Speaker-Specific Method of Spoofing Attack Detection Based on Anomaly Detection
Keywords:
speaker-specific approach, spoofing detection, presentation attack detection, biometric systems, voice biometrics, transfer learning, anomaly detectionAbstract
Most research in the field of voice presentation attack detection relies on the speaker-independent approach. Nevertheless, several scientific works indicate that using the speaker-specific approach, which involves utilizing prior knowledge about the identity of the claimed speaker to enhance the accuracy of spoofing detection, is likely to be beneficial. Therefore, the goal of this work is to propose a speaker-specific method of spoofing attack detection based on anomaly detection and to evaluate its applicability to the detection of synthesized speech and converted voice. Artificial neural networks pre-trained for the tasks of spoofing detection, speaker recognition, and audio pattern recognition are used for feature extraction. A set of anomaly detection models are used as backend classifiers. Each of them is trained on bonafide data of a target speaker. The experimental evaluation of the proposed method on the ASVspoof 2019 LA dataset shows that the best speaker-specific spoofing detection system, which uses an anomaly detection model and a neural network pre-trained for the task of speaker recognition, achieves an EER of 4.74%. This result suggests that embeddings extracted by networks pre-trained for speaker recognition contain information that can be utilized for spoofing detection. In addition, the proposed method allowed to increase the accuracy of three baseline systems pre-trained for the task of spoofing detection. Experiments with two baseline systems on the ASVspoof 2019 LA dataset showed relative improvement in terms of EER by 7.1% and 9.2%, and in terms of min t-DCF by 4.6%. Experiments with the third baseline system on the ASVspoof 2021 LA dataset showed relative improvement in terms of EER by 3.9% without significant improvement of min t-DCF.
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10. Novoselov S., Kozlov A., Lavrentyeva G., Simonchik K., Shchemelinin V. STC Anti-spoofing Systems for the ASVspoof 2015 Challenge. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2016. pp. 5475–5479. DOI: 10.1109/ICASSP.2016.7472724.
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14. Suthokumar G., Sriskandaraja K., Sethu V., Ambikairajah E., Li H. An Analysis of Speaker Dependent Models in Replay Detection. APSIPA Transactions on Signal and Information Processing. 2020. vol. 9. no. 1. DOI: 10.1017/ATSIP.2020.9.
15. Evsyukov M.V., Putyato M.M., Makaryan A.S. [The Effect of Speaker Variability on Distinguishability of Bonafide and Synthetized Speech]. Voprosy kiberbezopasnosti – Cybersecurity issues. 2024. vol. 60. no. 2. pp. 44–52. DOI: 10.21681/2311-3456-2024-2-44-52. (In Russ.).
16. Evsjukov M.V., Putjato M.M., Makarjan A.S., Cherkasov A.N. [Assessing Accuracy of Speaker-Specific Approach to Logical Access Spoofing Detection]. Vestnik Voronezhskogo gosudarstvennogo universiteta. Serija: Sistemnyj analiz i informacionnye tehnologii – Proceedings of Voronezh State University. Series: Systems Analysis and Information Technologies. 2024. no. 1. pp. 77–93. DOI: 10.17308/sait/1995-5499/2024/l/77-93. (In Russ.).
17. Castan D., Rahman M.H., Bakst S., Cobo-Kroenke C., McLaren M., Graciarena M., Lawson A. Speaker-Targeted Synthetic Speech Detection. Proc. of The Speaker and Language Recognition Workshop (Odyssey 2022). 2022. pp. 62–69. DOI: 10.21437/Odyssey.2022-9.
18. Zhang Y., Jiang F., Duan Z. One-Class Learning Towards Synthetic Voice Spoofing Detection. IEEE Signal Processing Letters. 2021. vol. 28. pp. 937–941. DOI: 10.1109/LSP.2021.3076358.
19. Brummer N., Swart A., Mosner L., Silnova A., Plchot O., Stafylakis T., Burget L. Probabilistic Spherical Discriminant Analysis: An Alternative to PLDA for length-normalized embeddings. Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech. 2022. pp. 1446–1450. DOI: 10.21437/Interspeech.2022-731.
20. Liu X., Sahidullah M., Lee K.A., Kinnunen T. Speaker-Aware Anti-spoofing. Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech. 2023. pp. 2498–2502. DOI: 10.21437/Interspeech.2023-1323.
21. Jung J.W. Heo H.S., Tak H., Shim H.J., Chung J.S., Lee B.J., Yu H.J., Evans N. AASIST: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2022. pp. 6367–6371. DOI: 10.1109/ICASSP43922.2022.9747766.
22. Fatemifar S., Arashloo S.R., Awais M., Kittler J. Client-Specific Anomaly Detection for Face Presentation Attack Detection. Pattern Recognition. 2020. vol. 112. no. 8. DOI: 10.1016/j.patcog.2020.107696.
23. Seliya N., Zadeh A.A., Khoshgoftaar T.M. A Literature Review on One-Class Classification and its Potential Applications in Big Data. Journal of Big Data. 2021. vol. 8. no. 1. DOI: 10.1186/s40537-021-00514-x.
