53支团队参与，评估语音伪造与对抗攻击检测效果
ASVspoof 5: Evaluation of Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech
- 基于众包数据集，测试53个团队的语音欺骗检测方案
- 模型在对抗攻击和神经编码压缩下性能显著下降
- 适合关注语音安全与深度伪造防御的研究者
ASVspoof 5 是该系列挑战的第五次，旨在推动语音欺骗与深度伪造检测技术的发展。与以往不同，本次挑战采用全新众包数据库，涵盖更多说话人及多样录音条件，并融合前沿与传统生成语音技术。本文对53支参赛团队的提交结果进行综述。尽管多数方案表现良好，但在对抗攻击及神经编码/压缩方案下性能明显下降。结合赛后分析，本文还探讨了校准问题及其他核心挑战，并为ASVspoof未来发展方向提出路线图。
原文摘要 · Abstract (English)
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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