53, 2024. [Online]. Available: http://dx.doi.org/10.14569/IJACSA.2024.0150704
  • M. Malik, M. K. Malik, K. Mehmood, and I. Makhdoom, “Automatic speech recognition: A survey,” Multimedia Tools and Applications, vol. 80, no. 6, pp.
    9411–9457, 2021. [Online]. Available: https://doi.org/10.1007/s11042-020-10073-7
  • M. Altayeb and A. Arabiat, “Crack detection based on mel-frequency cepstral coefficients features using multiple classifiers,” International Journal of
    Electrical and Computer Engineering (IJECE), vol. 14, no. 3, pp. 3332–3341, 2024. [Online]. Available: https://doi.org/10.11591/ijece.v14i3.pp3332-3341
  • Z. K. Abdul and A. K. Al-Talabani, “Mel frequency cepstral coefficient and its applications: A review,” IEEE Access, vol. 10, pp. 122 136–122 158, 2022.
    [Online]. Available: https://doi.org/10.1109/ACCESS.2022.3223444
  • S. Sigurdsson, K. B. Petersen, and T. Lehn-Schiøler, “Mel frequency cepstral coefficients: An evaluation of robustness of mp3 encoded music,” in 7th
    International Conference on Music Information Retrieval, 2006, pp. 286–289.
  • S. A. Alrubaie and I. M. Hassoon, “Support vector machine (svm) for colorization the grayscale image,” Al-Qadisiyah Journal for Engineering Sciences,
    vol. 13, no. 3, pp. 207–214, 2021. [Online]. Available: https://doi.org/10.30772/qjes.v13i3.658
  • L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaria, M. A. Fadhel, M. Al-Amidie, and L. Farhan, “Review
    of deep learning: Concepts, cnn architectures, challenges, applications, future directions,” Journal of Big Data, vol. 8, no. 1, 2021. [Online]. Available:
    https://doi.org/10.1186/s40537-021-00444-8
  • N. A. Abdulrazzaq and A. M. Radhi, “Face recognition using deep convolutional neural networks,” Al-Qadisiyah Journal for Engineering Sciences, vol. 18,
    no. 3, 2025.
  • S. Sharma, S. Sharma, and A. Athaiya, “Activation functions in neural networks,” International Journal of Engineering Applied Sciences and Technology,
    vol. 04, no. 12, pp. 310–316, 2020. [Online]. Available: https://www.geeksforgeeks.org/
  • S. K. Salim, M. M. Msallam, and H. Ismail, “Design novel cnn architecture to protect personal devices from unauthorized access using face recognition,”
    Nanotechnology Perceptions, vol. 20, no. 3, pp. 82–93, 2024. [Online]. Available: https://doi.org/10.62441/nano-ntp.v20i3.7
  • O. A. Olayemi, O. I. Salako, A. Jinadu, A. M. Obalalu, and B. E. Anyaegbuna, “Aerodynamic lift coefficient prediction of supercritical airfoils at transonic
    flow regime using convolutional neural networks (cnns) and multi-layer perceptions (mlps),” Al-Qadisiyah Journal for Engineering Sciences, vol. 16, no. 2,
    pp. 108–115, 2023. [Online]. Available: https://kwasuspace.kwasu.edu.ng/handle/123456789/272
  • A. M. Abood, A. R. Nasser, and H. Al-Khazraji, “Predictive maintenance of electromechanical systems based on enhanced generative adversarial neural
    network with convolutional neural network,” International Journal of Artificial Intelligence, vol. 12, no. 4, pp. 1704–1712, 2023. [Online]. Available:
    https://doi.org/10.11591/ijai.v12.i4.pp1704-1712
  • Z. Li, E. Wallace, S. Shen, K. Lin, K. Keutzer, D. Klein, and J. E. Gonzalez, “Train large, then compress: Rethinking model size for efficient
    training and inference of transformers,” in 37th International Conference on Machine Learning, 2020, pp. 5914–5924. [Online]. Available:
    https://doi.org/10.48550/arXiv.2002.11794
  • Y. Ho and S. Wookey, “The real-world-weight cross-entropy loss function: Modeling the costs of mislabeling,” IEEE Access, vol. 8, pp. 4806–4813, 2020.
  • C. Ahmadi, S. H. Wang, S. P. Chiu, and J. L. Chen, “Dual acoustic feature fusion for enhanced audio deepfake detection using vgg-16 architecture: Mitigating
    speech tampering with mfcc and eltp,” in 2024 RIVF International Conference on Computing and Communication Technologies (RIVF), 2024, pp. 216–220.
  • N. Bakken, S. Singh, M. Prashant, and T. Das, “Deep fake audio detection framework using mfccs, chroma features, and spectrogram images,” in 2025 IEEE
    Conference on Artificial Intelligence (CAI), 2025, pp. 1–6. [Online]. Available: https://doi.org/10.1109/CAI64502.2025.00175
Al-Qadisiyah Journal for Engineering Sciences
Volume 19, Issue 2
Summer 2026
Pages 270-277

  • Receive Date 24 June 2025
  • Revise Date 12 September 2025
  • Accept Date 13 May 2026

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