24. Khan S., Madden M. A Survey of Recent Trends in One Class Classification. Artificial Intelligence and Cognitive Science, Lecture Notes in Computer Science. 2009. vol. 6206. pp. 188–197. DOI: 10.1007/978-3-642-17080-5_21.
25. Villalba J., Miguel A., Ortega A., Lleida E. Spoofing Detection with DNN and One-Class SVM for the ASVspoof 2015 Challenge. Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech. 2015. pp. 2067–2071. DOI: 10.21437/interspeech.2015-468.
26. Amorim L.B.V., Cavalcanti G.D.C., Cruz R.M.O. The Choice of Scaling Technique Matters for Classification Performance. Applied Soft Computing. 2023. vol. 133. DOI: 10.1016/j.asoc.2022.109924.
27. Wang C., Xu R., Xu S., Meng W., Zhang X. CNDesc: Cross Normalization for Local Descriptors Learning. IEEE Transactions on Multimedia. 2022. vol. 99. DOI: 10.1109/TMM.2022.3169331.
28. Dorabiala O., Aravkin A.Y., Kutz J.N. Ensemble Principal Component Analysis. IEEE Access. 2024. vol. 12. pp. 6663–6671. DOI: 10.1109/ACCESS.2024.3350984.
29. Tak H., Todisco M., Wang X., Jung J., Yamagishi J., Evans N. Automatic Speaker Verification Spoofing and Deepfake Detection Using Wav2vec 2.0 and Data Augmentation. Proc. of The Speaker and Language Recognition Workshop (Odyssey 2022). 2022. pp. 112–119. DOI: 10.21437/Odyssey.2022-16.
30. Wang X. et al. ASVspoof 2019: A Large-Scale Public Database of Synthesized, Converted and Replayed Speech. Computer Speech & Language. 2020. vol. 64. DOI: 10.1016/j.csl.2020.101114.
31. Yamagishi J., Wang X., Todisco M., Sahidullah M., Patino J., Nautsch A., Liu X., Lee K.A., Kinnunen T., Evans N., Delgado H. ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection. Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech. 2021. pp. 47–54. DOI: 10.21437/asvspoof.2021-8.
32. Ge W., Tak H., Todisco M., Evans N. On the Potential of Jointly-Optimised Solutions to Spoofing Attack Detection and Automatic Speaker Verification. Proceedings of the 6th International Conference, IberSPEECH. 2022. pp. 51–55. DOI: 10.21437/iberspeech.2022-11.
33. Ding S., Chen T., Gong X., Zha W., Wang Z. AutoSpeech: Neural Architecture Search for Speaker Recognition. Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech. 2020. pp. 916–920. DOI: 10.21437/Interspeech.2020-1258.
34. Xie W., Nagrani A., Chung J.S., Zisserman A. Utterance-Level Aggregation for Speaker Recognition in the Wild. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2019. pp. 5791–5795. DOI: 10.1109/ICASSP.2019.8683120.
35. Ravanelli M., Bengio Y. Speaker Recognition from Raw Waveform with SincNet. IEEE Spoken Language Technology Workshop (SLT). 2018. pp. 1021–1028. DOI: 10.1109/SLT.2018.8639585.
36. Jung J.W., Kim Y., Heo H.S., Lee B.-J., Kwon Y., Son Chung J.S. Pushing the Limits of Raw Waveform Speaker Recognition. Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech. 2022. pp. 2228–2232. DOI: 10.21437/Interspeech.2022-126.
37. Nagraniy A., Chungy J.S., Zisserman A. VoxCeleb: A large-scale speaker identification dataset. Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech. 2017. pp. 2616–2620. DOI: 10.21437/Interspeech.2017-950.
38. Chung J.S., Nagrani A., Zisserman A. VoxCeleb2: Deep Speaker Recognition. Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech. 2018. pp. 1086–1090. DOI: 10.21437/Interspeech.2018-1929.
39. Panayotov V., Chen G., Povey D., Khudanpur S. LibriSpeech: An ASR Corpus Based on Public Domain Audio Books. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2015. pp. 5206–5210. DOI: 10.1109/ICASSP.2015.7178964.
40. Kong Q., Cao Y., Iqbal T., Wang Y., Wang W., Plumbley M.D. PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition. IEEE/ACM Transactions on Audio Speech and Language Processing. 2020. vol. 28. pp. 2880–2894. DOI: 10.1109/TASLP.2020.3030497.
41. Gemmeke G.F., Ellis D.P.W., Freedman D., Jansen A., Lawrence W., Moore R.C. Audio Set: An Ontology and Human-Labeled Dataset for Audio Events. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2017. pp. 776–780. DOI: 10.1109/ICASSP.2017.7952261.
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Published
2025-01-20
How to Cite
Evsyukov, M. (2025). Speaker-Specific Method of Spoofing Attack Detection Based on Anomaly Detection. Informatics and Automation, 24(1), 193-228. https://doi.org/10.15622/ia.24.1.8
Section
Information Security
Copyright (c) Михаил Витальевич Евсюков
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