AUTOMATIC SPOOFING DETECTION USING DEEP LEARNING

http://dx.doi.org/10.31703/gssr.2024(IX-I).11      10.31703/gssr.2024(IX-I).11      Published : Mar 1
Authored by : Muhammad Nafees , Abid Rauf , RabbiaMahum

11 Pages : 111-133

References

  • Almutairi, Z.,& H. Elgibreen(2023). “Detecting Fake Audio of Arabic Speakers Using Self-Supervised Deep Learning,” IEEE Access, 1, https://doi.org/10.1109/ACCESS.2023.3286864
  • Alzantot, M., Wang, Z.,&B. Srivastava, (2019)“Deep residual neural networks for audio spoofing detection,” in Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech, 1078– 1082. https://doi.org/10.21437/Interspeech.2019-3174
  • Balamurali, B. T. Lin, K. E. S. Lui, J. M. Chen, & D. (2019). Herremans, “Toward robust audio spoofing detection: A detailed comparison of traditional and learned features,” IEEE Access, 7, 84229–84241, https://doi.org/10.1109/ACCESS.2019.2923806
  • Columbia,B.( 2021). “A Capsule Network Based Approach for Detection of Audio Spoofing Attacks 1. Key Lab of Information Security, School of Computer Science and Engineering, Sun Yat-Sen University, 2. Alibaba Group, Hangzhou, China,” 6359–6363,
  • Delgado,H. et al., (2021). “ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan,” http://arxiv.org/abs/2109.00535
  • Dua, M. C. Jain, & Kumar, S.(2022). “LSTM and CNN based ensemble approach for spoof detection task in automatic speaker verification systems,” J. Ambient Intell. Humaniz.Comput, 13(4), 1985–2000, https://doi.org/10.1007/s12652-021-02960-0
  • Gao, Y.Vuong, M. Elyasi, G. Bharaj, & Singh, R.(2021). “Generalized Spoofing Detection Inspired from Audio Generation Artifacts,” http://arxiv.org/abs/2104.04111
  • Gomez-alanisA. et al. (2017). “On Joint Optimization of Automatic Speaker Verification and Anti-spoofing in the Embedding Space,” i, 1–15
  • Hamza, A. et al. (2022). “Deepfake Audio Detection via MFCC features using Machine Learning,” IEEE Access, 10, 134018–134028, https://doi.org/10.1109/ACCESS.2022.3231480
  • Ismail, A. M. Elpeltagy, M. S. Zaki., & K. Eldahshan, “A New Deep Learning-Based Methodology for Video Deepfake,” 1–1
  • Kinnunen, T. et al. (2017). “The ASVspoof 2017 challenge: Assessing the limits of replay spoofing attack detection,” in Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech, 2–6. https://doi.org/10.21437/Interspeech.2017-1111
  • Kinnunen, T. M. Todisco, N. Evans, J. Yamagishi., & K. A. Lee, (2017). “The ASVspoof 2017 Challenge : Assessing the Limits of Replay Spoofing Attack Detection National Institute of Informatics, Japan,” i, 2–6
  • Lavrentyeva, G. S. Novoselov, E. Malykh, A. Kozlov, O. Kudashev., &Shchemelinin, V. (2017). “Audio replay attack detection with deep learning frameworks,” in Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech, 82– 86. https://doi.org/10.21437/Interspeech.2017-360
  • Lorenzo-Trueba,J. et al.(2018). “The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods,” http://arxiv.org/abs/1804.04262
  • Mcuba, M. A. Singh, R. A. Ikuesan., & H. Venter, (2022). “The Effect of Deep Learning Methods on Deepfake Audio Detection for Digital Investigation,” Procedia Comput. Sci., 219, 211– 219. https://doi.org/10.1016/j.procs.2023.01.283
  • Todisco, M. et al.(2019). “ASVSpoof Future horizons in spoofed and fake audio detection,” Proc. Annu. Conf. Int. Speech Commun. Assoc. Interspeech, 1008–1012, https://doi.org/10.21437/Interspeech.2019-2249
  • Wang, Z. S. Cui, X. Kang, W. Sun., & Z. Li, (2020). “Densely Connected Convolutional Network for Audio Spoofing Detection,” 1352–1360
  • Wenger, E. M. Bronckers, C. Cianfarani, J. Cryan, A. Sha., & B. Y. Zhao, (2021). “Hello, It’s Me”: Deep Learning-based Speech Synthesis A acks in the Real World,” 235–251.
  • Yang, Y. et al.(2019). “The SJTU Robust Anti- spoofing System for the ASVspoof 2019 Challenge,” 1038–1042, https://doi.org/10.21437/Interspeech.2019-2170
  • Yi, J. et al.(2021). “Half-truth: A partially fake audio detection dataset,” Proc. Annu. Conf. Int. Speech Commun. Assoc. INTERSPEECH, 4, 2683–2687, https://doi.org/10.21437/Interspeech.2021-930
  • Yi, J et al.(2022). “ADD 2022: the First Audio Deep Synthesis Detection Challenge,” http://arxiv.org/abs/2202.08433
  • Yu, Y. et al.(2020). “RMAF : Relu-Memristor-Like Activation Function for Deep Learning,” IEEE Access, 8, 72727–72741, https://doi.org/10.1109/ACCESS.2020.2987829

