ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale
<|reference_start|>ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale: ASVspoof 5 is the fifth edition in a series of challenges that promote the study of speech spoofing and deepfake attacks, and the design of detection solutions. Compared to previous challenges, the ASVspoof 5 database is built from crowdsourced data collected from a vastly greater number of speakers in diverse acoustic conditions. Attacks, also crowdsourced, are generated and tested using surrogate detection models, while adversarial attacks are incorporated for the first time. New metrics support the evaluation of spoofing-robust automatic speaker verification (SASV) as well as stand-alone detection solutions, i.e., countermeasures without ASV. We describe the two challenge tracks, the new database, the evaluation metrics, baselines, and the evaluation platform, and present a summary of the results. Attacks significantly compromise the baseline systems, while submissions bring substantial improvements.<|reference_end|>
@article{wang2024asvspoof,
    title={ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks
  at Scale},
    author={Xin Wang, Hector Delgado, Hemlata Tak, Jee-weon Jung, Hye-jin Shim,
  Massimiliano Todisco, Ivan Kukanov, Xuechen Liu, Md Sahidullah, Tomi
  Kinnunen, Nicholas Evans, Kong Aik Lee, Junichi Yamagishi},
    journal={arXiv preprint arXiv:2408.08739},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08739},
    primaryClass={eess.AS cs.AI cs.SD}
}