Every Breath You Don't Take: Deepfake Speech Detection Using Breath
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arXiv
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| Format: | Preprint |
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2024
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| author | Layton, Seth De Andrade, Thiago Olszewski, Daniel Warren, Kevin Butler, Kevin Traynor, Patrick |
| author_facet | Layton, Seth De Andrade, Thiago Olszewski, Daniel Warren, Kevin Butler, Kevin Traynor, Patrick |
| contents | 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 hours of audio. We compare our model with the state-of-the-art SSL-wav2vec model and show that this complex deep learning model completely fails to classify the same in-the-wild samples (0.72 AUPRC and 0.99 EER). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_15143 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Every Breath You Don't Take: Deepfake Speech Detection Using Breath Layton, Seth De Andrade, Thiago Olszewski, Daniel Warren, Kevin Butler, Kevin Traynor, Patrick Sound Cryptography and Security Multimedia Audio and Speech Processing 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 hours of audio. We compare our model with the state-of-the-art SSL-wav2vec model and show that this complex deep learning model completely fails to classify the same in-the-wild samples (0.72 AUPRC and 0.99 EER). |
| title | Every Breath You Don't Take: Deepfake Speech Detection Using Breath |
| topic | Sound Cryptography and Security Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2404.15143 |