{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:32:13Z","timestamp":1760401933073,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":50,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032079558","type":"print"},{"value":"9783032079565","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-07956-5_22","type":"book-chapter","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T12:22:58Z","timestamp":1760358178000},"page":"307-321","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Source Vendor Tracing of\u00a0Audio Deepfakes"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6097-5426","authenticated-orcid":false,"given":"Marina","family":"Volkova","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Artem","family":"Chirkovskiy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Egor","family":"Ausev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ekaterina","family":"Shangina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"key":"22_CR1","unstructured":"Pindrop Security Inc.: Pindrop voice intelligence report. Technical report (2019)"},{"key":"22_CR2","unstructured":"Bethea, C.: The terrifying A.I. Scam that uses your loved one\u2019s voice. The New Yorker (2024). https:\/\/www.newyorker.com\/science\/annals-of-artificial-intelligence\/the-terrifying-ai-scam-that-uses-your-loved-ones-voice. Accessed 01 June 2025"},{"key":"22_CR3","unstructured":"Dhawan, H., Bhura, S.: Fooled by your own kid? Chilling rise of AI voice cloning scams. Times of India (2024). https:\/\/timesofindia.indiatimes.com\/india\/fooled-by-your-own-kid-chilling-rise-of-ai-voice-cloning-scams\/articleshow\/108569446.cms. Accessed 01 June 2025"},{"key":"22_CR4","unstructured":"Delgado, H., et al.: ASVspoof 2021: Automatic speaker verification spoofing and countermeasures challenge evaluation plan. arXiv preprint arXiv:2109.00535 (2021)"},{"key":"22_CR5","doi-asserted-by":"publisher","unstructured":"Jung, J., et al.: SASV 2022: the first spoofing-aware speaker verification challenge. In: Interspeech 2022 (2022). https:\/\/doi.org\/10.21437\/interspeech.2022-11270","DOI":"10.21437\/interspeech.2022-11270"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Yi, J., et al.: Add 2022: the first audio deep synthesis detection challenge. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 9216-9220. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9746939"},{"key":"22_CR7","unstructured":"Yi, J., et al.: ADD 2023: the second audio deepfake detection challenge. In: Proceedings of the 2nd Audio Deepfake Detection Challenge (ADD 2023), CEUR Workshop Proceedings, vol. 3597 (2023). https:\/\/ceur-ws.org\/Vol-3597\/"},{"key":"22_CR8","unstructured":"Liao, S., et al.: Fish-speech: leveraging large language models for advanced multilingual text-to-speech synthesis. arXiv preprint arXiv:2411.01156 (2024)"},{"key":"22_CR9","unstructured":"Koutsianos, D., Zacharopoulos, S., Panagakis, Y., Stafylakis, T.: Synthetic speech source tracing using metric learning. arXiv preprint arXiv:2506.02590 (2025)"},{"key":"22_CR10","unstructured":"Qin, X., Wang, X., Chen, Y., Meng, Q., Li, M.: From speaker verification to deepfake algorithm recognition: Our learned lessons from add2023 track 3. In: DADA@ IJCAI (2023)"},{"key":"22_CR11","unstructured":"Kulkarni, A., Dowerah, S., Alumae, T., Doss, M.M.: Unveiling audio deepfake origins. arXiv preprint arXiv:2506.02085 (2025)"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Xie, Y., et al.: Generalized source tracing: detecting novel audio deepfake algorithm with real emphasis and fake dispersion strategy. arXiv preprint arXiv:2406.03240 (2024)","DOI":"10.21437\/Interspeech.2024-254"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Muller, N.M., et al.: MLAAD: the multilanguage audio anti-spoofing dataset. In: IJCNN 2024 (2024)","DOI":"10.1109\/IJCNN60899.2024.10650962"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Todisco, M., et al.: ASVspoof 2019: future horizons in spoofed and fake audio detection. In: Interspeech 2019 (2019)","DOI":"10.21437\/Interspeech.2019-2249"},{"key":"22_CR15","unstructured":"Xie, Y., et al.: Neural codec source tracing. arXiv preprint arXiv:2501.06514 (2025)"},{"key":"22_CR16","unstructured":"Chen, X., et al.: Codec-based deepfake source tracing. arXiv preprint arXiv:2505.12994 (2025)"},{"key":"22_CR17","unstructured":"Chen, X., et al.: Towards generalized source tracing for codec-based