ASVspoof 5: Evaluation of Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911586237546496 |
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| author | Wang, Xin Delgado, Héctor Evans, Nicholas Liu, Xuechen Kinnunen, Tomi Tak, Hemlata Lee, Kong Aik Kukanov, Ivan Sahidullah, Md Todisco, Massimiliano Yamagishi, Junichi |
| author_facet | Wang, Xin Delgado, Héctor Evans, Nicholas Liu, Xuechen Kinnunen, Tomi Tak, Hemlata Lee, Kong Aik Kukanov, Ivan Sahidullah, Md Todisco, Massimiliano Yamagishi, Junichi |
| contents | ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake detection solutions. A significant change from previous challenge editions is a new crowdsourced database collected from a substantially greater number of speakers under diverse recording conditions, and a mix of cutting-edge and legacy generative speech technology. With the new database described elsewhere, we provide in this paper an overview of the ASVspoof 5 challenge results for the submissions of 53 participating teams. While many solutions perform well, performance degrades under adversarial attacks and the application of neural encoding/compression schemes. Together with a review of post-challenge results, we also report a study of calibration in addition to other principal challenges and outline a road-map for the future of ASVspoof. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03944 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ASVspoof 5: Evaluation of Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech Wang, Xin Delgado, Héctor Evans, Nicholas Liu, Xuechen Kinnunen, Tomi Tak, Hemlata Lee, Kong Aik Kukanov, Ivan Sahidullah, Md Todisco, Massimiliano Yamagishi, Junichi Signal Processing Sound ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake detection solutions. A significant change from previous challenge editions is a new crowdsourced database collected from a substantially greater number of speakers under diverse recording conditions, and a mix of cutting-edge and legacy generative speech technology. With the new database described elsewhere, we provide in this paper an overview of the ASVspoof 5 challenge results for the submissions of 53 participating teams. While many solutions perform well, performance degrades under adversarial attacks and the application of neural encoding/compression schemes. Together with a review of post-challenge results, we also report a study of calibration in addition to other principal challenges and outline a road-map for the future of ASVspoof. |
| title | ASVspoof 5: Evaluation of Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech |
| topic | Signal Processing Sound |
| url | https://arxiv.org/abs/2601.03944 |