References

  • Almutairi, Z.,& H. Elgibreen(2023). “Detecting Fake Audio of Arabic Speakers Using Self-Supervised Deep Learning,” IEEE Access, 1, https://doi.org/10.1109/ACCESS.2023.3286864
  • Alzantot, M., Wang, Z.,&B. Srivastava, (2019)“Deep residual neural networks for audio spoofing detection,” in Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech, 1078– 1082. https://doi.org/10.21437/Interspeech.2019-3174
  • Balamurali, B. T. Lin, K. E. S. Lui, J. M. Chen, & D. (2019). Herremans, “Toward robust audio spoofing detection: A detailed comparison of traditional and learned features,” IEEE Access, 7, 84229–84241, https://doi.org/10.1109/ACCESS.2019.2923806
  • Columbia,B.( 2021). “A Capsule Network Based Approach for Detection of Audio Spoofing Attacks 1. Key Lab of Information Security, School of Computer Science and Engineering, Sun Yat-Sen University, 2. Alibaba Group, Hangzhou, China,” 6359–6363,
  • Delgado,H. et al., (2021). “ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan,” http://arxiv.org/abs/2109.00535
  • Dua, M. C. Jain, & Kumar, S.(2022). “LSTM and CNN based ensemble approach for spoof detection task in automatic speaker verification systems,” J. Ambient Intell. Humaniz.Comput, 13(4), 1985–2000, https://doi.org/10.1007/s12652-021-02960-0
  • Gao, Y.Vuong, M. Elyasi, G. Bharaj, & Singh, R.(2021). “Generalized Spoofing Detection Inspired from Audio Generation Artifacts,” http://arxiv.org/abs/2104.04111
  • Gomez-alanisA. et al. (2017). “On Joint Optimization of Automatic Speaker Verification and Anti-spoofing in the Embedding Space,” i, 1–15
  • Hamza, A. et al. (2022). “Deepfake Audio Detection via MFCC features using Machine Learning,” IEEE Access, 10, 134018–134028, https://doi.org/10.1109/ACCESS.2022.3231480
  • Ismail, A. M. Elpeltagy, M. S. Zaki., & K. Eldahshan, “A New Deep Learning-Based Methodology for Video Deepfake,” 1–1
  • Kinnunen, T. et al. (2017). “The ASVspoof 2017 challenge: Assessing the limits of replay spoofing attack detection,” in Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech, 2–6. https://doi.org/10.21437/Interspeech.2017-1111
  • Kinnunen, T. M. Todisco, N. Evans, J. Yamagishi., & K. A. Lee, (2017). “The ASVspoof 2017 Challenge : Assessing the Limits of Replay Spoofing Attack Detection National Institute of Informatics, Japan,” i, 2–6
  • Lavrentyeva, G. S. Novoselov, E. Malykh, A. Kozlov, O. Kudashev., &Shchemelinin, V. (2017). “Audio replay attack detection with deep learning frameworks,” in Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech, 82– 86. https://doi.org/10.21437/Interspeech.2017-360
  • Lorenzo-Trueba,J. et al.(2018). “The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods,” http://arxiv.org/abs/1804.04262
  • Mcuba, M. A. Singh, R. A. Ikuesan., & H. Venter, (2022). “The Effect of Deep Learning Methods on Deepfake Audio Detection for Digital Investigation,” Procedia Comput. Sci., 219, 211– 219. https://doi.org/10.1016/j.procs.2023.01.283
  • Todisco, M. et al.(2019). “ASVSpoof Future horizons in spoofed and fake audio detection,” Proc. Annu. Conf. Int. Speech Commun. Assoc. Interspeech, 1008–1012, https://doi.org/10.21437/Interspeech.2019-2249
  • Wang, Z. S. Cui, X. Kang, W. Sun., & Z. Li, (2020). “Densely Connected Convolutional Network for Audio Spoofing Detection,” 1352–1360
  • Wenger, E. M. Bronckers, C. Cianfarani, J. Cryan, A. Sha., & B. Y. Zhao, (2021). “Hello, It’s Me”: Deep Learning-based Speech Synthesis A acks in the Real World,” 235–251.
  • Yang, Y. et al.(2019). “The SJTU Robust Anti- spoofing System for the ASVspoof 2019 Challenge,” 1038–1042, https://doi.org/10.21437/Interspeech.2019-2170
  • Yi, J. et al.(2021). “Half-truth: A partially fake audio detection dataset,” Proc. Annu. Conf. Int. Speech Commun. Assoc. INTERSPEECH, 4, 2683–2687, https://doi.org/10.21437/Interspeech.2021-930
  • Yi, J et al.(2022). “ADD 2022: the First Audio Deep Synthesis Detection Challenge,” http://arxiv.org/abs/2202.08433
  • Yu, Y. et al.(2020). “RMAF : Relu-Memristor-Like Activation Function for Deep Learning,” IEEE Access, 8, 72727–72741, https://doi.org/10.1109/ACCESS.2020.2987829

Cite this article

APA

CHICAGO

    CHICAGO : Nafees, Muhammad, Abid Rauf, and Rabbia Mahum. 2024. "Automatic Spoofing Detection Using Deep Learning." Global Social Sciences Review, IX (I): 111-133 doi: 10.31703/gssr.2024(IX-I).11

HARVARD

    HARVARD : NAFEES, M., RAUF, A. & MAHUM, R. 2024. Automatic Spoofing Detection Using Deep Learning. Global Social Sciences Review, IX, 111-133.

MHRA

    MHRA : Nafees, Muhammad, Abid Rauf, and Rabbia Mahum. 2024. "Automatic Spoofing Detection Using Deep Learning." Global Social Sciences Review, IX: 111-133

MLA

    MLA : Nafees, Muhammad, Abid Rauf, and Rabbia Mahum. "Automatic Spoofing Detection Using Deep Learning." Global Social Sciences Review, IX.I (2024): 111-133 Print.

OXFORD

    OXFORD : Nafees, Muhammad, Rauf, Abid, and Mahum, Rabbia (2024), "Automatic Spoofing Detection Using Deep Learning", Global Social Sciences Review, IX (I), 111-133

TURABIAN