ASVspoof 5: Evaluation of Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech

Xin Wang; Héctor Delgado; Nicholas Evans; Xuechen Liu; Tomi Kinnunen; Hemlata Tak; Kong Aik Lee; Ivan Kukanov; Md Sahidullah; Massimiliano Todisco; Junichi Yamagishi

arXiv · 2601.03944

ASVspoof 5: Evaluation of Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech

Xin Wang·Héctor Delgado·Nicholas Evans·Xuechen Liu·Tomi Kinnunen·Hemlata Tak·Kong Aik Lee·Ivan Kukanov·Md Sahidullah·Massimiliano Todisco·Junichi Yamagishi

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Abstract

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.

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Xin Wang, Héctor Delgado, Nicholas Evans, Xuechen Liu, Tomi Kinnunen, Hemlata Tak, Kong Aik Lee, Ivan Kukanov, Md Sahidullah, Massimiliano Todisco, Junichi Yamagishi. 2026-01-07. ASVspoof 5: Evaluation of Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech. https://arxiv.org/abs/2601.03944

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