deepfake speech. arXiv preprint arXiv:2506.07294 (2025)"},{"key":"22_CR18","unstructured":"Phukan, O.C.: Towards neural audio codec source parsing. arXiv preprint arXiv:2506.12627 (2025)"},{"key":"22_CR19","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2017). https:\/\/openreview.net\/forum?id=Hkg4TI9xl"},{"key":"22_CR20","unstructured":"Liu, W., Wang, X., Owens, J., Li, Y.: Energy-based out-of-distribution detection. In: Advances in Neural Information Processing Systems, vol. 33, pp. 21 464\u201321 475 (2020)"},{"key":"22_CR21","unstructured":"Sun, Y., Ming, Y., Zhu, X., Li, Y.: Out-of-distribution detection with deep nearest neighbors. In: International Conference on Machine Learning, pp. 20 827\u201320 840. PMLR (2022)"},{"key":"22_CR22","unstructured":"Lee, K., Lee, K., Lee, H., Shin, J.: A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In: International Conference on Machine Learning, vol. 31 (2018)"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Zhu, T., Wang, X., Qin, X., Li, M.: Source tracing: detecting voice spoofing. In: APSIPA ASC (2022)","DOI":"10.23919\/APSIPAASC55919.2022.9980129"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Klein, N., Chen, T., Tak, H., Casal, R., Khoury, E.: Source tracing of audio deepfake systems. In: Proceedings of the Interspeech 2024, pp. 4104\u20134108 (2024). https:\/\/www.isca-archive.org\/interspeech_2024\/klein24_interspeech.html","DOI":"10.21437\/Interspeech.2024-1283"},{"key":"22_CR25","unstructured":"Zhang, C.Y., et al.: Distinguishing neural speech synthesis models through fingerprints in speech waveforms. In: Proceedings of the CCL 2024 (2024). https:\/\/aclanthology.org\/2024.ccl-1.90"},{"key":"22_CR26","unstructured":"Mishra, J., Chhibber, M., Shim, H.J., Kinnunen, T.H.: Towards explainable spoofed speech attribution. Comput. Speech Lang. (2025)"},{"key":"22_CR27","unstructured":"Betker, J.: Better speech synthesis through scaling. arXiv preprint arXiv:2305.07243 (2023)"},{"key":"22_CR28","unstructured":"Rashad, M.: Multilingual-TTS datset. https:\/\/huggingface.co\/datasets\/MohamedRashad\/multilingual-tts. Accessed 01 June 2025"},{"key":"22_CR29","unstructured":"Du, Z., et al.: CosyVoice: a scalable multilingual zero-shot text-to-speech synthesizer based on supervised semantic tokens. arXiv preprint arXiv:2407.05407 (2024)"},{"key":"22_CR30","unstructured":"Liu, S.: Zero-shot voice conversion with diffusion transformers. arXiv preprint arXiv:2411.09943 (2024)"},{"key":"22_CR31","doi-asserted-by":"publisher","unstructured":"Kinnunen, T., et al.: The ASVspoof 2017 challenge: assessing the limits of replay spoofing attack detection. In: Proceedings of the Interspeech 2017, pp. 2\u20136 (2017). https:\/\/doi.org\/10.21437\/Interspeech.2017-1111","DOI":"10.21437\/Interspeech.2017-1111"},{"key":"22_CR32","unstructured":"Wang, X., et al.: ASVspoof 2019: a large-scale public database of synthesized, converted and replayed speech. arXiv preprint arXiv:1911.01601 (2019)"},{"key":"22_CR33","doi-asserted-by":"publisher","unstructured":"Zen, H., et al.: LibriTTS: a corpus derived from LibriSpeech for text-to-speech. In: Proceedings of the Interspeech 2019, pp. 1526\u20131530 (2019). https:\/\/doi.org\/10.21437\/Interspeech.2019-2441","DOI":"10.21437\/Interspeech.2019-2441"},{"key":"22_CR34","unstructured":"Ito, K., Johnson, L.: The LJ speech dataset (2017). https:\/\/keithito.com\/LJ-Speech-Dataset\/"},{"key":"22_CR35","doi-asserted-by":"publisher","unstructured":"Gong, Y., Yang, J., Huber, J., MacKnight, M., Poellabauer, C.: REMASC: realistic replay attack corpus for voice controlled systems. In: Proceedings of the Interspeech 2019, pp. 1801\u20131805 (2019). https:\/\/doi.org\/10.21437\/Interspeech.2019-1541","DOI":"10.21437\/Interspeech.2019-1541"},{"key":"22_CR36","unstructured":"Veaux, C., Yamagishi, J., MacDonald, K.: CSTR VCTK corpus: English multi-speaker corpus for CSTR voice cloning toolkit. University of Edinburgh. The Centre for Speech Technology Research (CSTR), vol. 6, p. 15 (2017)"},{"key":"22_CR37","doi-asserted-by":"publisher","unstructured":"Reimao, R., Tzerpos, V.: FoR: a dataset for synthetic speech detection. In: 2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), Timisoara, Romania, pp. 1\u201310 (2019). https:\/\/doi.org\/10.1109\/SPED.2019.8906599","DOI":"10.1109\/SPED.2019.8906599"},{"key":"22_CR38","doi-asserted-by":"publisher","unstructured":"Yamagishi, J., et