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- 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 Meâ€: 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
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APA : Nafees, M., Rauf, A., & Mahum, R. (2024). Automatic Spoofing Detection Using Deep Learning. Global Social Sciences Review, IX(I), 111-133. https://doi.org/10.31703/gssr.2024(IX-I).11
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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
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HARVARD : NAFEES, M., RAUF, A. & MAHUM, R. 2024. Automatic Spoofing Detection Using Deep Learning. Global Social Sciences Review, IX, 111-133.
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MHRA : Nafees, Muhammad, Abid Rauf, and Rabbia Mahum. 2024. "Automatic Spoofing Detection Using Deep Learning." Global Social Sciences Review, IX: 111-133
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MLA : Nafees, Muhammad, Abid Rauf, and Rabbia Mahum. "Automatic Spoofing Detection Using Deep Learning." Global Social Sciences Review, IX.I (2024): 111-133 Print.
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OXFORD : Nafees, Muhammad, Rauf, Abid, and Mahum, Rabbia (2024), "Automatic Spoofing Detection Using Deep Learning", Global Social Sciences Review, IX (I), 111-133
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TURABIAN : Nafees, Muhammad, Abid Rauf, and Rabbia Mahum. "Automatic Spoofing Detection Using Deep Learning." Global Social Sciences Review IX, no. I (2024): 111-133. https://doi.org/10.31703/gssr.2024(IX-I).11