{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T16:24:52Z","timestamp":1762273492501,"version":"3.44.0"},"reference-count":49,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Digital Threats"],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>Deepfake speech represents a real and growing threat to systems and society. Many detectors have been created to aid in defense against speech deepfakes. While these detectors implement myriad methodologies, many rely on low-level fragments of the speech generation process. We hypothesize that breath, a higher-level part of speech, is a key component of natural speech and thus improper generation in deepfake speech is a performant discriminator. To evaluate this, we create a breath detector and leverage this against a custom dataset of online news article audio to discriminate between real\/deepfake speech. Additionally, we make this custom dataset publicly available to facilitate comparison for future work. Applying our simple breath detector as a deepfake speech discriminator on in-the-wild samples allows for accurate classification (perfect 1.0 AUPRC and 0.0 EER on test data) across 33.6\u2009hours of audio. We compare our model with the state-of-the-art SSL-wav2vec and Codecfake models and show that these complex deep learning model completely either fail to classify the same in-the-wild samples (0.72 AUPRC and 0.89 EER), or substantially lack in the computational and temporal performance compared to our methodology (37\u2009seconds to predict a 1 minute sample with Codecfake vs. 0.3\u2009seconds with our model).<\/jats:p>","DOI":"10.1145\/3754456","type":"journal-article","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T16:03:19Z","timestamp":1753977799000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Every Breath You Don\u2019t Take: Deepfake Speech Detection Using\u00a0Breath"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7989-7350","authenticated-orcid":false,"given":"Seth","family":"Layton","sequence":"first","affiliation":[{"name":"University of Florida, Gainesville, Florida, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3453-0364","authenticated-orcid":false,"given":"Thiago","family":"De Andrade","sequence":"additional","affiliation":[{"name":"University of Florida, Gainesville, Florida, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7807-7941","authenticated-orcid":false,"given":"Daniel","family":"Olszewski","sequence":"additional","affiliation":[{"name":"University of Florida, Gainesville, Florida, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9806-5887","authenticated-orcid":false,"given":"Kevin","family":"Warren","sequence":"additional","affiliation":[{"name":"University of Florida, Gainesville, Florida, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5629-4755","authenticated-orcid":false,"given":"Carrie","family":"Gates","sequence":"additional","affiliation":[{"name":"Dalhousie University, Halifax, Nova Scotia, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7498-4239","authenticated-orcid":false,"given":"Kevin","family":"Butler","sequence":"additional","affiliation":[{"name":"University of Florida, Gainesville, Florida, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7143-5189","authenticated-orcid":false,"given":"Patrick","family":"Traynor","sequence":"additional","affiliation":[{"name":"University of Florida, Gainesville, Florida, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,9,12]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","unstructured":"Mikey Elmers Raphael Werner Beeke Muhlack Bernd Mobius and Jurgen Trouvain. 2021. 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