al.: ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection. In: Proceedings of the 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge, pp. 47\u201354 (2021). https:\/\/doi.org\/10.21437\/ASVSPOOF.2021-8","DOI":"10.21437\/ASVSPOOF.2021-8"},{"key":"22_CR39","doi-asserted-by":"publisher","unstructured":"M\u00fcller, N., Czempin, P., Diekmann, F., Froghyar, A., B\u00f6ttinger, K.: Does audio deepfake detection generalize? In: Proceedings of the Interspeech 2022, pp. 2783\u20132787 (2022). https:\/\/doi.org\/10.21437\/Interspeech.2022-108","DOI":"10.21437\/Interspeech.2022-108"},{"key":"22_CR40","doi-asserted-by":"crossref","unstructured":"Conneau, A., et al.: FLEURS: few-shot learning evaluation of universal representations of speech. arXiv preprint arXiv:2205.12446 (2023)","DOI":"10.1109\/SLT54892.2023.10023141"},{"key":"22_CR41","doi-asserted-by":"publisher","unstructured":"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. In: Proceedings of the Odyssey 2022 The Speaker and Language Recognition Workshop, pp. 100\u2013107 (2022). https:\/\/doi.org\/10.21437\/Odyssey.2022-16","DOI":"10.21437\/Odyssey.2022-16"},{"issue":"6","key":"22_CR42","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1109\/JSTSP.2022.3188113","volume":"16","author":"S Chen","year":"2022","unstructured":"Chen, S., et al.: WavLM: large-scale self-supervised pre-training for full stack speech processing. IEEE J. Sel. Top. Signal Process. 16(6), 1505\u20131518 (2022). https:\/\/doi.org\/10.1109\/JSTSP.2022.3188113","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"22_CR43","doi-asserted-by":"crossref","unstructured":"Babu, A., et al.: XLS-R: self-supervised cross-lingual speech representation learning at scale. arXiv preprint arXiv:2111.09296 (2021)","DOI":"10.21437\/Interspeech.2022-143"},{"key":"22_CR44","unstructured":"Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation. https:\/\/github.com\/TakHemlata\/SSL_Anti-spoofing. Accessed 01 June 2025"},{"key":"22_CR45","doi-asserted-by":"publisher","unstructured":"Tak, H., Kamble, M., Patino, J., Todisco, M., Evans, N.: Rawboost: a raw data boosting and augmentation method applied to automatic speaker verification anti-spoofing. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, Singapore, pp. 6382\u20136386 (2022). https:\/\/doi.org\/10.1109\/ICASSP43922.2022.9746213","DOI":"10.1109\/ICASSP43922.2022.9746213"},{"key":"22_CR46","doi-asserted-by":"publisher","unstructured":"M\u00fcller, N.M., Dieckmann, F., Czempin, P., Canals, R., B\u00f6ttinger, K., Williams, J.: Speech is silver, silence is golden: what do ASVspoof-trained models really learn?. In: Proceedings of the ASVspoof 2021 Workshop, pp. 32\u201339 (2021). https:\/\/doi.org\/10.21437\/ASVSPOOF.2021-9","DOI":"10.21437\/ASVSPOOF.2021-9"},{"key":"22_CR47","unstructured":"Stan, A., et al.: TADA: training-free attribution and out-of-domain detection. arXiv preprint arXiv:2506.05802 (2025)"},{"key":"22_CR48","unstructured":"Akhtar, M.M., et al.: Source tracing of synthetic speech systems through paralinguistic pre-trained representations. arXiv preprint arXiv:2506.01157 (2025)"},{"key":"22_CR49","unstructured":"Negroni, V., et al.: Source verification for speech deepfakes. arXiv preprint arXiv:2505.14188 (2025)"},{"key":"22_CR50","unstructured":"Xiao, Y., Das, R.K.: Class incremental learning method for audio deepfake source tracing. arXiv preprint arXiv:2505.14601 (2025)"}],"container-title":["Lecture Notes in Computer Science","Speech and Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-07956-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T12:23:08Z","timestamp":1760358188000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-07956-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,13]]},"ISBN":["9783032079558","9783032079565"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-07956-5_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,13]]},"assertion":[{"value":"13 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"SPECOM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Speech and Computer","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Szeged","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hungary","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"specom2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/specom.inf.u-szeged.hu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}